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48 materials found

Keywords: FAIR  or Programming 


WORKSHOP: Make your bioinformatics workflows findable and citable

This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023.

Event description

Computational workflows are invaluable resources for research communities. They help...

Keywords: Bioinformatics, Workflows, WorkflowHub, FAIR, Open Science

WORKSHOP: Make your bioinformatics workflows findable and citable https://dresa.org.au/materials/workshop-make-your-bioinformatics-workflows-findable-and-citable-74e85d1c-d869-429e-b942-8391f4bab23d This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023. Event description Computational workflows are invaluable resources for research communities. They help us  standardise common analyses, collaborate with other researchers, and support reproducibility. Bioinformatics workflow developers invest significant time and expertise to create, share, and maintain these resources for the benefit of the wider community and being able to easily find and access workflows is an essential factor in their uptake by the community. Increasingly, the research community is turning to workflow registries to find and access public workflows that can be applied to their research. Workflow registries support workflow findability and citation by providing a central repository and allowing users to search for and discover them easily. This workshop will introduce you to workflow registries and support attendees to register their workflows on the popular workflow registry, WorkflowHub. We’ll kick off the workshop with an introduction to the concepts underlying workflow findability, how it can benefit workflow developers, and how you can make the most of workflow registries to share your computational workflows with the research community. You will then have the opportunity to register your own workflows in WorkflowHub with support from our trainers.  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. 2023-03-21_Workflows_slides (PDF): A copy of the slides presented during the workshop Materials shared elsewhere: A recording of the first part of this workshop is available on the Australian BioCommons YouTube Channel: https://youtu.be/2kGKxaPuQN8 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, WorkflowHub, FAIR, Open Science
WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia

This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022.

Event description 

Have you discovered a brilliant...

Keywords: Bioinformatics, Workflows, FAIR, Galaxy Australia

WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia https://dresa.org.au/materials/webinar-here-s-one-we-prepared-earlier-re-creating-bioinformatics-methods-and-workflows-with-galaxy-australia-134a8bf5-3801-421f-a454-e0f9020f4871 This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022. Event description  Have you discovered a brilliant bioinformatics workflow but you’re not quite sure how to use it? In this webinar we will introduce the power of Galaxy for construction and (re)use of reproducible workflows, whether building workflows from scratch, recreating them from published descriptions and/or extracting from Galaxy histories. Using an established bioinformatics method, we’ll show you how to: Use the workflows creator in Galaxy Australia  Build a workflow based on a published method Annotate workflows so that you (and others) can understand them  Make workflows finable and citable (important and very easy to do!) 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. GalaxyWorkflows_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/IMkl6p7hkho Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, FAIR, Galaxy Australia
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description 

bio.tools provides easy access to essential scientific...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software-aea38c9e-0b40-4308-bafd-f7580563f520 This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. Event description  bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   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. biotools_EDAM_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/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Research Data Governance

This video contains key information for those who make research data-related decisions. It will help project leaders to start investigating ways to develop their own data governance policy, roles and responsibilities and procedures with the input of appropriate stakeholders.

If you want to share...

Keywords: data governance, data, research, FAIR, data management, authority, share, reuse, access, provenance, policy, responsibilities, ARDC_AU, training material

Research Data Governance https://dresa.org.au/materials/research-data-governance-6ad9ab90-1a29-41db-b4aa-f1988501530d This video contains key information for those who make research data-related decisions. It will help project leaders to start investigating ways to develop their own data governance policy, roles and responsibilities and procedures with the input of appropriate stakeholders. If you want to share the video please use this: Australian Research Data Commons, 2021. Research Data Governance. [video] Available at: https://youtu.be/K_xVQRdgCIc  DOI: http://doi.org/10.5281/zenodo.5044585 [Accessed dd Month YYYY]. contact@ardc.edu.au Martinez, Paula Andrea (type: ProjectLeader) Wilkinson, Max (type: Editor) Callaghan,Shannon (type: Editor) Savill, Jo (type: Editor) Kang, Kristan (type: Editor) Levett, Kerry (type: Editor) Russell, Keith (type: Editor) Simons, Natasha (type: Editor) data governance, data, research, FAIR, data management, authority, share, reuse, access, provenance, policy, responsibilities, ARDC_AU, training material
ARDC Skills Landscape

The Australian Research Data Commons is driving transformational change in the research data ecosystem, enabling researchers to conduct world class data-intensive research. One interconnected component of this ecosystem is skills development/uplift, which is critical to the Commons and its...

Keywords: skills, data skills, eresearch skills, community, skilled workforce, FAIR, research data management, data stewardship, data governance, data use, data generation, training material

ARDC Skills Landscape https://dresa.org.au/materials/ardc-skills-landscape-56b224ca-9e30-4771-8615-d028c7be86a6 The Australian Research Data Commons is driving transformational change in the research data ecosystem, enabling researchers to conduct world class data-intensive research. One interconnected component of this ecosystem is skills development/uplift, which is critical to the Commons and its purpose of providing Australian researchers with a competitive advantage through data.   In this presentation, Kathryn Unsworth introduces the ARDC Skills Landscape. The Landscape is a first step in developing a national skills framework to enable a coordinated and cohesive approach to skills development across the Australian eResearch sector. It is also a first step towards helping to analyse current approaches in data training to identify: - Siloed skills initiatives, and finding ways to build partnerships and improve collaboration - Skills deficits, and working to address the gaps in data skills - Areas of skills development for investment by skills stakeholders like universities, research organisations, skills and training service providers, ARDC, etc.   contact@ardc.edu.au skills, data skills, eresearch skills, community, skilled workforce, FAIR, research data management, data stewardship, data governance, data use, data generation, training material
ARDC Your first step to FAIR

This workshop gives a brief overview of the FAIR principles, including a method to make a one-file dataset FAIR.

Keywords: training material, FAIR, data, workshop

ARDC Your first step to FAIR https://dresa.org.au/materials/ardc-your-first-step-to-fair-1ee3dc3c-23b0-4287-b96c-c120c5697932 This workshop gives a brief overview of the FAIR principles, including a method to make a one-file dataset FAIR. contact@ardc.edu.au Stokes, Liz (type: Editor) Martinez, Paula Andrea (type: Editor) Russell, Keith (type: Editor) training material, FAIR, data, workshop
ARDC Training Materials Metadata Checklist v1.1

The ARDC Training Materials Metadata Checklist aims to support learning designers, training materials creators, trainers and national training infrastructure providers to capture key information and apply appropriate mechanisms to enable sharing and reuse of their training materials

Keywords: checklist, Training material, FAIR, standard, requirements, metadata

ARDC Training Materials Metadata Checklist v1.1 https://dresa.org.au/materials/ardc-training-materials-metadata-checklist-v1-1 The ARDC Training Materials Metadata Checklist aims to support learning designers, training materials creators, trainers and national training infrastructure providers to capture key information and apply appropriate mechanisms to enable sharing and reuse of their training materials contact@ardc.edu.au checklist, Training material, FAIR, standard, requirements, metadata
Locking the front door without leaving the windows open: positioning authentication technologies within the "Five Safes" framework for effective use of sensitive research data

This project explores the options for access to sensitive data sets; what authentication technologies (e.g. multi-factor authentication) are needed to access sensitive data and secure compute environments.  This project seeks to position choices around authentication technologies within the Five...

Keywords: ARDC, Storage and Compute Summit, FAIR, Infrastructure, NCRIS, eResearch, training material

Locking the front door without leaving the windows open: positioning authentication technologies within the "Five Safes" framework for effective use of sensitive research data https://dresa.org.au/materials/locking-the-front-door-without-leaving-the-windows-open-positioning-authentication-technologies-within-the-five-safes-framework-for-effective-use-of-sensitive-research-data-b83124f8-2add-41c6-b194-d5dd50d098f6 This project explores the options for access to sensitive data sets; what authentication technologies (e.g. multi-factor authentication) are needed to access sensitive data and secure compute environments.  This project seeks to position choices around authentication technologies within the Five Safes framework for research use of sensitive data, proposed in 2003 by Felix Ritchie of the UK Office of National Statistics: • Safe Projects: is the proposed research use of the data appropriate?  • Safe People: can the users be trusted to use the data in an appropriate manner?  • Safe Settings: does the access facility limit unauthorised use? • Safe Data: is there a disclosure risk in the data itself? • Safe Outputs: are the research results non-disclosive i.e. they do not compromise privacy or breach confidentiality? contact@ardc.edu.au ARDC, Storage and Compute Summit, FAIR, Infrastructure, NCRIS, eResearch, training material
ARDC FAIR Data 101 self-guided

FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles

The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course.

The course structure was based on 'FAIR Data in the...

Keywords: training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management

ARDC FAIR Data 101 self-guided https://dresa.org.au/materials/ardc-fair-data-101-self-guided-2d794a84-f0ff-4e11-a39c-fa8ea481e097 FAIR Data 101 v3.0 is a self-guided course covering the FAIR Data principles The FAIR Data 101 virtual course was designed and delivered by the ARDC Skilled Workforce Program twice in 2020 and has now been reworked as a self-guided course. The course structure was based on 'FAIR Data in the Scholarly Communications Lifecycle', run by Natasha Simons at the FORCE11 Scholarly Communications Institute. These training materials are hosted on GitHub. contact@ardc.edu.au training material, FAIR data, video, webinar, activities, quiz, FAIR, research data management
HIP Workshop

The Heterogeneous Interface for Portability (HIP) provides a programming framework for harnessing the compute capabilities of multicore processors, such as the MI250X GPU’s on Setonix.

In this course we focus on the essentials of developing HIP applications with a focus on...

Keywords: HIP, supercomputing, Programming, GPUs, MPI, debugging

Resource type: full-course

HIP Workshop https://dresa.org.au/materials/hip-workshop The Heterogeneous Interface for Portability (HIP) provides a programming framework for harnessing the compute capabilities of multicore processors, such as the MI250X GPU’s on Setonix. In this course we focus on the essentials of developing HIP applications with a focus on supercomputing. Agenda - Introduction to HIP and high level features - How to build and run applications on Setonix with HIP and MPI - A complete line-by-line walkthrough of a HIP-enabled application - Tools and techniques for debugging and measuring the performance of HIP applications training@pawsey.org.au HIP, supercomputing, Programming, GPUs, MPI, debugging
C/C++ Refresher

The C++ programming language and its C subset is used extensively in research environments. In particular it is the language utilised in the parallel programming frameworks CUDA, HIP, and OpenCL.

This workshop is designed to equip participants with “Survival C++”, an understanding of the basic...

Keywords: supercomputing, C/C++, Programming

Resource type: activity

C/C++ Refresher https://dresa.org.au/materials/c-c-refresher The C++ programming language and its C subset is used extensively in research environments. In particular it is the language utilised in the parallel programming frameworks CUDA, HIP, and OpenCL. This workshop is designed to equip participants with “Survival C++”, an understanding of the basic syntax, how information is encoded in binary format, and how to compile and debug C++ software. training@pawsey.org.au supercomputing, C/C++, Programming
WORKSHOP: Make your bioinformatics workflows findable and citable

This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023.

Event description

Computational workflows are invaluable resources for research communities. They help...

Keywords: Bioinformatics, Workflows, WorkflowHub, FAIR, Open Science

WORKSHOP: Make your bioinformatics workflows findable and citable https://dresa.org.au/materials/workshop-make-your-bioinformatics-workflows-findable-and-citable This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023. Event description Computational workflows are invaluable resources for research communities. They help us  standardise common analyses, collaborate with other researchers, and support reproducibility. Bioinformatics workflow developers invest significant time and expertise to create, share, and maintain these resources for the benefit of the wider community and being able to easily find and access workflows is an essential factor in their uptake by the community. Increasingly, the research community is turning to workflow registries to find and access public workflows that can be applied to their research. Workflow registries support workflow findability and citation by providing a central repository and allowing users to search for and discover them easily. This workshop will introduce you to workflow registries and support attendees to register their workflows on the popular workflow registry, WorkflowHub. We’ll kick off the workshop with an introduction to the concepts underlying workflow findability, how it can benefit workflow developers, and how you can make the most of workflow registries to share your computational workflows with the research community. You will then have the opportunity to register your own workflows in WorkflowHub with support from our trainers.  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. 2023-03-21_Workflows_slides (PDF): A copy of the slides presented during the workshop Materials shared elsewhere: A recording of the first part of this workshop is available on the Australian BioCommons YouTube Channel: https://youtu.be/2kGKxaPuQN8 Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, WorkflowHub, FAIR, Open Science
WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia

This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022.

Event description 

Have you discovered a...

Keywords: Bioinformatics, Workflows, FAIR, Galaxy Australia

WEBINAR: Here's one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia https://dresa.org.au/materials/webinar-here-s-one-we-prepared-earlier-re-creating-bioinformatics-methods-and-workflows-with-galaxy-australia This record includes training materials associated with the Australian BioCommons webinar ‘Here’s one we prepared earlier: (re)creating bioinformatics methods and workflows with Galaxy Australia’. This webinar took place on 26 October 2022. **Event description**  Have you discovered a brilliant bioinformatics workflow but you’re not quite sure how to use it? In this webinar we will introduce the power of Galaxy for construction and (re)use of reproducible workflows, whether building workflows from scratch, recreating them from published descriptions and/or extracting from Galaxy histories. Using an established bioinformatics method, we’ll show you how to: * Use the workflows creator in Galaxy Australia  * Build a workflow based on a published method * Annotate workflows so that you (and others) can understand them  * Make workflows finable and citable (important and very easy to do!) 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. * GalaxyWorkflows_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/IMkl6p7hkho Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Workflows, FAIR, Galaxy Australia
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description

bio.tools provides easy access to essential...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software-9180e32a-f4f5-4993-a90a-a9bfcfafd4f3 This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. **Event description** bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   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. * biotools_EDAM_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/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Programming and tidy data analysis in R

A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps...

Keywords: R, Tidyverse, Programming

Resource type: tutorial

Programming and tidy data analysis in R https://dresa.org.au/materials/programming-and-tidy-data-analysis-in-r A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps needed to load data and convert it into a tidy form. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R, Tidyverse, Programming phd ecr researcher
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description

bio.tools provides easy access to essential...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. **Event description** bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   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. - biotools_EDAM_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/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Data Visualisation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will explore different types of graphs and learn how to...

Keywords: Programming, R

Data Visualisation in R https://dresa.org.au/materials/data-visualisation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: - Using the Grammar of Graphics to convert data into figures using the ggplot2 package - Configuring plot elements within ggplot2 - Exploring different types of plots using ggplot2 #### Prerequisites: Either [Learn to Program: R](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. We also strongly recommend attending the [Data Manipulation in R](https://intersect.org.au/training/course/r201/) course. **For more information, please click [here](https://intersect.org.au/training/course/r202).** training@intersect.org.au Programming, R
Data Manipulation and Visualisation in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

In this workshop, you will learn how to manipulate, explore and get insights from...

Keywords: Programming, R

Data Manipulation and Visualisation in R https://dresa.org.au/materials/data-manipulation-and-visualisation-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: - DataFrame Manipulation using the dplyr package - DataFrame Transformation using the tidyr package - Using the Grammar of Graphics to convert data into figures using the ggplot2 package - Configuring plot elements within ggplot2 - Exploring different types of plots using ggplot2 #### Prerequisites: Either [Learn to Program: R](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/r203).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: Introduction & Linear Regression

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...

Keywords: Programming, R

Introduction to Machine Learning using R: Introduction & Linear Regression https://dresa.org.au/materials/introduction-to-machine-learning-using-r-introduction-linear-regression Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### 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 R and and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### 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 R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r205).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: Classification

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...

Keywords: Programming, R

Introduction to Machine Learning using R: Classification https://dresa.org.au/materials/introduction-to-machine-learning-using-r-classification Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### 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 R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### 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 R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r206).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: SVM & Unsupervised Learning

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...

Keywords: Programming, R

Introduction to Machine Learning using R: SVM & Unsupervised Learning https://dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### 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 R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r207).** training@intersect.org.au Programming, R
Exploring Chi-square and correlation in R

This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures...

Keywords: Programming, R

Exploring Chi-square and correlation in R https://dresa.org.au/materials/exploring-chi-square-and-correlation-in-r This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson's r, Spearman's Rho and Kendall’s tau) in real world datasets. #### You'll learn: - Obtain inferential statistics and assess data normality - Manipulate data and create graphs - Perform Chi-Square tests (Goodness of Fit test and Test of Independence) - Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r210).** training@intersect.org.au Programming, R
Traversing t tests in R

R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

The primary goal of this workshop is to familiarise you with basic statistical concepts in R from...

Keywords: Programming, R

Traversing t tests in R https://dresa.org.au/materials/traversing-t-tests-in-r R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** training@intersect.org.au Programming, R
Exploring ANOVAs in R

R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework.

This half-day course covers one and two-way Analyses of Variance (ANOVA) and their...

Keywords: Programming, R

Exploring ANOVAs in R https://dresa.org.au/materials/exploring-anovas-in-r R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R. To better understand the tests, assumptions and associated concepts, we will be using a dataset containing the Mathematics scores of secondary students. This dataset also includes information regarding their mother's and father's jobs and education levels, the number of hours dedicated to study, and time spent commuting to and from school. Lifestyle information about alcohol consumption habits, whether the students have quality relationships with their families and whether they have free time after school is included in this dataset. #### You'll learn: - Basic statistical theory behind ANOVAs - How to check that the data meets the assumptions - One-way ANOVA in R and post-hoc analysis - Two-way ANOVA plus interaction effects and post-hoc analysis - Non-parametric alternatives to one and two-way ANOVA #### Prerequisites: This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required. If you’re new to programming in R, we strongly recommend you register for the [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) workshops first. **For more information, please click [here](https://intersect.org.au/training/course/r212).** training@intersect.org.au Programming, R
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: Programming

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. #### You'll learn: - How a human solves tasks - How a computer solves tasks - Overview of programming concepts: - Variables - Loops - Conditionals - Functions - Data types #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/coding003).** training@intersect.org.au Programming
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://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://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://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: In order to participate, attendees must have a licensed copy of MATLAB installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](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
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://dresa.org.au/materials/learn-to-program-python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Python - How to load external data into Python - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/python101).** training@intersect.org.au Programming, Python
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://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