From PC to Cloud or High Performance Computing
Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.
We may refer to both types as “large scale computing” - but what...
Keywords: Research Computing
From PC to Cloud or High Performance Computing
https://intersect.org.au/training/course/compute001
https://dresa.org.au/materials/from-pc-to-cloud-or-high-performance-computing
Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.
We may refer to both types as “large scale computing” - but what is the difference? Both systems target scalability of computing, but in different ways.
This webinar will give a good overview to the researchers thinking to make a move from their local computer to Cloud of High Performance Computing Cluster.
#### You'll learn:
- Introduction
- HPC vs Cloud computing
- When to use HPC
- When to use the Cloud
- The Cloud – Pros and Cons
- HPC – Pros and Cons
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/compute001).**
training@intersect.org.au
Research Computing
Survey Tools in Research: REDCap and Qualtrics
Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your...
Keywords: Data Management, REDCap, Qualtrics
Survey Tools in Research: REDCap and Qualtrics
https://intersect.org.au/training/course/surveys001
https://dresa.org.au/materials/survey-tools-in-research-redcap-and-qualtrics
Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your research up for success through intuitive data collection and validation, scheduling and reporting.
This webinar will introduce and compare two of the most popular research tools for the collection of survey data and patient records: REDCap and Qualtrics.
#### You'll learn:
- Electronic Data Capture: Surveys vs Forms
- Confidential vs Anonymous data collection
- Strengths and weaknesses of Qualtrics and REDCap
- Real-life use cases for each tool
- Using survey tools for longitudinal studies
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/surveys001).**
training@intersect.org.au
Data Management, REDCap, Qualtrics
Getting Started with Excel
We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors.
This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool,...
Keywords: Data Analysis, Excel
Getting Started with Excel
https://intersect.org.au/training/course/excel001
https://dresa.org.au/materials/getting-started-with-excel
We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors.
This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data.
#### You'll learn:
- Introduction to Microsoft Excel user interface
- Interpret data using sorting, filtering, and conditional formatting
- Summarise data using functions
- Analyse data using pivot tables
- Manipulate and visualise data
- Handy tips to speed up your work
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/excel001).**
training@intersect.org.au
Data Analysis, Excel
Excel for Researchers
Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise,...
Keywords: Data Analysis, Excel
Excel for Researchers
https://intersect.org.au/training/course/excel101
https://dresa.org.au/materials/excel-for-researchers
Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data.
While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data.
#### You'll learn:
- 'Clean up’ messy research data
- Organise, format and name your data
- Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING)
- Perform calculations on your data using functions (MAX, MIN, AVERAGE)
- Extract significant findings from your data (PIVOT TABLE, VLOOKUP)
- Manipulate your data (convert data format, work with DATES and TIMES)
- Create graphs and charts to visualise your data (CHARTS)
- Handy tips to speed up your work
#### Prerequisites:
In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
**For more information, please click [here](https://intersect.org.au/training/course/excel101).**
training@intersect.org.au
Data Analysis, Excel
Version Control with Git
Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there...
Keywords: Data Management, Git
Version Control with Git
https://intersect.org.au/training/course/git101
https://dresa.org.au/materials/version-control-with-git
Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available.
Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- keep versions of data, scripts, and other files
- examine commit logs to find which files were changed when
- restore earlier versions of files
- compare changes between versions of a file
- push your versioned files to a remote location, for backup and to facilitate collaboration
#### Prerequisites:
The course has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/git101).**
training@intersect.org.au
Data Management, Git
Getting started with HPC using PBS Pro
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis...
Keywords: Research Computing, HPC
Getting started with HPC using PBS Pro
https://intersect.org.au/training/course/hpc201
https://dresa.org.au/materials/getting-started-with-hpc-using-pbs-pro
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?
High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using PBS Pro.
#### You'll learn:
- Connect to an HPC cluster
- Use the Unix command line to operate a remote computer and create job scripts
- Submit and manage jobs on a cluster using a scheduler
- Transfer files to and from a remote computer
- Use software through environment modules
- Use parallelisation to speed up data analysis
- Access the facilities available to you as a researcher
- This is the PBS Pro version of the Getting Started with HPC course.
#### Prerequisites:
This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our [Unix Shell and Command Line Basics](https://intersect.org.au/training/course/unix101/) course.
**For more information, please click [here](https://intersect.org.au/training/course/hpc201).**
training@intersect.org.au
Research Computing, HPC
Getting started with HPC using Slurm
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis...
Keywords: Research Computing, HPC
Getting started with HPC using Slurm
https://intersect.org.au/training/course/hpc202
https://dresa.org.au/materials/getting-started-with-hpc-using-slurm
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?
High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using Slurm.
#### You'll learn:
- Connect to an HPC cluster
- Use the Unix command line to operate a remote computer and create job scripts
- Submit and manage jobs on a cluster using a scheduler
- Transfer files to and from a remote computer
- Use software through environment modules
- Use parallelisation to speed up data analysis
- Access the facilities available to you as a researcher
- This is the Slurm version of the Getting Started with HPC course.
#### Prerequisites:
This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our [Unix Shell and Command Line Basics](https://intersect.org.au/training/course/unix101/) course.
**For more information, please click [here](https://intersect.org.au/training/course/hpc202).**
training@intersect.org.au
Research Computing, HPC
Parallel Programming for HPC
You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you've submitted batch jobs.
Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance...
Keywords: Research Computing, HPC
Parallel Programming for HPC
https://intersect.org.au/training/course/hpc301
https://dresa.org.au/materials/parallel-programming-for-hpc
You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you've submitted batch jobs.
Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance Computing.
#### You'll learn:
- OpenMP (Open Multi-Processing): a widespread method for shared memory programming
- MPI (Message Passing Interface): a leading distributed memory programming model
#### Prerequisites:
To do this course you need to have:
A good working knowledge of HPC. Consider taking our
Getting Started with HPC using PBS Pro course to come up to speed beforehand.
Prior experience of writing programs in either C or Fortran.
**For more information, please click [here](https://intersect.org.au/training/course/hpc301).**
training@intersect.org.au
Research Computing, HPC
Learn to Program: Julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...
Keywords: Programming, Julia
Learn to Program: Julia
https://intersect.org.au/training/course/julia101
https://dresa.org.au/materials/learn-to-program-julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages!
This workshop expects that you are coming to Julia with some experience in the basic concepts of programming in another language. It is designed to help you migrate the basic concepts of programming that you already know to the Julia context.
Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly.
#### You'll learn:
- Introduction to the JupyterLab interface for programming
- Basic syntax and data types in Julia
- How to load external data into Julia
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data using the Plots library in Julia
#### Prerequisites:
Some experience with the basic concepts of programming in another language needed to attend this course. It is an intensive course that is designed to help you migrate the basic concepts of programming that you already know to the Julia context in half a day instead of a full day. If you don't have any prior experience in programming, please consider attending one of the [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) prior to this course.
We also strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/julia101).**
training@intersect.org.au
Programming, Julia
Beyond the Basics: Julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...
Keywords: Programming, Julia
Beyond the Basics: Julia
https://intersect.org.au/training/course/julia201
https://dresa.org.au/materials/beyond-the-basics-julia
Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages!
This workshop explores the more advanced features of functions in Julia, introduces widely used tools within Julia, as well as demonstrates the speed of Julia by benchmarking functions and different styles of scripting within Julia.
Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly.
#### You'll learn:
- Understand the role of Types within Julia
- Create functions with complex arguments
- Demonstrate programming patterns of list comprehension, pipes, and anonymous functions.
- Benchmark Julia code and understand how to make it fast
#### Prerequisites:
If you already have experience with programming, please check the topics covered in the [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/) to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/julia201).**
training@intersect.org.au
Programming, Julia
Learn to Program: MATLAB
MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you're just getting started - with MATLAB and, more generally, with programming?
Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors!
So...
Keywords: Programming, MATLAB
Learn to Program: MATLAB
https://intersect.org.au/training/course/matlab101
https://dresa.org.au/materials/learn-to-program-matlab
MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits. But what if you're just getting started - with MATLAB and, more generally, with programming?
Nothing beats a hands-on, face-to-face training session to get you past the inevitable syntax errors!
So join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the MATLAB interface for programming
- Basic syntax and data types in MATLAB
- How to load external data into MATLAB
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS – CONDITIONALS)
- Ways to visualise data in MATLAB
#### Prerequisites:
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/matlab101).**
training@intersect.org.au
Programming, MATLAB
Getting started with NVivo for Windows
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply...
Keywords: Data Analysis, NVivo
Getting started with NVivo for Windows
https://intersect.org.au/training/course/nvivo101
https://dresa.org.au/materials/getting-started-with-nvivo-for-windows
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.
#### You'll learn:
- Create and organise a qualitative research project in NVivo
- Import a range of data sources using NVivo's integrated tools
- Code and classify your data
- Format your data to take advantage of NVivo’s auto-coding ability
- Use NVivo to discover new themes and trends in research
- Visualise relationships and trends in your data
#### Prerequisites:
In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users.
**For more information, please click [here](https://intersect.org.au/training/course/nvivo101).**
training@intersect.org.au
Data Analysis, NVivo
Getting Started with NVivo for Mac
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply...
Keywords: Data Management, NVivo
Getting Started with NVivo for Mac
https://intersect.org.au/training/course/nvivo102
https://dresa.org.au/materials/getting-started-with-nvivo-for-mac
Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.
NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.
#### You'll learn:
- Create and organise a qualitative research project in NVivo
- Import a range of data sources using NVivo's integrated tools
- Code and classify your data
- Format your data to take advantage of NVivo’s auto-coding ability
- Use NVivo to discover new themes and trends in research
- Visualise relationships and trends in your data
#### Prerequisites:
In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software.
This course is taught using NVivo 12 Pro for Mac and is not suitable for NVivo for Windows users.
**For more information, please click [here](https://intersect.org.au/training/course/nvivo102).**
training@intersect.org.au
Data Management, NVivo
Learn to Program: Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results,...
Keywords: Programming, Python
Learn to Program: Python
https://intersect.org.au/training/course/python101
https://dresa.org.au/materials/learn-to-program-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the JupyterLab interface for programming
- Basic syntax and data types in Python
- How to load external data into Python
- Creating functions (FUNCTIONS)
- Repeating actions and anylsing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data in Python
#### Prerequisites:
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/python101).**
training@intersect.org.au
Programming, Python
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...
Keywords: Programming, Python
Python for Research
https://intersect.org.au/training/course/python110
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.
#### You'll learn:
- 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
#### Prerequisites:
[Learn to Program: Python](https://intersect.org.au/training/course/python101/) or any of the [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) or [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/), needed to attend this course. If you already have some experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) course to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/python110).**
training@intersect.org.au
Programming, 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 the...
Keywords: Programming, Python
Data Manipulation in Python
https://intersect.org.au/training/course/python201
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.
#### You'll learn:
- Working with pandas DataFrames
- Indexing, slicing and subsetting in pandas DataFrames
- Missing data values
- Combine multiple pandas DataFrames
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/python201).**
training@intersect.org.au
Programming, Python
Data Visualisation in Python
Course Materials
You'll learn:
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
Prerequisites:
Either [Learn to...
Keywords: Programming, Python
Data Visualisation in Python
https://intersect.org.au/training/course/python202
https://dresa.org.au/materials/data-visualisation-in-python
Course Materials
#### You'll learn:
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
We also strongly recommend attending the [Data Manipulation in Python](https://intersect.org.au/training/course/python201/).
**For more information, please click [here](https://intersect.org.au/training/course/python202).**
training@intersect.org.au
Programming, 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 the...
Keywords: Programming, Python
Data Manipulation and Visualisation in Python
https://intersect.org.au/training/course/python203
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.
#### You'll learn:
- Working with pandas DataFrames
- Indexing, slicing and subsetting in pandas DataFrames
- Missing data values
- Combine multiple pandas DataFrames
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/python203).**
training@intersect.org.au
Programming, 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: Programming, Python
Introduction to Machine Learning using Python: Introduction & Linear Regression
https://intersect.org.au/training/course/python205
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.
#### You'll learn:
- Understand the difference between supervised and unsupervised Machine Learning.
- Understand the fundamentals of Machine Learning.
- Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) 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.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python205).**
training@intersect.org.au
Programming, 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: Programming, Python
Introduction to Machine Learning using Python: Classification
https://intersect.org.au/training/course/python206
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.
#### You'll learn:
- Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) 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.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python206).**
training@intersect.org.au
Programming, 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: Programming, Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning
https://intersect.org.au/training/course/python207
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.
#### You'll learn:
- Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) 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.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python207).**
training@intersect.org.au
Programming, Python
Surveying with Qualtrics
Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?
Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow...
Keywords: Data Management, Qualtrics
Surveying with Qualtrics
https://intersect.org.au/training/course/qltrics101
https://dresa.org.au/materials/surveying-with-qualtrics
Needing to collect data from people in a structured and intuitive way? Have you thought about using Qualtrics?
Qualtrics in a powerful cloud-based survey tool, ideal for social scientists from all disciplines. This course will introduce the technical components of the whole research workflow from building a survey to analysing the results using Qualtrics. We will discover the numerous design elements available in order to get the most useful results and make life as easy as can be for your respondents.
If your institution has a licence to Qualtrics, then this course is right for you.
#### You'll learn:
- Format a sample survey using the Qualtrics online platform
- Configure the survey using a range of design features to improve user experience
- Decide which distribution channel is right for your needs
- Understand the available data analysis and export options in Qualtrics
#### Prerequisites:
You must have access to a Qualtrics instance, such as through your university license. Speak to your local university IT or Research Office for assistance in accessing the Qualtrics instance.
**For more information, please click [here](https://intersect.org.au/training/course/qltrics101).**
training@intersect.org.au
Data Management, Qualtrics
Learn to Program: R
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
But getting started with R can be challenging,...
Learn to Program: R
https://intersect.org.au/training/course/r101
https://dresa.org.au/materials/learn-to-program-r
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in.
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the RStudio interface for programming
- Basic syntax and data types in R
- How to load external data into R
- Creating functions (FUNCTIONS)
- Repeating actions and analysing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data in R
#### Prerequisites:
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/r101).**
training@intersect.org.au
Programming, R
R for Social Scientists
R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.
But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory...
R for Social Scientists
https://intersect.org.au/training/course/r103
https://dresa.org.au/materials/r-for-social-scientists
R is quickly gaining popularity as a programming language of choice for researchers. It has an excellent ecosystem including the powerful RStudio development environment.
But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Data Carpentry.
#### You'll learn:
- Basic syntax and data types in R
- RStudio interface
- How to import CSV files into R
- The structure of data frames
- A brief introduction to data wrangling and data transformation
- How to calculate summary statistics
- A brief introduction to visualise data
#### Prerequisites:
No prior experience with programming needed to attend this course.
**For more information, please click [here](https://intersect.org.au/training/course/r103).**
training@intersect.org.au
Programming, R
R for Research
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
This workshop is an introduction to data...
R for Research
https://intersect.org.au/training/course/r110
https://dresa.org.au/materials/r-for-research
R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework.
This workshop is an introduction to data structures (DataFrames) and visualisation (using the ggplot2 package) in R. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals.
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Project Management with RStudio
- Introduction to Data Structures in R
- Introduction to DataFrames in R
- Selecting values in DataFrames
- Quick introduction to Plotting using the ggplot2 package
#### Prerequisites:
[Learn to Program: R](https://intersect.org.au/training/course/r101/) or any of the [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/), [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/), needed to attend this course. If you already have some 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.
**For more information, please click [here](https://intersect.org.au/training/course/r110).**
training@intersect.org.au
Programming, R
Data Manipulation in R
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will learn how to manipulate, explore and get insights from...
Data Manipulation in R
https://intersect.org.au/training/course/r201
https://dresa.org.au/materials/data-manipulation-in-r
R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.
In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package).
We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly.
Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation.
#### You'll learn:
- DataFrame Manipulation using the dplyr package
- DataFrame Transformation using the tidyr package
#### Prerequisites:
Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) 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/) and [R for Research](https://intersect.org.au/training/course/r110/) courses to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/r201).**
training@intersect.org.au
Programming, R
Heurist Tutorials
A set of video tutorials with accompanying walkthroughs for building your first Heurist database and website. The first three tutorials show you how to get started in Heurist. The five subsequent tutorials introduce you to the five main menus in the Heurist interface.
Keywords: Heurist, Data management, Data visualisation, Digital Humanities, Databasing, website
Resource type: tutorial
Heurist Tutorials
https://heuristnetwork.org/tutorials
https://dresa.org.au/materials/heurist-tutorials
A set of video tutorials with accompanying walkthroughs for building your first Heurist database and website. The first three tutorials show you how to get started in Heurist. The five subsequent tutorials introduce you to the five main menus in the Heurist interface.
michael.falk@sydney.edu.au
Falk, Michael
Johnson, Ian
Osmakov, Artem
Heurist, Data management, Data visualisation, Digital Humanities, Databasing, website
mbr
phd
ecr
researcher
support
ARDC digital research capabilities and skills framework
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role...
Keywords: training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
ARDC digital research capabilities and skills framework
https://zenodo.org/record/6558642
https://dresa.org.au/materials/ardc-digital-research-capabilities-and-skills-framework-e0acf524-0666-466c-ac93-f13c133b03cf
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role profiles
Learning paths
Skills/Data roles matrix
contact@ardc.edu.au
ARDC
Savill, Jo (type: Editor)
Duncan, Ian (type: Editor)
Unsworth, Kathryn (type: Editor)
Murphy, Paul (type: Editor)
training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
ARDC digital research capabilities and skills framework
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role...
Keywords: training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
ARDC digital research capabilities and skills framework
https://zenodo.org/record/6540798
https://dresa.org.au/materials/ardc-digital-research-capabilities-and-skills-framework
This informational flyer outlines the value of skills frameworks and describes at a high level the various elements of the ARDC's Capabilities and Skills Framework.
Capabilities and Skills Landscape
Glossary - Framework terminology
Data and Digital Research roles
Skills/Role profiles
Learning paths
Skills/Data roles matrix
Kathryn Unsworth (kathryn.unsworth@ardc.edu.au)
ARDC
Savill, Jo (type: Editor)
Duncan, Ian (type: Editor)
Unsworth, Kathryn (type: Editor)
Murphy, Paul (type: Editor)
training material, skills framework, ARDC skills framework, ARDC capabilities framework, national skills framework
WEBINAR: Protection of genomic data and the Australian Privacy Act: when is genomic data 'personal information'?
This record includes training materials associated with the Australian BioCommons webinar ‘Protection of genomic data and the Australian Privacy Act: when is genomic data ‘personal information’?’. This webinar took place on 6 April 2022.
Event description
It is easy to assume that...
Keywords: Bioinformatics, Genomics, Genetic data, Personal information, Health information, Privacy
WEBINAR: Protection of genomic data and the Australian Privacy Act: when is genomic data 'personal information'?
https://zenodo.org/record/6423621
https://dresa.org.au/materials/webinar-protection-of-genomic-data-and-the-australian-privacy-act-when-is-genomic-data-personal-information
This record includes training materials associated with the Australian BioCommons webinar ‘Protection of genomic data and the Australian Privacy Act: when is genomic data ‘personal information’?’. This webinar took place on 6 April 2022.
**Event description**
It is easy to assume that genomic data will be captured by legal definitions of ‘health information’ and ‘genetic information’, but the legal meaning of ‘genetic information’ need not align with scientific categories.
There are many different types of genomic data, with varied characteristics, uses and applications. Clarifying when genomic data is covered by the Privacy Act 1988 (Cth) is an ongoing evaluative exercise but is important for at least 3 reasons:
1. those subject to the Privacy Act need to be able to confidently navigate their responsibilities
2. understanding current controls is a prerequisite for meaningful external critique (and this is particularly important at a time when the Privacy Act is under review), and
3. while legislation that applies to state public sector agencies is generally distinct from the Privacy Act there are similarities that extend the relevance of the question when is genomic data ‘personal information’ under the Privacy Act?
In this presentation, Mark will explore the relationship between the legal concept of genetic information and the concept of genomic data relevant to health and medical research, reflect on the characteristics of each, and the possibility
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.
- Taylor_Slides (PDF): A PDF copy of the slides presented during the webinar.
**Materials shared elsewhere:**
A recording of this webinar is available on the Australian BioCommons YouTube Channel:
https://youtu.be/Iaei-9Gu-AI
Melissa Burke (melissa@biocommons.org.au)
Taylor, Mark (orcid: 0000-0003-2009-6284)
Bioinformatics, Genomics, Genetic data, Personal information, Health information, Privacy