[{"id":286,"title":"Learn to Program: R","url":"https://staging.dresa.org.au/materials/learn-to-program-r-ce8796ad-a8ad-48b7-9a30-b43c8046d8d1.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\nBut getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in.\n\nWe teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.\n\nJoin 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\n- Introduction 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\n**No prior experience with programming** needed to attend this course.\n\nWe strongly recommend attending the [Start Coding without Hesitation: Programming Languages Showdown](https://intersect.org.au/training/course/coding001/) and [Thinking like a computer: The Fundamentals of Programming](https://intersect.org.au/training/course/coding003/) webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).","doi":"10.5281/zenodo.57541","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\nThe skills developed in [Learn to Program: R](https://intersect.org.au/training/course/r101/) are needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) 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":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":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":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":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":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":252,"title":"Introduction to Machine Learning using R: Classification","url":"https://staging.dresa.org.au/materials/introduction-to-machine-learning-using-r-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 R programming language and its scientific computing packages.\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 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 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.6423743","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":254,"title":"Exploring Chi-square and correlation in R","url":"https://staging.dresa.org.au/materials/exploring-chi-square-and-correlation-in-r.json","description":"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.\n\nObtain inferential statistics and assess data normality  \n Manipulate data and create graphs  \n Perform Chi-Square tests (Goodness of Fit test and Test of Independence)  \n Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau)\n\nThis 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).   \n 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.6423750","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\n- DataFrame 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\nThe skills developed in [Learn to Program: R](https://intersect.org.au/training/course/r101/) are needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) course 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":249,"title":"Data Visualisation in R","url":"https://staging.dresa.org.au/materials/data-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 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\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\nThe skills developed in [Learn to Program: R](https://intersect.org.au/training/course/r101/) are needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) course to ensure that you are familiar with the knowledge needed for this course.\n\nWe also strongly recommend attending the [Data Manipulation in R](https://intersect.org.au/training/course/r201/) course.","doi":"10.5281/zenodo.6423736","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":221,"title":"A showcase of Data Analysis in Python and R: A case study using COVID-19 data","url":"https://staging.dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data.json","description":"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.\n\nCleaning up a dataset for analysis  \n Using Jupyter lab for interactive analysis  \n Making the most of the tidyverse (R) and pandas (python)  \n Simple data visualisation using ggplot (R) and seaborn (python)  \n Best practices for readable code\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423522","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]},{"id":220,"title":"Start Coding without Hesitation: Programming Languages Showdown","url":"https://staging.dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown.json","description":"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.\n\nWhy use Programming  \n An overview of Python, R, MATLAB, and Julia  \n Code comparison of the four programming languages  \n Popularity and job opportunities  \n Intersect’s comparison  \n General guidelines on how to choose the best programming language for your research\n\nThe webinar has no prerequisites.","doi":"10.5281/zenodo.6423516","remote_updated_date":null,"remote_created_date":null,"scientific_topics":[],"operations":[]}]