[{"id":345,"title":"Randomised Controlled Trials with REDCap","url":"https://staging.dresa.org.au/materials/randomised-controlled-trials-with-redcap.json","description":"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.\n\n- Create Data Access Groups (DAGs) and assign users to manage trial sites\n- Build randomisation allocation table \n- Enable and implement participant randomisation module\n- Design an adverse reporting system using Automated Survey Invitations and Alerts\n- Create an automated participant withdrawal process\n- Customise record dashboards\n\nLearners 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/).","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":287,"title":"Data Entry, Exploration, \u0026 Analysis in SPSS","url":"https://staging.dresa.org.au/materials/data-entry-exploration-analysis-in-spss.json","description":"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.  \n  \n 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.\n\n- Navigate SPSS Variable and Data views.\n- Create and describe data from scratch.\n- Import Data from Excel.\n- Familiarise yourself with exploratory data analysis (EDA), including: \n  - Understand variable types, identity missing data and outliers.\n  - Visualise data in graphs and tables.\n- Compose SPSS Syntax to repeat and store analysis steps.\n- Generate a report testing assumptions of statistical tests.\n- Additional exercises:\n- Check assumptions for common statistical tests.\n- Make stunning plots.\n\nIn 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.","doi":"10.5281/zenodo.6423850","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":271,"title":"Exploring Chi-Square and Correlation in SPSS","url":"https://staging.dresa.org.au/materials/exploring-chi-square-and-correlation-in-spss-84543235-5cfe-4268-8546-b16727125073.json","description":"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.\n\n#### You'll learn:\n\n- Perform Pearson’s Correlation (r) Test\n- Perform Spearman’s Rho Correlation (⍴) Test\n- Carry out basic reliability analysis on survey items\n- Perform Chi-Square Goodness-of-Fit test\n- Perform Chi-Square Test of Association\n\n#### Prerequisites:\n\nIn 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.\n\nThis 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.\n\n  \n  \n  \n**For more information, please click [here](https://intersect.org.au/training/course/spss102).**","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":270,"title":"Beyond Basics: Conditionals and Visualisation in Excel","url":"https://staging.dresa.org.au/materials/beyond-basics-conditionals-and-visualisation-in-excel.json","description":"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.\n\n- Cell syntax and conditional formatting\n- IF functions\n- Pivot Table summaries\n- Nesting multiple AND/IF/OR calculations\n- Combining nested calculations with conditional formatting to bring out important elements of the dataset\n- MINIFS function\n- Box plot creation and outlier identification\n- Trendline and error bar chart enhancements\n\nFamiliarity with the content of Excel for Researchers, specifically: \n\n- the general Office/Excel interface (menus, ribbons/toolbars, etc.)\n- workbooks and worksheets\n- absolute and relative references, e.g. $A$1, A1.\n- simple ranges, e.g. A1:B5","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":263,"title":"Data Entry and Processing in SPSS","url":"https://staging.dresa.org.au/materials/data-entry-and-processing-in-spss.json","description":"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.  \n  \n 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.\n\nNavigate the SPSS working environment  \n Prepare data files and define variables  \n Enter data in SPSS and Import data from Excel  \n Perform data screening  \n Compose SPSS Syntax for data processing  \n Obtain descriptive statistics, create graphs \u0026amp; assess normality  \n Manipulate and transform variables\n\nIn 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.","doi":"10.5281/zenodo.6423850","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":264,"title":"Exploring Chi-Square and correlation in SPSS","url":"https://staging.dresa.org.au/materials/exploring-chi-square-and-correlation-in-spss.json","description":"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.\n\n#### You'll learn:\n\n- Perform Pearson’s Correlation (r) Test\n- Perform Spearman’s Rho Correlation (⍴) Test\n- Carry out basic reliability analysis on survey items\n- Perform Chi-Square Goodness-of-Fit test\n- Perform Chi-Square Test of Association\n\n#### Prerequisites:\n\nThis workshop is recommended for researchers and postgraduate students who have previously attended the Intersect’s Data Entry and Processing in SPSS training course.\n\n  \n  \n  \n**For more information, please click [here](https://intersect.org.au/training/course/spss102).**","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":266,"title":"Getting Started with Tableau for Data Analysis and Visualisation","url":"https://staging.dresa.org.au/materials/getting-started-with-tableau-for-data-analysis-and-visualisation.json","description":"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.\n\nThis 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.\n\n#### You'll learn:\n\n- Import and combine data\n- Filter data\n- Create cross tabulation table\n- Create interactive plots including graph map\n- Create and design an interactive dashboard\n\n#### Prerequisites:\n\nThe course has no prerequisites.\n\n  \n  \n  \n**For more information, please click [here](https://intersect.org.au/training/course/tableau101).**","doi":"","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":248,"title":"Data Manipulation in R","url":"https://staging.dresa.org.au/materials/data-manipulation-in-r.json","description":"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.  \n  \n 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).  \n  \n We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.  \n  \n 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.\n\nDataFrame Manipulation using the dplyr package  \n DataFrame Transformation using the tidyr package\n\nEither \\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.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":258,"title":"Data Capture and Surveys with REDCap","url":"https://staging.dresa.org.au/materials/data-capture-and-surveys-with-redcap.json","description":"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.  \n  \n 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.\n\nGet started with REDCap  \n Create and set up projects  \n Design forms and surveys using the online designer  \n Learn how to use branching logic, piping, and calculations  \n Enter data via forms and distribute surveys  \n Create, view and export data reports  \n Add collaborators and set their privileges\n\nThe course has no prerequisites.","doi":"10.5281/zenodo.6423762","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":257,"title":"Research Data Management Techniques","url":"https://staging.dresa.org.au/materials/research-data-management-techniques.json","description":"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?  \n  \n 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.\n\nHow to manage research data according to legal, statutory, ethical, funding body and university requirements  \n Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data  \n Services supporting research data at your institution\n\nThe course has no prerequisites.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":253,"title":"Introduction to Machine Learning using R: SVM \u0026 Unsupervised Learning","url":"https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning.json","description":"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.\n\nComprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.  \n Know the differences between various core Machine Learning models.  \n Understand the Machine Learning modelling workflows.  \n Use R and its relevant packages to process real datasets, train and apply Machine Learning models.\n\n\\\\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 \u0026amp; 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.\\\\","doi":"10.5281/zenodo.6423747","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":265,"title":"Databases and SQL","url":"https://staging.dresa.org.au/materials/databases-and-sql.json","description":"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.  \n  \n 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.  \n  \n Developed using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.\n\nUnderstand and compose a query using SQL  \n Use the SQL syntax to select, sort and filter data  \n Calculate new values from existing data  \n Aggregate data into sums, averages, and other operations  \n Combine data from multiple tables  \n Design and build your own relational databases\n\nThe course has no prerequisites.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":259,"title":"Longitudinal Trials with REDCap","url":"https://staging.dresa.org.au/materials/longitudinal-trials-with-redcap.json","description":"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.  \n  \n 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.\n\nBuild a longitudinal project  \n Manage participants throughout multiple events  \n Configure and use Automated Survey Invitations  \n Use Smart Variables to add powerful features to your logic  \n Take advantage of high-granularity permissions for your collaborators  \n Understand the data structure of a longitudinal project\n\nThis 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.","doi":"10.5281/zenodo.6423773","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":245,"title":"Learn to Program: R","url":"https://staging.dresa.org.au/materials/learn-to-program-r.json","description":"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.  \n  \n But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in.  \n  \n We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.  \n  \n 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.\n\nIntroduction to the RStudio interface for programming  \n Basic syntax and data types in R  \n How to load external data into R  \n Creating functions (FUNCTIONS)  \n Repeating actions and analysing multiple data sets (LOOPS)  \n Making choices (IF STATEMENTS – CONDITIONALS)  \n Ways to visualise data in R\n\nNo prior experience with programming needed to attend this course.  \n  \n 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\\.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":240,"title":"Data Manipulation and Visualisation in Python","url":"https://staging.dresa.org.au/materials/data-manipulation-and-visualisation-in-python.json","description":"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.  \n  \n 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).  \n  \n 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.  \n  \n 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.\n\nWorking with pandas DataFrames  \n Indexing, slicing and subsetting in pandas DataFrames  \n Missing data values  \n Combine multiple pandas DataFrames  \n Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries  \n Configuring plot elements within seaborn and matplotlib  \n Exploring different types of plots using seaborn\n\nEither \\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.","doi":"10.5281/zenodo.6423718","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":242,"title":"Introduction to Machine Learning using Python: Classification","url":"https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-python-classification.json","description":"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.\n\nComprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.  \n Know the differences between various core Machine Learning models.  \n Understand the Machine Learning modelling workflows.  \n Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.\n\nEither \\Learn to Program: Python\\, \\Data Manipulation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ or \\Learn to Program: Python\\, \\Data Manipulation and Visualisation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ needed to attend this course.   \n 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.  \n 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.","doi":"10.5281/zenodo.6423726","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":243,"title":"Introduction to Machine Learning using Python: SVM \u0026 Unsupervised Learning","url":"https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-python-svm-unsupervised-learning.json","description":"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.\n\nComprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.  \n Know the differences between various core Machine Learning models.  \n Understand the Machine Learning modelling workflows.  \n Use Python and scikit-learn to process real datasets, train and apply Machine Learning models.\n\nEither \\Learn to Program: Python\\, \\Data Manipulation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ or \\Learn to Program: Python\\, \\Data Manipulation and Visualisation in Python\\ and \\Introduction to ML using Python: Introduction \u0026amp; Linear Regression\\ needed to attend this course.   \n 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.  \n 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.","doi":"10.5281/zenodo.6423728","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":239,"title":"Data Visualisation in Python","url":"https://staging.dresa.org.au/materials/data-visualisation-in-python.json","description":"[Course Materials](https://intersectaustralia.github.io/training/PYTHON203/sources/Data-Adv_Python.zip)\n\nUsing the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries  \n Configuring plot elements within seaborn and matplotlib  \n Exploring different types of plots using seaborn\n\nEither \\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.  \n  \n We also strongly recommend attending the \\Data Manipulation in Python\\.","doi":"10.5281/zenodo.6423716","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":251,"title":"Introduction to Machine Learning using R: Introduction \u0026 Linear Regression","url":"https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-r-introduction-linear-regression.json","description":"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.\n\nUnderstand the difference between supervised and unsupervised Machine Learning.  \n Understand the fundamentals of Machine Learning.  \n Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.  \n Understand the Machine Learning modelling workflows.  \n Use R and and its relevant packages to process real datasets, train and apply Machine Learning models\n\n\\\\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.\\\\","doi":"10.5281/zenodo.6423740","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":256,"title":"Exploring ANOVAs in R","url":"https://staging.dresa.org.au/materials/exploring-anovas-in-r.json","description":"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.\n\nANOVA (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.\n\n- Basic statistical theory behind ANOVAs\n- How to check that the data meets the assumptions\n- One-way ANOVA in R and post-hoc analysis\n- Two-way ANOVA plus interaction effects and post-hoc analysis\n- Non-parametric alternatives to one and two-way ANOVA\n\nThis 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.  \n  \n 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. ","doi":"10.5281/zenodo.6423760","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":241,"title":"Introduction to Machine Learning using Python: Introduction \u0026 Linear Regression","url":"https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-python-introduction-linear-regression.json","description":"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.\n\nUnderstand the difference between supervised and unsupervised Machine Learning.  \n Understand the fundamentals of Machine Learning.  \n Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.  \n Understand the Machine Learning modelling workflows.  \n Use Python and scikit-learn to process real datasets, train and apply Machine Learning models\n\nEither \\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.   \n 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.  \n 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.","doi":"10.5281/zenodo.6423722","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":260,"title":"Cleaning Data with Open Refine","url":"https://staging.dresa.org.au/materials/cleaning-data-with-open-refine.json","description":"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?  \n  \n 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.\n\nDownload, install and run Open Refine  \n Import data from csv, text or online sources and create projects  \n Navigate data using the Open Refine interface  \n Explore data by using facets  \n Clean data using clustering  \n Parse data using GREL syntax  \n Extend data using Application Programming Interfaces (APIs)  \n Export project for use in other applications\n\nThe course has no prerequisites.","doi":"10.5281/zenodo.6423840","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":268,"title":"Collecting Web Data","url":"https://staging.dresa.org.au/materials/collecting-web-data.json","description":"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.  \n  \n 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.  \n  \n 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.\n\nThe concept of structured data  \n The use of XPath queries on HTML document  \n How to scrape data using browser extensions  \n How to scrape using Python and Scrapy  \n How to automate the scraping of multiple web pages\n\nA 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.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":261,"title":"Mastering text with Regular Expressions","url":"https://staging.dresa.org.au/materials/mastering-text-with-regular-expressions.json","description":"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?  \n  \n 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.\n\nComprehend and apply the syntax of regular expressions  \n Use the http://regexr.com tool to test a regular expression against some text  \n 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  \n Craft and test a progressively more complex regular expression  \n Find helpful resources covering regular expressions on the web\n\nComprehend and apply the syntax of regular expressions  \n Use the http://regexr.com tool to test a regular expression against some text  \n 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  \n Craft and test a progressively more complex regular expression  \n Find helpful resources covering regular expressions on the web","doi":"10.5281/zenodo.6423846","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":246,"title":"R for Social Scientists","url":"https://staging.dresa.org.au/materials/r-for-social-scientists.json","description":"R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.  \n  \n But getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in.  \n  \n 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.\n\nBasic syntax and data types in R  \n RStudio interface  \n How to import CSV files into R  \n The structure of data frames  \n A brief introduction to data wrangling and data transformation  \n How to calculate summary statistics  \n A brief introduction to visualise data\n\nNo prior experience with programming needed to attend this course.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":267,"title":"Unix Shell and Command Line Basics","url":"https://staging.dresa.org.au/materials/unix-shell-and-command-line-basics.json","description":"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.  \n  \n 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.  \n  \n 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.\n\nNavigate and work with files and directories (folders)  \n Use a selection of essential tools  \n Combine data and tools to build a processing workflow  \n Automate repetitive analysis using the command line\n\nThe course has no prerequisites.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":247,"title":"R for Research","url":"https://staging.dresa.org.au/materials/r-for-research.json","description":"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.  \n  \n 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.  \n  \n We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.  \n  \n 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.\n\nProject Management with RStudio  \n Introduction to Data Structures in R  \n Introduction to DataFrames in R  \n Selecting values in DataFrames  \n Quick introduction to Plotting using the ggplot2 package\n\n\\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.","doi":null,"remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":250,"title":"Data Manipulation and Visualisation in R","url":"https://staging.dresa.org.au/materials/data-manipulation-and-visualisation-in-r.json","description":"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.  \n  \n 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).  \n  \n We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.  \n  \n 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.\n\nDataFrame Manipulation using the dplyr package  \n DataFrame Transformation using the tidyr package  \n Using the Grammar of Graphics to convert data into figures using the ggplot2 package  \n Configuring plot elements within ggplot2  \n Exploring different types of plots using ggplot2\n\nEither \\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.","doi":"10.5281/zenodo.6423738","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":255,"title":"Traversing t tests in R","url":"https://staging.dresa.org.au/materials/traversing-t-tests-in-r.json","description":"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.  \n  \n 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.\n\nRead in and manipulate data  \n Check assumptions of t tests  \n Perform one-sample t tests  \n Perform two-sample t tests (Independent-samples, Paired-samples)  \n Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test)\n\nThis 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\\","doi":"10.5281/zenodo.6423756","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":244,"title":"Surveying with Qualtrics","url":"https://staging.dresa.org.au/materials/surveying-with-qualtrics.json","description":"Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?  \n  \n 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.  \n  \n If your institution has a licence to Qualtrics, then this course is right for you.\n\nFormat a sample survey using the Qualtrics online platform  \n Configure the survey using a range of design features to improve user experience  \n Decide which distribution channel is right for your needs  \n Understand the available data analysis and export options in Qualtrics\n\nYou 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.","doi":"10.5281/zenodo.6423732","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]}]