Intersect’s mission is to help researchers to be more efficient and effective in their research; reducing the time to move from an idea to a tested solution. Intersect provides an extensive range of technology-focused training to researchers and higher degree research (HDR) students across Australia including training courses at the awareness, introductory, and intermediate to advanced levels, covering the breadth of research-relevant digital tools and technologies. The training is delivered by Intersect’s team of experts.
Intersect Australia
https://intersect.org.au/
https://staging.dresa.org.au/content_providers/intersect-australia
Intersect’s mission is to help researchers to be more efficient and effective in their research; reducing the time to move from an idea to a tested solution. Intersect provides an extensive range of technology-focused training to researchers and higher degree research (HDR) students across Australia including training courses at the awareness, introductory, and intermediate to advanced levels, covering the breadth of research-relevant digital tools and technologies. The training is delivered by Intersect’s team of experts.
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Excel for Researchers
Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. We’ll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise,...
Keywords: Excel
Excel for Researchers
https://intersect.org.au/training/course/excel101
https://staging.dresa.org.au/materials/excel-for-researchers
Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. We’ll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data.
While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data.
‘Clean up’ messy research data
Organise, format and name your data
Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING)
Perform calculations on your data using functions (MAX, MIN, AVERAGE)
Extract significant findings from your data (PIVOT TABLE, VLOOKUP)
Manipulate your data (convert data format, work with DATES and TIMES)
Create graphs and charts to visualise your data (CHARTS)
Handy tips to speed up your work
In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
training@intersect.org.au
Intersect Australia
Excel
Introduction to Machine Learning using Python: Introduction & Linear Regression
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: Python
Introduction to Machine Learning using Python: Introduction & Linear Regression
https://intersect.org.au/training/course/python205
https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-python-introduction-linear-regression
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
Understand the difference between supervised and unsupervised Machine Learning.
Understand the fundamentals of Machine Learning.
Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.
Understand the Machine Learning modelling workflows.
Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
Either \Learn to Program: Python\ and \Data Manipulation in Python\ or \Learn to Program: Python\ and \Data Manipulation and Visualisation in Python\ needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
training@intersect.org.au
Intersect Australia
Python
Parallel Programming for HPC
You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you’ve submitted batch jobs.
Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance...
Keywords: HPC
Parallel Programming for HPC
https://intersect.org.au/training/course/hpc301
https://staging.dresa.org.au/materials/parallel-programming-for-hpc
You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you’ve submitted batch jobs.
Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance Computing.
OpenMP (Open Multi-Processing): a widespread method for shared memory programming
MPI (Message Passing Interface): a leading distributed memory programming model
To do this course you need to have:
A good working knowledge of HPC. Consider taking our
Getting Started with HPC using PBS Pro course to come up to speed beforehand.
Prior experience of writing programs in either C or Fortran.
training@intersect.org.au
Intersect Australia
HPC
Research Data Management Techniques
Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how?
This workshop is ideal for researchers who want to know how research data management can support...
Keywords: Data Management
Research Data Management Techniques
https://intersect.org.au/training/course/rdmt001
https://staging.dresa.org.au/materials/research-data-management-techniques
Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how?
This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data.
How to manage research data according to legal, statutory, ethical, funding body and university requirements
Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data
Services supporting research data at your institution
The course has no prerequisites.
training@intersect.org.au
Intersect Australia
Data Management
Getting started with HPC using PBS Pro
Is your computer’s limited power throttling your research ambitions? Are your analysis scripts pushing your laptop’s processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis...
Keywords: HPC
Getting started with HPC using PBS Pro
https://intersect.org.au/training/course/hpc201
https://staging.dresa.org.au/materials/getting-started-with-hpc-using-pbs-pro
Is your computer’s limited power throttling your research ambitions? Are your analysis scripts pushing your laptop’s processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?
High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using PBS Pro.
Connect to an HPC cluster
Use the Unix command line to operate a remote computer and create job scripts
Submit and manage jobs on a cluster using a scheduler
Transfer files to and from a remote computer
Use software through environment modules
Use parallelisation to speed up data analysis
Access the facilities available to you as a researcher
This is the PBS Pro version of the Getting Started with HPC course.
This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our \Unix Shell and Command Line Basics\ course.
training@intersect.org.au
Intersect Australia
HPC
From PC to Cloud or High Performance Computing
Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.
We may refer to both types as “large scale computing” – but...
Keywords: HPC
From PC to Cloud or High Performance Computing
https://intersect.org.au/training/course/compute001
https://staging.dresa.org.au/materials/from-pc-to-cloud-or-high-performance-computing
Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.
We may refer to both types as “large scale computing” – but what is the difference? Both systems target scalability of computing, but in different ways.
This webinar will give a good overview to the researchers thinking to make a move from their local computer to Cloud of High Performance Computing Cluster.
Introduction
HPC vs Cloud computing
When to use HPC
When to use the Cloud
The Cloud – Pros and Cons
HPC – Pros and Cons
The webinar has no prerequisites.
training@intersect.org.au
Intersect Australia
HPC
Exploring Chi-square and correlation in R
This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures...
Keywords: R
Exploring Chi-square and correlation in R
https://intersect.org.au/training/course/r210
https://staging.dresa.org.au/materials/exploring-chi-square-and-correlation-in-r
This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson’s r, Spearman’s Rho and Kendall’s tau) in real world datasets.
Obtain inferential statistics and assess data normality
Manipulate data and create graphs
Perform Chi-Square tests (Goodness of Fit test and Test of Independence)
Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau)
This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package).
Please consider attending Intersect’s following courses to get up to speed: \Learn to Program: R\, \Data Manipulation and Visualisation in R\
training@intersect.org.au
Intersect Australia
R
Mastering text with Regular Expressions
Have you ever wanted to extract phone numbers out of a block of unstructured text? Or email addresses. Or find all the words that start with “e” and end with “ed”, no matter their length? Or search through DNA sequences for a pattern? Or extract coordinates from GPS data?
Regular...
Keywords: Regular Expressions
Mastering text with Regular Expressions
https://intersect.org.au/training/course/regex101
https://staging.dresa.org.au/materials/mastering-text-with-regular-expressions
Have you ever wanted to extract phone numbers out of a block of unstructured text? Or email addresses. Or find all the words that start with “e” and end with “ed”, no matter their length? Or search through DNA sequences for a pattern? Or extract coordinates from GPS data?
Regular Expressions (regexes) are a powerful way to handle a multitude of different types of data. They can be used to find patterns in text and make sophisticated replacements. Think of them as find and replace on steroids. Come along to this workshop to learn what they can do and how to apply them to your research.
Comprehend and apply the syntax of regular expressions
Use the http://regexr.com tool to test a regular expression against some text
Construct simple regular expressions to find capitalised words; all numbers; all words that start with a specific set of letters, etc. in a block of text
Craft and test a progressively more complex regular expression
Find helpful resources covering regular expressions on the web
Comprehend and apply the syntax of regular expressions
Use the http://regexr.com tool to test a regular expression against some text
Construct simple regular expressions to find capitalised words; all numbers; all words that start with a specific set of letters, etc. in a block of text
Craft and test a progressively more complex regular expression
Find helpful resources covering regular expressions on the web
training@intersect.org.au
Intersect Australia
Regular Expressions
R for Social Scientists
R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.
But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this...
Keywords: R
R for Social Scientists
https://intersect.org.au/training/course/r103
https://staging.dresa.org.au/materials/r-for-social-scientists
R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.
But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Data Carpentry.
Basic syntax and data types in R
RStudio interface
How to import CSV files into R
The structure of data frames
A brief introduction to data wrangling and data transformation
How to calculate summary statistics
A brief introduction to visualise data
No prior experience with programming needed to attend this course.
training@intersect.org.au
The Carpentries
R
Exploring Chi-Square and correlation in SPSS
This hands-on training is designed to familiarize you further with the SPSS data analysis environment. In this session, we will traverse into the realm of inferential statistics, beginning with linear correlation and reliability. We will present a brief conceptual overview and the SPSS procedures...
Keywords: Data Analysis, SPSS
Exploring Chi-Square and correlation in SPSS
https://intersect.org.au/training/course/spss102
https://staging.dresa.org.au/materials/exploring-chi-square-and-correlation-in-spss
This hands-on training is designed to familiarize you further with the SPSS data analysis environment. In this session, we will traverse into the realm of inferential statistics, beginning with linear correlation and reliability. We will present a brief conceptual overview and the SPSS procedures for computing Pearson's r and Spearman's Rho, followed by a short session on reliability . In the remainder of the session, we will explore the Chi-Square Goodness-of-Fit test and Chi-Square Test of Association for analysing categorical data.
#### You'll learn:
- Perform Pearson’s Correlation (r) Test
- Perform Spearman’s Rho Correlation (⍴) Test
- Carry out basic reliability analysis on survey items
- Perform Chi-Square Goodness-of-Fit test
- Perform Chi-Square Test of Association
#### Prerequisites:
This workshop is recommended for researchers and postgraduate students who have previously attended the Intersect’s Data Entry and Processing in SPSS training course.
**For more information, please click [here](https://intersect.org.au/training/course/spss102).**
training@intersect.org.au
Data Analysis, SPSS
Data Manipulation and Visualisation in R
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will learn how to manipulate, explore and get insights...
Keywords: R
Data Manipulation and Visualisation in R
https://intersect.org.au/training/course/r203
https://staging.dresa.org.au/materials/data-manipulation-and-visualisation-in-r
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation).
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation.
DataFrame Manipulation using the dplyr package
DataFrame Transformation using the tidyr package
Using the Grammar of Graphics to convert data into figures using the ggplot2 package
Configuring plot elements within ggplot2
Exploring different types of plots using ggplot2
Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
Intersect Australia
R
Exploring Chi-Square and Correlation in SPSS
This hands-on training is designed to familiarize you further with the SPSS data analysis environment. In this session, we will traverse into the realm of inferential statistics, beginning with linear correlation and reliability. We will present a brief conceptual overview and the SPSS procedures...
Keywords: Data Analysis, SPSS
Exploring Chi-Square and Correlation in SPSS
https://intersect.org.au/training/course/spss102
https://staging.dresa.org.au/materials/exploring-chi-square-and-correlation-in-spss-84543235-5cfe-4268-8546-b16727125073
This hands-on training is designed to familiarize you further with the SPSS data analysis environment. In this session, we will traverse into the realm of inferential statistics, beginning with linear correlation and reliability. We will present a brief conceptual overview and the SPSS procedures for computing Pearson's r and Spearman's Rho, followed by a short session on reliability . In the remainder of the session, we will explore the Chi-Square Goodness-of-Fit test and Chi-Square Test of Association for analysing categorical data.
#### You'll learn:
- Perform Pearson’s Correlation (r) Test
- Perform Spearman’s Rho Correlation (⍴) Test
- Carry out basic reliability analysis on survey items
- Perform Chi-Square Goodness-of-Fit test
- Perform Chi-Square Test of Association
#### Prerequisites:
In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This workshop is recommended for researchers and postgraduate students who have previously attended the Intersect’s [Data Entry and Processing in SPSS](https://intersect.org.au/training/course/spss101/) workshop.
**For more information, please click [here](https://intersect.org.au/training/course/spss102).**
training@intersect.org.au
Data Analysis, SPSS
Getting Started with Tableau for Data Analysis and Visualisation
Tableau is a powerful data visualisation software that can help anyone see and understand their data. With the features to connect to almost any database, drag and drop to create visualizations, and share with a click, it definately makes thing easier.
This course is suitable for all researchers...
Keywords: Data Analysis, Tableau
Getting Started with Tableau for Data Analysis and Visualisation
https://intersect.org.au/training/course/tableau101
https://staging.dresa.org.au/materials/getting-started-with-tableau-for-data-analysis-and-visualisation
Tableau is a powerful data visualisation software that can help anyone see and understand their data. With the features to connect to almost any database, drag and drop to create visualizations, and share with a click, it definately makes thing easier.
This course is suitable for all researchers and research students from any discipline. It provides step by step guides on how to visualise your research data on an interactive dashboard.
#### You'll learn:
- Import and combine data
- Filter data
- Create cross tabulation table
- Create interactive plots including graph map
- Create and design an interactive dashboard
#### Prerequisites:
The course has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/tableau101).**
training@intersect.org.au
Data Analysis, Tableau
Getting started with NVivo for Windows
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to...
Keywords: NVivo
Getting started with NVivo for Windows
https://intersect.org.au/training/course/nvivo101
https://staging.dresa.org.au/materials/getting-started-with-nvivo-for-windows
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.
Create and organise a qualitative research project in NVivo
Import a range of data sources using NVivo’s integrated tools
Code and classify your data
Format your data to take advantage of NVivo’s auto-coding ability
Use NVivo to discover new themes and trends in research
Visualise relationships and trends in your data
In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users.
training@intersect.org.au
Intersect Australia
NVivo
Collecting Web Data
Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.
Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format,...
Keywords: Python
Collecting Web Data
https://intersect.org.au/training/course/webdata201
https://staging.dresa.org.au/materials/collecting-web-data
Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.
Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it’s a skill worth learning.
So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
The concept of structured data
The use of XPath queries on HTML document
How to scrape data using browser extensions
How to scrape using Python and Scrapy
How to automate the scraping of multiple web pages
A good knowledge of the basic concepts and techniques in Python. Consider taking our \Learn to Program: Python\ and \Python for Research\ courses to come up to speed beforehand.
training@intersect.org.au
Intersect Australia
Python
Learn to Program: Julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...
Keywords: Julia
Learn to Program: Julia
https://intersect.org.au/training/course/julia101
https://staging.dresa.org.au/materials/learn-to-program-julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages!
This workshop expects that you are coming to Julia with some experience in the basic concepts of programming in another language. It is designed to help you migrate the basic concepts of programming that you already know to the Julia context.
Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly.
Introduction to the JupyterLab interface for programming
Basic syntax and data types in Julia
How to load external data into Julia
Creating functions (FUNCTIONS)
Repeating actions and analysing multiple data sets (LOOPS)
Making choices (IF STATEMENTS – CONDITIONALS)
Ways to visualise data using the Plots library in Julia
Some experience with the basic concepts of programming in another language needed to attend this course. It is an intensive course that is designed to help you migrate the basic concepts of programming that you already know to the Julia context in half a day instead of a full day. If you don’t have any prior experience in programming, please consider attending one of the \Learn to Program: Python\, \Learn to Program: R\ or \Learn to Program: MATLAB\ prior to this course.
We also strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\.
training@intersect.org.au
Intersect Australia
Julia
Regular Expressions on the Command Line
Would you like to use regular expressions with the classic command line utilities find, grep, sed and awk? These venerable Unix utilities allow you to search, filter and transform large amounts of text (including many common data formats) efficiently and repeatably.
find to locate files and...
Keywords: Regular Expressions
Regular Expressions on the Command Line
https://intersect.org.au/training/course/regex201
https://staging.dresa.org.au/materials/regular-expressions-on-the-command-line
Would you like to use regular expressions with the classic command line utilities find, grep, sed and awk? These venerable Unix utilities allow you to search, filter and transform large amounts of text (including many common data formats) efficiently and repeatably.
find to locate files and directories matching regexes.
grep to filter lines in files based on pattern matches.
sed to find and replace using regular expressions and captures.
awk to work with row- and column-oriented data.
This course assumes prior knowledge of the basic syntax of regular expressions. If you’re new to regular expressions or would like a refresher, take our Mastering text with Regular Expressions course first.
This course also assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. Take our Unix Shell and Command Line Basics course to get up to speed quickly.
training@intersect.org.au
Intersect Australia
Regular Expressions
Learn to Program: Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results,...
Keywords: Programming, Python
Learn to Program: Python
https://intersect.org.au/training/course/python101
https://staging.dresa.org.au/materials/learn-to-program-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the JupyterLab interface for programming
- Basic syntax and data types in Python
- How to load external data into Python
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data in Python
#### Prerequisites:
No prior experience with programming is needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/python101).**
training@intersect.org.au
Programming, Python
Randomised Controlled Trials with REDCap
REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. In this course, learn how to manage a Randomised Controlled Trial using REDCap, including the randomisation module, adverse event reporting and automated participant withdrawals. This...
Keywords: REDCap
Randomised Controlled Trials with REDCap
https://intersect.org.au/training/course/redcap202
https://staging.dresa.org.au/materials/randomised-controlled-trials-with-redcap
REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. In this course, learn how to manage a Randomised Controlled Trial using REDCap, including the randomisation module, adverse event reporting and automated participant withdrawals. This course will introduce some of REDCap’s more advanced features for running randomised trials, and builds on the material taught in REDCAP201 – Longitudinal Trials with REDCap.
- Create Data Access Groups (DAGs) and assign users to manage trial sites
- Build randomisation allocation table
- Enable and implement participant randomisation module
- Design an adverse reporting system using Automated Survey Invitations and Alerts
- Create an automated participant withdrawal process
- Customise record dashboards
Learners should have a solid understanding of REDCap and be familiar with the content of [Data Capture and Surveys with REDCap](https://intersectaustralia.github.io/training/REDCAP101/) and [Longitudinal Trials with REDCap](https://intersectaustralia.github.io/training/REDCAP201/).
training@intersect.org.au
Intersect Australia
REDCap
Data Entry and Processing in SPSS
This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in...
Keywords: SPSS
Data Entry and Processing in SPSS
https://intersect.org.au/training/course/spss101
https://staging.dresa.org.au/materials/data-entry-and-processing-in-spss
This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in SPSS and perform visualization.
This workshop is recommended for researchers and postgraduate students who are new to SPSS or Statistics; or those simply looking for a refresher course before taking a deep dive into using SPSS, either to apply it to their research or to add it to their arsenal of eResearch skills.
Navigate the SPSS working environment
Prepare data files and define variables
Enter data in SPSS and Import data from Excel
Perform data screening
Compose SPSS Syntax for data processing
Obtain descriptive statistics, create graphs & assess normality
Manipulate and transform variables
In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
training@intersect.org.au
Intersect Australia
SPSS
Traversing t tests in R
R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
The primary goal of this workshop is to familiarise you with basic statistical concepts in R...
Keywords: R
Traversing t tests in R
https://intersect.org.au/training/course/r211
https://staging.dresa.org.au/materials/traversing-t-tests-in-r
R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R.
Read in and manipulate data
Check assumptions of t tests
Perform one-sample t tests
Perform two-sample t tests (Independent-samples, Paired-samples)
Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test)
This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect’s following courses to get up to speed: \Learn to Program: R\, \Data Manipulation and Visualisation in R\
training@intersect.org.au
Intersect Australia
R
Survey Tools in Research: REDCap and Qualtrics
Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your...
Keywords: REDCap, Qualtrics
Survey Tools in Research: REDCap and Qualtrics
https://intersect.org.au/training/course/surveys001
https://staging.dresa.org.au/materials/survey-tools-in-research-redcap-and-qualtrics
Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your research up for success through intuitive data collection and validation, scheduling and reporting.
This webinar will introduce and compare two of the most popular research tools for the collection of survey data and patient records: REDCap and Qualtrics.
Electronic Data Capture: Surveys vs Forms
Confidential vs Anonymous data collection
Strengths and weaknesses of Qualtrics and REDCap
Real-life use cases for each tool
Using survey tools for longitudinal studies
The webinar has no prerequisites.
training@intersect.org.au
Intersect Australia
REDCap, Qualtrics
Data Manipulation in R
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will learn how to manipulate, explore and get insights...
Keywords: R
Data Manipulation in R
https://intersect.org.au/training/course/r201
https://staging.dresa.org.au/materials/data-manipulation-in-r
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package).
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation.
DataFrame Manipulation using the dplyr package
DataFrame Transformation using the tidyr package
Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
The Carpentries
R
A showcase of Data Analysis in Python and R: A case study using COVID-19 data
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and...
Keywords: Python, R
A showcase of Data Analysis in Python and R: A case study using COVID-19 data
https://intersect.org.au/training/course/coding002
https://staging.dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python.
Cleaning up a dataset for analysis
Using Jupyter lab for interactive analysis
Making the most of the tidyverse (R) and pandas (python)
Simple data visualisation using ggplot (R) and seaborn (python)
Best practices for readable code
The webinar has no prerequisites.
training@intersect.org.au
Intersect Australia
Python, R
Introduction to Machine Learning using R: SVM & Unsupervised Learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: R
Introduction to Machine Learning using R: SVM & Unsupervised Learning
https://intersect.org.au/training/course/r207
https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages.
Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
Know the differences between various core Machine Learning models.
Understand the Machine Learning modelling workflows.
Use R and its relevant packages to process real datasets, train and apply Machine Learning models.
\\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\
training@intersect.org.au
Intersect Australia
R
Data Manipulation and Visualisation in Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using...
Keywords: Python
Data Manipulation and Visualisation in Python
https://intersect.org.au/training/course/python203
https://staging.dresa.org.au/materials/data-manipulation-and-visualisation-in-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation).
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Working with pandas DataFrames
Indexing, slicing and subsetting in pandas DataFrames
Missing data values
Combine multiple pandas DataFrames
Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
Configuring plot elements within seaborn and matplotlib
Exploring different types of plots using seaborn
Either \Learn to Program: Python\ or \Learn to Program: Python\ and \Python for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: Python\ and \Python for Research\ courses to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
Intersect Australia
Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning
https://intersect.org.au/training/course/python207
https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-python-svm-unsupervised-learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
Know the differences between various core Machine Learning models.
Understand the Machine Learning modelling workflows.
Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.
Either \Learn to Program: Python\, \Data Manipulation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ or \Learn to Program: Python\, \Data Manipulation and Visualisation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
training@intersect.org.au
Intersect Australia
Python
Getting started with HPC using Slurm
Is your computer’s limited power throttling your research ambitions? Are your analysis scripts pushing your laptop’s processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis...
Keywords: HPC
Getting started with HPC using Slurm
https://intersect.org.au/training/course/hpc202
https://staging.dresa.org.au/materials/getting-started-with-hpc-using-slurm
Is your computer’s limited power throttling your research ambitions? Are your analysis scripts pushing your laptop’s processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?
High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using Slurm.
Connect to an HPC cluster
Use the Unix command line to operate a remote computer and create job scripts
Submit and manage jobs on a cluster using a scheduler
Transfer files to and from a remote computer
Use software through environment modules
Use parallelisation to speed up data analysis
Access the facilities available to you as a researcher
This is the Slurm version of the Getting Started with HPC course.
This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our \Unix Shell and Command Line Basics\ course.
training@intersect.org.au
Intersect Australia
HPC
Start Coding without Hesitation: Programming Languages Showdown
Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are...
Keywords: Python, R, Matlab, Julia
Start Coding without Hesitation: Programming Languages Showdown
https://intersect.org.au/training/course/coding001
https://staging.dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown
Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are exploring four of the most popular programming languages that are widely used in academia, namely Python, R, MATLAB, and Julia.
Why use Programming
An overview of Python, R, MATLAB, and Julia
Code comparison of the four programming languages
Popularity and job opportunities
Intersect’s comparison
General guidelines on how to choose the best programming language for your research
The webinar has no prerequisites.
training@intersect.org.au
Intersect Australia
Python, R, Matlab, Julia
Introduction to Machine Learning using Python: Classification
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: Python
Introduction to Machine Learning using Python: Classification
https://intersect.org.au/training/course/python206
https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-python-classification
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
Know the differences between various core Machine Learning models.
Understand the Machine Learning modelling workflows.
Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.
Either \Learn to Program: Python\, \Data Manipulation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ or \Learn to Program: Python\, \Data Manipulation and Visualisation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
training@intersect.org.au
Intersect Australia
Python
Exploring ANOVAs in R
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.This half-day course covers one and two-way Analyses of Variance (ANOVA) and their...
Keywords: R
Exploring ANOVAs in R
https://intersect.org.au/training/course/r212
https://staging.dresa.org.au/materials/exploring-anovas-in-r
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R.
ANOVA (Analysis of Variance) is a statistical method used to determine whether there are significant differences between the means of three or more groups. It helps analyse the effect of independent variables on a dependent variable by comparing the variance within groups to the variance between groups. ANOVA tests assume normality, homogeneity of variances, and independence of observations, and can be used to explore relationships in datasets, such as how factors like study time or parental education affect student performance.
- Basic statistical theory behind ANOVAs
- How to check that the data meets the assumptions
- One-way ANOVA in R and post-hoc analysis
- Two-way ANOVA plus interaction effects and post-hoc analysis
- Non-parametric alternatives to one and two-way ANOVA
This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required.
If you’re new to programming in R, we strongly recommend you register for the \Learn to Program: R\, \Data Manipulation and Visualisation in R\ workshops first.
training@intersect.org.au
Intersect Australia
R
Cleaning Data with Open Refine
Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet?
Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring,...
Keywords: Open Refine
Cleaning Data with Open Refine
https://intersect.org.au/training/course/refine101
https://staging.dresa.org.au/materials/cleaning-data-with-open-refine
Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet?
Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring, normalising and cleaning datasets, and extending data by accessing the internet through APIs. In this course we’ll work through the various features of Refine, including importing data, faceting, clustering, and calling remote APIs, by working on a fictional but plausible humanities research project.
Download, install and run Open Refine
Import data from csv, text or online sources and create projects
Navigate data using the Open Refine interface
Explore data by using facets
Clean data using clustering
Parse data using GREL syntax
Extend data using Application Programming Interfaces (APIs)
Export project for use in other applications
The course has no prerequisites.
training@intersect.org.au
Intersect Australia
Open Refine
Learn to Program: MATLAB
MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you’re just getting started – with MATLAB and, more generally, with programming?
Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors! ...
Keywords: Matlab
Learn to Program: MATLAB
https://intersect.org.au/training/course/matlab101
https://staging.dresa.org.au/materials/learn-to-program-matlab
MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you’re just getting started – with MATLAB and, more generally, with programming?
Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors!
So join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Introduction to the MATLAB interface for programming
Basic syntax and data types in MATLAB
How to load external data into MATLAB
Creating functions (FUNCTIONS)
Repeating actions and analysing multiple data sets (LOOPS)
Making choices (IF STATEMENTS – CONDITIONALS)
Ways to visualise data in MATLAB
In order to participate, attendees must have a licensed copy of MATLAB installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\.
training@intersect.org.au
The Carpentries
Matlab
R for Research
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
This workshop is an introduction to data...
Keywords: R
R for Research
https://intersect.org.au/training/course/r110
https://staging.dresa.org.au/materials/r-for-research
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
This workshop is an introduction to data structures (DataFrames) and visualisation (using the ggplot2 package) in R. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals.
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Project Management with RStudio
Introduction to Data Structures in R
Introduction to DataFrames in R
Selecting values in DataFrames
Quick introduction to Plotting using the ggplot2 package
\Learn to Program: R\ or any of the \Learn to Program: Python\, \Learn to Program: MATLAB\, \Learn to Program: Julia\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \Learn to Program: R\ course to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
The Carpentries
R
Data Visualisation in Python
Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
Configuring plot elements within seaborn and matplotlib
Exploring different types of...
Keywords: Python
Data Visualisation in Python
https://intersect.org.au/training/course/python202
https://staging.dresa.org.au/materials/data-visualisation-in-python
[Course Materials](https://intersectaustralia.github.io/training/PYTHON203/sources/Data-Adv_Python.zip)
Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
Configuring plot elements within seaborn and matplotlib
Exploring different types of plots using seaborn
Either \Learn to Program: Python\ or \Learn to Program: Python\ and \Python for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: Python\ and \Python for Research\ courses to ensure that you are familiar with the knowledge needed for this course.
We also strongly recommend attending the \Data Manipulation in Python\.
training@intersect.org.au
Intersect Australia
Python
Python for Research
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.
This workshop is an introduction to data structures (DataFrames...
Keywords: Python
Python for Research
https://intersect.org.au/training/course/python110
https://staging.dresa.org.au/materials/python-for-research
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.
This workshop is an introduction to data structures (DataFrames using the pandas library) and visualisation (using the matplotlib library) in Python. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Introduction to Libraries and Built-in Functions in Python
Introduction to DataFrames using the pandas library
Reading and writing data in DataFrames
Selecting values in DataFrames
Quick introduction to Plotting using the matplotlib library
\Learn to Program: Python\ or any of the \Learn to Program: R\, \Learn to Program: MATLAB\ or \Learn to Program: Julia\, needed to attend this course. If you already have some experience with programming, please check the topics covered in the \Learn to Program: Python\ course to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
The Carpentries
Python
Version Control with Git
Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there...
Keywords: Git
Version Control with Git
https://intersect.org.au/training/course/git101
https://staging.dresa.org.au/materials/version-control-with-git
Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available.
Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
keep versions of data, scripts, and other files
examine commit logs to find which files were changed when
restore earlier versions of files
compare changes between versions of a file
push your versioned files to a remote location, for backup and to facilitate collaboration
The course has no prerequisites.
training@intersect.org.au
The Carpentries
Git
Thinking like a computer: The Fundamentals of Programming
Human brains are extremely good at evaluating a small amount of information simultaneously, ignoring anomalies and coming up with an answer to a problem without much in the way of conscious thought. Computers on the other hand are extremely good at performing individual calculations, one at a...
Keywords: Python
Thinking like a computer: The Fundamentals of Programming
https://intersect.org.au/training/course/coding003
https://staging.dresa.org.au/materials/thinking-like-a-computer-the-fundamentals-of-programming
Human brains are extremely good at evaluating a small amount of information simultaneously, ignoring anomalies and coming up with an answer to a problem without much in the way of conscious thought. Computers on the other hand are extremely good at performing individual calculations, one at a time, and can keep the results in a large bank of short-term memory for quick recall. These two approaches are fundamentally different.
Humans can only reasonably retain seven plus or minus two pieces of information in short-term memory, and new items push older items out, whereas a computer is hopeless when given multiple pieces of information simultaneously.
Understanding this fact is key to being able to write instructions for computers – also known as programs – in a way that takes advantage of their strengths, and overcomes their drawbacks.
Suitable for the programming novice, this webinar is good preparation for researchers wanting to learn how to program.
How a human solves tasks
How a computer solves tasks
Overview of programming concepts:
Variables
Loops
Conditionals
Functions
Data types
The webinar has no prerequisites.
training@intersect.org.au
Intersect Australia
Python
Data Entry, Exploration, & Analysis in SPSS
This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in...
Keywords: SPSS
Data Entry, Exploration, & Analysis in SPSS
https://intersect.org.au/training/course/spss101
https://staging.dresa.org.au/materials/data-entry-exploration-analysis-in-spss
This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in SPSS and perform visualization.
This workshop is recommended for researchers and postgraduate students who are new to SPSS or Statistics; or those simply looking for a refresher course before taking a deep dive into using SPSS, either to apply it to their research or to add it to their arsenal of eResearch skills.
- Navigate SPSS Variable and Data views.
- Create and describe data from scratch.
- Import Data from Excel.
- Familiarise yourself with exploratory data analysis (EDA), including:
- Understand variable types, identity missing data and outliers.
- Visualise data in graphs and tables.
- Compose SPSS Syntax to repeat and store analysis steps.
- Generate a report testing assumptions of statistical tests.
- Additional exercises:
- Check assumptions for common statistical tests.
- Make stunning plots.
In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
training@intersect.org.au
Intersect Australia
SPSS
Getting Started with Excel
We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors.
This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool,...
Keywords: Excel
Getting Started with Excel
https://intersect.org.au/training/course/excel001
https://staging.dresa.org.au/materials/getting-started-with-excel
We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors.
This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data.
Introduction to Microsoft Excel user interface
Interpret data using sorting, filtering, and conditional formatting
Summarise data using functions
Analyse data using pivot tables
Manipulate and visualise data
Handy tips to speed up your work
The webinar has no prerequisites.
training@intersect.org.au
Intersect Australia
Excel
Unix Shell and Command Line Basics
The Unix environment is incredibly powerful but quite daunting to the newcomer. Command line confidence unlocks powerful computing resources beyond the desktop, including virtual machines and High Performance Computing. It enables repetitive tasks to be automated. And it comes with a swag of...
Keywords: Unix
Unix Shell and Command Line Basics
https://intersect.org.au/training/course/unix101
https://staging.dresa.org.au/materials/unix-shell-and-command-line-basics
The Unix environment is incredibly powerful but quite daunting to the newcomer. Command line confidence unlocks powerful computing resources beyond the desktop, including virtual machines and High Performance Computing. It enables repetitive tasks to be automated. And it comes with a swag of handy tools that can be combined in powerful ways. Getting started is the hardest part, but our helpful instructors are there to demystify Unix as you get to work running programs and writing scripts on the command line.
Every attendee is given a dedicated training environment for the duration of the workshop, with all software and data fully loaded and ready to run.
We teach this course within a GNU/Linux environment. This is best characterised as a Unix-like environment. We teach how to run commands within the Bash Shell. The skills you’ll learn at this course are generally transferable to other Unix environments.
Navigate and work with files and directories (folders)
Use a selection of essential tools
Combine data and tools to build a processing workflow
Automate repetitive analysis using the command line
The course has no prerequisites.
training@intersect.org.au
The Carpentries
Unix
Introduction to Machine Learning using R: Classification
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: R
Introduction to Machine Learning using R: Classification
https://intersect.org.au/training/course/r206
https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-r-classification
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages.
Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
Know the differences between various core Machine Learning models.
Understand the Machine Learning modelling workflows.
Use R and its relevant packages to process real datasets, train and apply Machine Learning models.
\\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\
training@intersect.org.au
Intersect Australia
R
Getting Started with NVivo for Mac
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to...
Keywords: NVivo
Getting Started with NVivo for Mac
https://intersect.org.au/training/course/nvivo102
https://staging.dresa.org.au/materials/getting-started-with-nvivo-for-mac
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.
Create and organise a qualitative research project in NVivo
Import a range of data sources using NVivo’s integrated tools
Code and classify your data
Format your data to take advantage of NVivo’s auto-coding ability
Use NVivo to discover new themes and trends in research
Visualise relationships and trends in your data
In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users.
training@intersect.org.au
Intersect Australia
NVivo
Data Visualisation in R
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will explore different types of graphs and learn how to...
Keywords: R
Data Visualisation in R
https://intersect.org.au/training/course/r202
https://staging.dresa.org.au/materials/data-visualisation-in-r
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation).
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation.
Using the Grammar of Graphics to convert data into figures using the ggplot2 package
Configuring plot elements within ggplot2
Exploring different types of plots using ggplot2
Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course.
We also strongly recommend attending the \Data Manipulation in R\ course.
training@intersect.org.au
Intersect Australia
R
Beyond the Basics: Julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...
Keywords: Julia
Beyond the Basics: Julia
https://intersect.org.au/training/course/julia201
https://staging.dresa.org.au/materials/beyond-the-basics-julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages!
This workshop explores the more advanced features of functions in Julia, introduces widely used tools within Julia, as well as demonstrates the speed of Julia by benchmarking functions and different styles of scripting within Julia.
Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly.
Understand the role of Types within Julia
Create functions with complex arguments
Demonstrate programming patterns of list comprehension, pipes, and anonymous functions.
Benchmark Julia code and understand how to make it fast
If you already have experience with programming, please check the topics covered in the \Learn to Program: Julia\ to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
Intersect Australia
Julia
Beyond Basics: Conditionals and Visualisation in Excel
After cleaning your dataset, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested...
Keywords: Excel
Beyond Basics: Conditionals and Visualisation in Excel
https://intersect.org.au/training/course/excel201
https://staging.dresa.org.au/materials/beyond-basics-conditionals-and-visualisation-in-excel
After cleaning your dataset, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel’s diverse functionality and apply it to your research project.
- Cell syntax and conditional formatting
- IF functions
- Pivot Table summaries
- Nesting multiple AND/IF/OR calculations
- Combining nested calculations with conditional formatting to bring out important elements of the dataset
- MINIFS function
- Box plot creation and outlier identification
- Trendline and error bar chart enhancements
Familiarity with the content of Excel for Researchers, specifically:
- the general Office/Excel interface (menus, ribbons/toolbars, etc.)
- workbooks and worksheets
- absolute and relative references, e.g. $A$1, A1.
- simple ranges, e.g. A1:B5
training@intersect.org.au
Intersect Australia
Excel
Data Capture and Surveys with REDCap
Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you.
This course will introduce you to REDCap, a rapidly evolving web tool developed by...
Keywords: REDCap
Data Capture and Surveys with REDCap
https://intersect.org.au/training/course/redcap101
https://staging.dresa.org.au/materials/data-capture-and-surveys-with-redcap
Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you.
This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs.
Get started with REDCap
Create and set up projects
Design forms and surveys using the online designer
Learn how to use branching logic, piping, and calculations
Enter data via forms and distribute surveys
Create, view and export data reports
Add collaborators and set their privileges
The course has no prerequisites.
training@intersect.org.au
Intersect Australia
REDCap
Longitudinal Trials with REDCap
REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. With powerful features such as organising data collection instruments into predefined events, you can shepherd your participants through a complex survey at various time points with...
Keywords: REDCap
Longitudinal Trials with REDCap
https://intersect.org.au/training/course/redcap201
https://staging.dresa.org.au/materials/longitudinal-trials-with-redcap
REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. With powerful features such as organising data collection instruments into predefined events, you can shepherd your participants through a complex survey at various time points with very little configuration.
This course will introduce some of REDCap’s more advanced features for running longitudinal studies, and builds on the foundational material taught in REDCAP101 – Managing Data Capture and Surveys with REDCap.
Build a longitudinal project
Manage participants throughout multiple events
Configure and use Automated Survey Invitations
Use Smart Variables to add powerful features to your logic
Take advantage of high-granularity permissions for your collaborators
Understand the data structure of a longitudinal project
This course requires the participant to have a fairly good basic knowledge of REDCap. To come up to speed, consider taking our \Data Capture and Surveys with REDCap\ workshop.
training@intersect.org.au
Intersect Australia
REDCap
Databases and SQL
A relational database is an extremely efficient, fast and widespread means of storing structured data, and Structured Query Language (SQL) is the standard means for reading from and writing to databases. Databases use multiple tables, linked by well-defined relationships, to store large amounts...
Keywords: SQL
Databases and SQL
https://intersect.org.au/training/course/sql101
https://staging.dresa.org.au/materials/databases-and-sql
A relational database is an extremely efficient, fast and widespread means of storing structured data, and Structured Query Language (SQL) is the standard means for reading from and writing to databases. Databases use multiple tables, linked by well-defined relationships, to store large amounts of data without needless repetition while maintaining the integrity of your data.
Moving from spreadsheets and text documents to a structured relational database can be a steep learning curve, but one that will reward you many times over in speed, efficiency and power.
Developed using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Understand and compose a query using SQL
Use the SQL syntax to select, sort and filter data
Calculate new values from existing data
Aggregate data into sums, averages, and other operations
Combine data from multiple tables
Design and build your own relational databases
The course has no prerequisites.
training@intersect.org.au
The Carpentries
SQL
Introduction to Machine Learning using R: Introduction & Linear Regression
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: R
Introduction to Machine Learning using R: Introduction & Linear Regression
https://intersect.org.au/training/course/r205
https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-r-introduction-linear-regression
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages.
Understand the difference between supervised and unsupervised Machine Learning.
Understand the fundamentals of Machine Learning.
Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.
Understand the Machine Learning modelling workflows.
Use R and and its relevant packages to process real datasets, train and apply Machine Learning models
\\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\
training@intersect.org.au
Intersect Australia
R
Data Manipulation in Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using...
Keywords: Python
Data Manipulation in Python
https://intersect.org.au/training/course/python201
https://staging.dresa.org.au/materials/data-manipulation-in-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Working with pandas DataFrames
Indexing, slicing and subsetting in pandas DataFrames
Missing data values
Combine multiple pandas DataFrames
Either \Learn to Program: Python\ or \Learn to Program: Python\ and \Python for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: Python\ and \Python for Research\ courses to ensure that you are familiar with the knowledge needed for this course.
training@intersect.org.au
The Carpentries
Python
Surveying with Qualtrics
Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?
Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research...
Keywords: Qualtrics
Surveying with Qualtrics
https://intersect.org.au/training/course/qltrics101
https://staging.dresa.org.au/materials/surveying-with-qualtrics
Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?
Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow from building a survey to analysing the results using Qualtrics. We will discover the numerous design elements available in order to get the most useful results and make life as easy as can be for your respondents.
If your institution has a licence to Qualtrics, then this course is right for you.
Format a sample survey using the Qualtrics online platform
Configure the survey using a range of design features to improve user experience
Decide which distribution channel is right for your needs
Understand the available data analysis and export options in Qualtrics
You must have access to a Qualtrics instance, such as through your university license. Speak to your local university IT or Research Office for assistance in accessing the Qualtrics instance.
training@intersect.org.au
Intersect Australia
Qualtrics
Learn to Program: R
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
But getting started with R can be...
Keywords: R
Learn to Program: R
https://intersect.org.au/training/course/r101
https://staging.dresa.org.au/materials/learn-to-program-r
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in.
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
Introduction to the RStudio interface for programming
Basic syntax and data types in R
How to load external data into R
Creating functions (FUNCTIONS)
Repeating actions and analysing multiple data sets (LOOPS)
Making choices (IF STATEMENTS – CONDITIONALS)
Ways to visualise data in R
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\.
training@intersect.org.au
The Carpentries
R
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Data Entry and Processing in SPSS at UC Online
11 - 12 June 2025
Data Entry and Processing in SPSS at UC Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/data-entry-and-processing-in-spss-at-uc-online-bbaafc46-0282-4408-a11f-47747497656f This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in SPSS and perform visualization. This workshop is recommended for researchers and postgraduate students who are new to SPSS or Statistics; or those simply looking for a refresher course before taking a deep dive into using SPSS, either to apply it to their research or to add it to their arsenal of eResearch skills. #### You'll learn: Navigate the SPSS working environment Prepare data files and define variables Enter data in SPSS and Import data from Excel Perform data screening Compose SPSS Syntax for data processing Obtain descriptive statistics, create graphs & assess normality Manipulate and transform variables #### Prerequisites: In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. For more information, please click [here](https://intersect.org.au/training/course/spss101). 2025-06-11 09:30:00 UTC 2025-06-12 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Getting started with NVivo for Windows at LTU Online
7 June 2025
Getting started with NVivo for Windows at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/getting-started-with-nvivo-for-windows-at-ltu-online-f49f7ebf-40cb-47be-b6f8-c988fa4e7c1f Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: Create and organise a qualitative research project in NVivo Import a range of data sources using NVivo’s integrated tools Code and classify your data Format your data to take advantage of NVivo’s auto-coding ability Use NVivo to discover new themes and trends in research Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. For more information, please click [here](https://intersect.org.au/training/course/nvivo101). 2025-06-07 10:00:00 UTC 2025-06-07 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Learn to Program: Python at LTU Online
9 - 10 July 2025
Learn to Program: Python at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/learn-to-program-python-at-ltu-online-4995e32f-64f1-457f-bd90-68f6f93bf3cc Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: Introduction to the JupyterLab interface for programming Basic syntax and data types in Python How to load external data into Python Creating functions (FUNCTIONS) Repeating actions and analysing multiple data sets (LOOPS) Making choices (IF STATEMENTS – CONDITIONALS) Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). For more information, please click [here](https://intersect.org.au/training/course/python101). 2025-07-09 10:00:00 UTC 2025-07-10 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Exploring Chi-square and correlation in R at UC Online
4 July 2025
Exploring Chi-square and correlation in R at UC Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/exploring-chi-square-and-correlation-in-r-at-uc-online-e42829ce-f307-408b-b067-aea169dc0f30 This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson’s r, Spearman’s Rho and Kendall’s tau) in real world datasets. #### You'll learn: Obtain inferential statistics and assess data normality Manipulate data and create graphs Perform Chi-Square tests (Goodness of Fit test and Test of Independence) Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: \Learn to Program: R\, \Data Manipulation and Visualisation in R\ For more information, please click [here](https://intersect.org.au/training/course/r210). 2025-07-04 09:30:00 UTC 2025-07-04 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Data Manipulation and Visualisation in Python at DPE Online
22 - 23 May 2025
Data Manipulation and Visualisation in Python at DPE Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/data-manipulation-and-visualisation-in-python-at-dpe-online Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation). We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: Working with pandas DataFrames Indexing, slicing and subsetting in pandas DataFrames Missing data values Combine multiple pandas DataFrames Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries Configuring plot elements within seaborn and matplotlib Exploring different types of plots using seaborn #### Prerequisites: Either \Learn to Program: Python\ or \Learn to Program: Python\ and \Python for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: Python\ and \Python for Research\ courses to ensure that you are familiar with the knowledge needed for this course. For more information, please click [here](https://intersect.org.au/training/course/python203). 2025-05-22 09:30:00 UTC 2025-05-23 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Data Manipulation in R at UC Online
6 June 2025
Data Manipulation in R at UC Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/data-manipulation-in-r-at-uc-online-612a8903-ba83-45f8-8ca8-81f4212db0c1 R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: DataFrame Manipulation using the dplyr package DataFrame Transformation using the tidyr package #### Prerequisites: Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course. For more information, please click [here](https://intersect.org.au/training/course/r201). 2025-06-06 09:30:00 UTC 2025-06-06 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Machine Learning using Python: Introduction & Linear Regression at DPE Online
11 - 12 June 2025
Introduction to Machine Learning using Python: Introduction & Linear Regression at DPE Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-machine-learning-using-python-introduction-linear-regression-at-dpe-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: Understand the difference between supervised and unsupervised Machine Learning. Understand the fundamentals of Machine Learning. Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. Understand the Machine Learning modelling workflows. Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either \Learn to Program: Python\ and \Data Manipulation in Python\ or \Learn to Program: Python\ and \Data Manipulation and Visualisation in Python\ needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. For more information, please click [here](https://intersect.org.au/training/course/python205). 2025-06-11 09:30:00 UTC 2025-06-12 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Excel for Researchers at LTU Online
11 - 12 June 2025
Excel for Researchers at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/excel-for-researchers-at-ltu-online-0ca890e1-e7dc-49a4-8f81-1d3c6e128d2e Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. We’ll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: ‘Clean up’ messy research data Organise, format and name your data Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) Perform calculations on your data using functions (MAX, MIN, AVERAGE) Extract significant findings from your data (PIVOT TABLE, VLOOKUP) Manipulate your data (convert data format, work with DATES and TIMES) Create graphs and charts to visualise your data (CHARTS) Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. For more information, please click [here](https://intersect.org.au/training/course/excel101). 2025-06-11 10:00:00 UTC 2025-06-12 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Traversing t tests in R at UC Online
11 July 2025
Traversing t tests in R at UC Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/traversing-t-tests-in-r-at-uc-online-3ad7842a-7eaf-4727-829b-5207e792b2e6 R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: Read in and manipulate data Check assumptions of t tests Perform one-sample t tests Perform two-sample t tests (Independent-samples, Paired-samples) Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect’s following courses to get up to speed: \Learn to Program: R\, \Data Manipulation and Visualisation in R\ For more information, please click [here](https://intersect.org.au/training/course/r211). 2025-07-11 09:30:00 UTC 2025-07-11 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Exploring ANOVAs in R at UC Online
17 July 2025
Exploring ANOVAs in R at UC Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/exploring-anovas-in-r-at-uc-online-831f2c6c-a14b-41b4-aa80-5e761746cb20 R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R. To better understand the tests, assumptions and associated concepts, we will be using a dataset containing the Mathematics scores of secondary students. This dataset also includes information regarding their mother’s and father’s jobs and education levels, the number of hours dedicated to study, and time spent commuting to and from school. Lifestyle information about alcohol consumption habits, whether the students have quality relationships with their families and whether they have free time after school is included in this dataset. #### You'll learn: Basic statistical theory behind ANOVAs How to check that the data meets the assumptions One-way ANOVA in R and post-hoc analysis Two-way ANOVA plus interaction effects and post-hoc analysis Non-parametric alternatives to one and two-way ANOVA #### Prerequisites: This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required. If you’re new to programming in R, we strongly recommend you register for the \Learn to Program: R\, \Data Manipulation and Visualisation in R\ workshops first. For more information, please click [here](https://intersect.org.au/training/course/r212). 2025-07-17 09:30:00 UTC 2025-07-17 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Data Capture and Surveys with REDCap at LTU Online
2 July 2025
Data Capture and Surveys with REDCap at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/data-capture-and-surveys-with-redcap-at-ltu-online-630d4057-47c4-4077-a431-d7d18549684c Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: Get started with REDCap Create and set up projects Design forms and surveys using the online designer Learn how to use branching logic, piping, and calculations Enter data via forms and distribute surveys Create, view and export data reports Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/redcap101). 2025-07-02 10:00:00 UTC 2025-07-02 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Machine Learning using Python: Introduction & Linear Regression at DU Online
4 - 5 June 2025
Introduction to Machine Learning using Python: Introduction & Linear Regression at DU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-machine-learning-using-python-introduction-linear-regression-at-du-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: Understand the difference between supervised and unsupervised Machine Learning. Understand the fundamentals of Machine Learning. Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. Understand the Machine Learning modelling workflows. Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either \Learn to Program: Python\ and \Data Manipulation in Python\ or \Learn to Program: Python\ and \Data Manipulation and Visualisation in Python\ needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. For more information, please click [here](https://intersect.org.au/training/course/python205). 2025-06-04 09:30:00 UTC 2025-06-05 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Machine Learning using Python: SVM & Unsupervised Learning at DU Online
2 July 2025
Introduction to Machine Learning using Python: SVM & Unsupervised Learning at DU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-du-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. Know the differences between various core Machine Learning models. Understand the Machine Learning modelling workflows. Use Python and scikit-learn to process real datasets, train and apply Machine Learning models. #### Prerequisites: Either \Learn to Program: Python\, \Data Manipulation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ or \Learn to Program: Python\, \Data Manipulation and Visualisation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. For more information, please click [here](https://intersect.org.au/training/course/python207). 2025-07-02 09:30:00 UTC 2025-07-02 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Machine Learning using R: SVM & Unsupervised Learning at LTU Online
28 May 2025
Introduction to Machine Learning using R: SVM & Unsupervised Learning at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-machine-learning-using-r-svm-unsupervised-learning-at-ltu-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. Know the differences between various core Machine Learning models. Understand the Machine Learning modelling workflows. Use R and its relevant packages to process real datasets, train and apply Machine Learning models. #### Prerequisites: \\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\ For more information, please click [here](https://intersect.org.au/training/course/r207). 2025-05-28 10:00:00 UTC 2025-05-28 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Data Capture and Surveys with REDCap at UC Online
22 May 2025
Data Capture and Surveys with REDCap at UC Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/data-capture-and-surveys-with-redcap-at-uc-online-ea43193d-6d9b-4ecb-9e53-f7cbf2509412 Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: Get started with REDCap Create and set up projects Design forms and surveys using the online designer Learn how to use branching logic, piping, and calculations Enter data via forms and distribute surveys Create, view and export data reports Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/redcap101). 2025-05-22 09:30:00 UTC 2025-05-22 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Research Data Management at WSU Online
18 July 2025
Introduction to Research Data Management at WSU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-research-data-management-at-wsu-online-a2e2899c-7968-4d36-934a-7762216bb462 Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: How to manage research data according to legal, statutory, ethical, funding body and university requirements Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/rdmt001). 2025-07-18 10:00:00 UTC 2025-07-18 12:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Data Manipulation and Visualisation in R at UTS City campus, Moore Park precinct
9 July 2025
Moore Park, Australia
Data Manipulation and Visualisation in R at UTS City campus, Moore Park precinct https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/data-manipulation-and-visualisation-in-r-at-uts-city-campus-moore-park-precinct R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: DataFrame Manipulation using the dplyr package DataFrame Transformation using the tidyr package Using the Grammar of Graphics to convert data into figures using the ggplot2 package Configuring plot elements within ggplot2 Exploring different types of plots using ggplot2 #### Prerequisites: Either \Learn to Program: R\ or \Learn to Program: R\ and \R for Research\ needed to attend this course. If you already have experience with programming, please check the topics covered in the \Learn to Program: R\ and \R for Research\ courses to ensure that you are familiar with the knowledge needed for this course. For more information, please click [here](https://intersect.org.au/training/course/r203). 2025-07-09 09:30:00 UTC 2025-07-09 16:30:00 UTC Intersect Australia Rugby Australia Building, Moore Park Rd, Moore Park, Australia Rugby Australia Building, Moore Park Rd Moore Park Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Getting Started with NVivo for Mac at ACU
18 June 2025
Getting Started with NVivo for Mac at ACU https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/getting-started-with-nvivo-for-mac-at-acu-e244145d-f693-4f68-b565-8a9048295f0e Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: Create and organise a qualitative research project in NVivo Import a range of data sources using NVivo’s integrated tools Code and classify your data Format your data to take advantage of NVivo’s auto-coding ability Use NVivo to discover new themes and trends in research Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users. For more information, please click [here](https://intersect.org.au/training/course/nvivo102). 2025-06-18 09:30:00 UTC 2025-06-18 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Getting started with NVivo for Windows at ACU
24 June 2025
Getting started with NVivo for Windows at ACU https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/getting-started-with-nvivo-for-windows-at-acu-ac15c59d-5459-481b-b391-95efde8a03b4 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it’s easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: Create and organise a qualitative research project in NVivo Import a range of data sources using NVivo’s integrated tools Code and classify your data Format your data to take advantage of NVivo’s auto-coding ability Use NVivo to discover new themes and trends in research Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. For more information, please click [here](https://intersect.org.au/training/course/nvivo101). 2025-06-24 09:30:00 UTC 2025-06-24 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Longitudinal Trials with REDCap at DU Online
12 June 2025
Longitudinal Trials with REDCap at DU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/longitudinal-trials-with-redcap-at-du-online REDCap is a powerful and extensible application for managing and running longitiudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shephard your participants through a complex survey at various time points with very little configuration. This course will introduce some of REDCap’s more advanced features for running longitudinal studies, and builds on the foundational material taught in REDCAP101 – Managing Data Capture and Surveys with REDCap. #### You'll learn: Build a longitudinal project Manage participants throughout multiple events Configure and use Automated Survey Invitations Use Smart Variables to add powerful features to your logic Take advantage of high-granularity permissions for your collaborators Understand the data structure of a longitudinal project #### Prerequisites: This course requires the participant to have a fairly good basic knowledge of REDCap. To come up to speed, consider taking our \Data Capture and Surveys with REDCap\ workshop. For more information, please click [here](https://intersect.org.au/training/course/redcap201). 2025-06-12 09:30:00 UTC 2025-06-12 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Machine Learning using Python: Classification at DU Online
25 - 26 June 2025
Introduction to Machine Learning using Python: Classification at DU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-du-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. Know the differences between various core Machine Learning models. Understand the Machine Learning modelling workflows. Use Python and scikit-learn to process real datasets, train and apply Machine Learning models. #### Prerequisites: Either \Learn to Program: Python\, \Data Manipulation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ or \Learn to Program: Python\, \Data Manipulation and Visualisation in Python\ and \Introduction to ML using Python: Introduction & Linear Regression\ needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. For more information, please click [here](https://intersect.org.au/training/course/python206). 2025-06-25 09:30:00 UTC 2025-06-26 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Excel for Researchers at LTU Online
16 - 17 July 2025
Excel for Researchers at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/excel-for-researchers-at-ltu-online-32b9d9a5-f037-4c4a-946a-b7df3743c7ad Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there’s too much of it. Frequently, it has errors. We’ll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: ‘Clean up’ messy research data Organise, format and name your data Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) Perform calculations on your data using functions (MAX, MIN, AVERAGE) Extract significant findings from your data (PIVOT TABLE, VLOOKUP) Manipulate your data (convert data format, work with DATES and TIMES) Create graphs and charts to visualise your data (CHARTS) Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. For more information, please click [here](https://intersect.org.au/training/course/excel101). 2025-07-16 10:00:00 UTC 2025-07-17 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Learn to Program: R at UTS City campus, Moore Park precinct
11 June 2025
Moore Park, Australia
Learn to Program: R at UTS City campus, Moore Park precinct https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/learn-to-program-r-at-uts-city-campus-moore-park-precinct R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: Introduction to the RStudio interface for programming Basic syntax and data types in R How to load external data into R Creating functions (FUNCTIONS) Repeating actions and analysing multiple data sets (LOOPS) Making choices (IF STATEMENTS – CONDITIONALS) Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found \here\. For more information, please click [here](https://intersect.org.au/training/course/r101). 2025-06-11 09:30:00 UTC 2025-06-11 16:30:00 UTC Intersect Australia Rugby Australia Building, Moore Park Rd, Moore Park, Australia Rugby Australia Building, Moore Park Rd Moore Park Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Research Data Management at WSU Online
21 November 2025
Introduction to Research Data Management at WSU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-research-data-management-at-wsu-online-09e95ef0-c087-44b6-8697-66b1622db301 Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: How to manage research data according to legal, statutory, ethical, funding body and university requirements Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/rdmt001). 2025-11-21 10:00:00 UTC 2025-11-21 12:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Research Data Management at WSU Online
20 June 2025
Introduction to Research Data Management at WSU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-research-data-management-at-wsu-online-ea46a838-978b-404c-b960-66fb9089632a Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: How to manage research data according to legal, statutory, ethical, funding body and university requirements Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/rdmt001). 2025-06-20 10:00:00 UTC 2025-06-20 12:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Research Data Management at WSU Online
19 September 2025
Introduction to Research Data Management at WSU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-research-data-management-at-wsu-online-65b15ca0-085d-4d29-8da1-1ed78e8d488a Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: How to manage research data according to legal, statutory, ethical, funding body and university requirements Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/rdmt001). 2025-09-19 10:00:00 UTC 2025-09-19 12:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Research Data Management at WSU Online
17 October 2025
Introduction to Research Data Management at WSU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-research-data-management-at-wsu-online-7414a51e-300b-4669-b9f2-d9ea8afdcfcc Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: How to manage research data according to legal, statutory, ethical, funding body and university requirements Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/rdmt001). 2025-10-17 10:00:00 UTC 2025-10-17 12:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Introduction to Research Data Management at WSU Online
22 August 2025
Introduction to Research Data Management at WSU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/introduction-to-research-data-management-at-wsu-online-76d8ad95-15fb-45bb-bf1a-7bafb9b08823 Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don’t know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: How to manage research data according to legal, statutory, ethical, funding body and university requirements Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. For more information, please click [here](https://intersect.org.au/training/course/rdmt001). 2025-08-22 10:00:00 UTC 2025-08-22 12:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Learn to Program: Python at LTU Online
28 - 29 May 2025
Learn to Program: Python at LTU Online https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/learn-to-program-python-at-ltu-online-c6cbcba1-b524-4278-b487-4f2ee8c7db50 Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: Introduction to the JupyterLab interface for programming Basic syntax and data types in Python How to load external data into Python Creating functions (FUNCTIONS) Repeating actions and analysing multiple data sets (LOOPS) Making choices (IF STATEMENTS – CONDITIONALS) Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). For more information, please click [here](https://intersect.org.au/training/course/python101). 2025-05-28 10:00:00 UTC 2025-05-29 13:00:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution [] -
Randomised Controlled Trials with REDCap at ACU
28 May 2025
Randomised Controlled Trials with REDCap at ACU https://intersect.org.au/training/schedule https://staging.dresa.org.au/events/randomised-controlled-trials-with-redcap-at-acu REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. In this course, learn how to manage a Randomised Controlled Trial using REDCap, including the randomisation module, adverse event reporting and automated participant withdrawals. This course will introduce some of REDCap’s more advanced features for running randomised trials, and builds on the material taught in REDCAP201 – Longitudinal Trials with REDCap. #### You'll learn: - Create Data Access Groups (DAGs) and assign users to manage trial sites - Build randomisation allocation table - Enable and implement participant randomisation module - Design an adverse reporting system using Automated Survey Invitations and Alerts - Create an automated participant withdrawal process - Customise record dashboards #### Prerequisites: Learners should have a solid understanding of REDCap and be familiar with the content of [Data Capture and Surveys with REDCap](https://intersectaustralia.github.io/training/REDCAP101/) and [Longitudinal Trials with REDCap](https://intersectaustralia.github.io/training/REDCAP201/). For more information, please click [here](https://intersect.org.au/training/course/redcap202). 2025-05-28 09:30:00 UTC 2025-05-28 12:30:00 UTC Intersect Australia Australia Australia Intersect Australia training@intersect.org.au [] [] [] host_institution []
