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Keywords: R software  or Python 

ALA Labs

ALA Labs provides resources and articles from the Atlas of Living Australia's Science and Decision Support team. On the website, you can find:

  • Posts: Code, articles, analyses and visualisations that will hopefully help you in your own work
  • Research: Highlighted summaries of scientific...

Keywords: Ecology, R, Python, Rstats, Biodiversity data, Open science, Reproducibility, Coding, Data cleaning, Data visualisation, Species Distribution Modelling, Beginner R coding

ALA Labs https://dresa.org.au/materials/ala-labs ALA Labs provides resources and articles from the Atlas of Living Australia's Science and Decision Support team. On the website, you can find: - Posts: Code, articles, analyses and visualisations that will hopefully help you in your own work - Research: Highlighted summaries of scientific research that has used data from the Atlas of Living Australia - Software: R & Python packages that the Science & Decision Support team manage - Books: Long-form resources with best-practice data wrangling and visualisation - Gallery: Showcasing external work that uses tools from ALA Labs Atlas of Living Australia support@ala.org.au Ecology, R, Python, Rstats, Biodiversity data, Open science, Reproducibility, Coding, Data cleaning, Data visualisation, Species Distribution Modelling, Beginner R coding
Tutorials to learn how to use STAN

Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics.

Keywords: Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB

Tutorials to learn how to use STAN https://dresa.org.au/materials/tutorials-to-learn-how-to-use-stan Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics. https://mc-stan.org/about/team/ Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB
Species Distribution Modelling in R

This set of scripts and videos provide an introduction to running SDMs in R and include some steps to consider that go beyond what's available in the EcoCommons SDM point-and-click tools.

Five videos include: 1. An introduction to SDM in R, 2. occurrence data, 3. environmental data, 4. fitting...

Keywords: Species Distribution Modelling, Ecology, R software, EcoCommons

Species Distribution Modelling in R https://dresa.org.au/materials/species-distribution-modelling-in-r This set of scripts and videos provide an introduction to running SDMs in R and include some steps to consider that go beyond what's available in the EcoCommons SDM point-and-click tools. Five videos include: 1. An introduction to SDM in R, 2. occurrence data, 3. environmental data, 4. fitting your model, 5. model evaluation Scripts and files are available here: https://github.com/EcoCommons-Australia/educational_material/tree/main/SDMs_in_R/Scripts Scripts for all four modules are here: https://www.ecocommons.org.au/wp-content/uploads/EcoCommons_steps_1_to_4.html https://www.ecocommons.org.au/contact/ Species Distribution Modelling, Ecology, R software, EcoCommons ugrad mbr phd
WORKSHOP: Single cell RNAseq analysis in R

This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022.

Event description

Analysis and interpretation of single cell RNAseq (scRNAseq) data...

Keywords: Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq

WORKSHOP: Single cell RNAseq analysis in R https://dresa.org.au/materials/workshop-single-cell-rnaseq-analysis-in-r-4f60b82d-2f1e-4021-9569-6955878dd945 This record includes training materials associated with the Australian BioCommons workshop ‘Single cell RNAseq analysis in R’. This workshop took place over two, 3.5 hour sessions on 22 and 3 August 2022. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data.  We will discuss the ‘why’ behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons and Queensland Cyber Infrastructure Foundation (QCIF) with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. scRNAseq_Slides (PDF): Slides used to introduce topics scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop scRNAseq_Resources (PDF): A list of resources recommended by trainers and participants scRNAseq_QandA(PDF): Archive of questions and their answers from the workshop Slack Channel.   Materials shared elsewhere: This workshop follows the tutorial ‘scRNAseq Analysis in R with Seurat’ https://swbioinf.github.io/scRNAseqInR_Doco/index.html This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here. Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Transcriptomics, R software, Single cell RNAseq, scRNAseq
WORKSHOP: R: fundamental skills for biologists

This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.

 

Event description

Biologists need data analysis skills to be able to...

Keywords: Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation

WORKSHOP: R: fundamental skills for biologists https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists-81aa00db-63ad-4962-a7ac-b885bf9f676b This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.   Event description Biologists need data analysis skills to be able to interpret, visualise and communicate their research results. While Excel can cover some data analysis needs, there is a better choice, particularly for large and complex datasets.  R is a free, open-source software and programming language that enables data exploration, statistical analysis, visualisation and more. The large variety of R packages available for analysing biological data make it a robust and flexible option for data of all shapes and sizes.  Getting started can be a little daunting for those without a background in statistics and programming. In this workshop we will equip you with the foundations for getting the most out of R and RStudio, an interactive way of structuring and keeping track of your work in R. Using biological data from a model of influenza infection, you will learn how to efficiently and reproducibly organise, read, wrangle, analyse, visualise and generate reports from your data in R. Topics covered in this workshop include: Spreadsheets, organising data and first steps with R Manipulating and analysing data with dplyr Data visualisation Summarized experiments and getting started with Bioconductor   This workshop is presented by the Australian BioCommons and Saskia Freytag from WEHI  with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Schedule (PDF): A breakdown of the topics and timings for the workshop Recommended resources (PDF): A list of resources recommended by trainers and participants Q_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel. Materials shared elsewhere:   This workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available. https://saskiafreytag.github.io/biocommons-r-intro/ This is derived from material produced as part of The Carpentries Incubator project https://carpentries-incubator.github.io/bioc-intro/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation
WORKSHOP: Working with genomics sequences and features in R with Bioconductor

This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021.

Workshop description

Explore the many useful functions that the Bioconductor...

Keywords: R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis

WORKSHOP: Working with genomics sequences and features in R with Bioconductor https://dresa.org.au/materials/workshop-working-with-genomics-sequences-and-features-in-r-with-bioconductor-8399bf0d-1e9e-48f3-a840-3f70f23254bb This record includes training materials associated with the Australian BioCommons workshop ‘Working with genomics sequences and features in R with Bioconductor’. This workshop took place on 23 September 2021. Workshop description Explore the many useful functions that the Bioconductor environment offers for working with genomic data and other biological sequences.  DNA and proteins are often represented as files containing strings of nucleic acids or amino acids. They are associated with text files that provide additional contextual information such as genome annotations. This workshop provides hands-on experience with tools, software and packages available in R via Bioconductor for manipulating, exploring and extracting information from biological sequences and annotation files. We will look at tools for working with some commonly used file formats including FASTA, GFF3, GTF, methods for identifying regions of interest, and easy methods for obtaining data packages such as genome assemblies.  This workshop is presented by the Australian BioCommons and Monash Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Schedule (PDF): schedule for the workshop providing a breakdown of topics and timings   Materials shared elsewhere: This workshop follows the tutorial ‘Working with DNA sequences and features in R with Bioconductor - version 2’ developed for Monash Bioinformatics Platform and Monash Data Fluency by Paul Harrison. https://monashdatafluency.github.io/r-bioc-2/ Melissa Burke (melissa@biocommons.org.au) R software, Bioconductor, Bioinformatics, Analysis, Genomics, Sequence analysis
VOSON Lab Code Blog

The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.

Keywords: visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics

Resource type: tutorial, other

VOSON Lab Code Blog https://dresa.org.au/materials/voson-lab-code-blog The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages. robert.ackland@anu.edu.au visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics researcher support phd masters
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://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 Python
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://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 Python
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://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 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://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 Python
Data Visualisation in Python

Course Materials

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://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 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://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 Python
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://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 Python
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://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 Python
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://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 Python
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://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 Python, R, Matlab, Julia
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://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 Python, R