$25 – $30

Women in Science and Engineering: Coding 101 Workshop

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Location

Memorial University of Newfoundland Engineering Building

40 Arctic Avenue

EN2100, EN2116

St. John's, NL A1B 3X7

Canada

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Refund Policy

Refund Policy

Refunds up to 1 day before event

Event description

Description

Workshop Structure:

This two-day workshop will offer two concurrent streams: (1) The Technical Stream will focus on computational tools and skills more suited to those in technical fields, including Unix Shell, Git, and Reproducible Research in Python. (2) The Analytical Stream will focus on computational tools and skills more suited to those in analytical fields, including Data Organization, OpenRefine, and Reproducible Research in R/RStudio. See the descriptions of each lesson, below.

Participants are asked to purchase tickets for one stream to guarantee their seat in that stream but can contact the workshop facilitators (see Workshop Websites, below) if they are interested in attending particular units from the alternative stream (see Schedule, below).

If you have any questions, please contact one of the workshop facilitators:

Oihane: occ356@mun.ca

Danielle: danielle.quinn@mun.ca

Target Audience:

Women in any field of research or industry who would benefit from learning how to code, including graduate students, researchers, faculty, and industry professionals from Memorial University or the greater St. John's community. These workshops will cover introductory material from The Carpentries curriculum designed for those brand new to programming but may be suitable for those looking to expand upon existing coding skills by learning a new programming language.

What does the cost of this workshop cover?

The $30 workshop fee, in addition to contributions from WISE NL and WISE GSS, will cover the two-day workshop, two coffee/snack breaks per day, and lunch for both days (if you have any special dietary requirements or allergies, please email the workshop facilitators). Graduate students who are members of WISE GSS are eligible to receive a $5 discount! If you are unsure if you are a WISE GSS member, please contact one of the workshop administrators, listed above.

What should I do to prepare?

Each student should bring their own laptop, with administrative rights and the appropriate software installed. For detailed information regarding set-up, please refer to the Workshop Websites, below. Participants will receive additional information by email regarding pre-workshop help hours for any installation issues that may arise.

Workshop Websites:

For more information, please visit the workshop websites, below.

Technical Stream: https://daniellequinn.github.io/2019-03-30-WISE1/

Analytical Stream: https://daniellequinn.github.io/2019-03-30-WISE2/

Description of Lessons:

Technical Stream

Automating Tasks with Unix Shell

The Unix shell has been around longer than most of its users have been alive. It has survived so long because it’s a powerful tool that allows people to do complex things with just a few keystrokes. More importantly, it helps users combine existing programs in new ways and automate repetitive tasks. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including “high-performance computing” supercomputers). This introduction will cover foundational shell commands and teach users about navigating through and working with files and directories, creating workflows, and writing script files.

Version Control with Git / GitHub

Version control is the lab notebook of the digital world: it’s what professionals use to keep track of what they’ve done and to collaborate with other people. Every large software development project relies on it, and most programmers use it for their small jobs as well. And it isn’t just for software: books, papers, small data sets, and anything that changes over time or needs to be shared can and should be stored in a version control system. This lesson serves as an introduction to version control using Git, including how to create a repository, track and explore changes in files, and share changes through a remote repository on GitHub.

Introduction to Python

Python is a high-level, general-purpose programming language with a syntax that emphasizes readability. It is used for a wide variety of tasks, including data manipulation, analysis, and visualization. This is an introduction to Python designed for participants with no programming experience. This lesson starts with basic information about Python syntax and the Jupyter notebook interface, introduces key concepts like libraries, functions, and data structure, then guides learners through the process of importing and exploring data.

Python for Reproducible Scientific Analysis

This lesson will cover more advanced programming techniques, including building functions, analyzing data, and generating plots in Python.

Analytical Stream

Data Organization

Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. To use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. This lesson covers good data entry practices, how to avoid common formatting mistakes, approaches for handling dates in spreadsheets, basic quality control techniques, and exporting data into universal file formats.

OpenRefine

OpenRefine is a powerful open-source tool for working with messy data and is used to clean and transform datasets in a diligent, reproducible way.

Introduction to R and RStudio

R is a programming language and environment used for a wide variety of tasks, including data manipulation, analysis, and visualization. This is an introduction to R designed for participants with no programming experience. This lesson walks learners through the RStudio interface and importing, exploring, and manipulating data frames, and introduces key programming concepts like functions, arguments, objects, and data classes.

R for Reproducible Scientific Analysis

This lesson provides learners with an introduction to some of the most versatile R packages, including {ggplot2}, {dplyr}, {tidyr} and {lubridate}. These packages work in harmony to clean, process, model, and visualize data in a reproducible way.

Schedule

Technical Stream

Day One

8:30 am - 9 am: Software Installation Troubleshooting

9 am - 12 pm: Automating Tasks with Unix Shell

12 pm - 1 pm: Lunch* (included)

1 pm - 4:30 pm: Version Control with Git / GitHub

Day Two

8:30 am - 9 am: Software Installation Troubleshooting

9 am - 12 pm: Introduction to Python

12 pm - 1 pm: Lunch* (included)

1 pm - 4:30 pm: Reproducible Research with Python

4:30 pm - 5 pm: Workshop Feedback and Closing

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Analytical Stream

Day One

8:30 am - 9 am: Software Installation Troubleshooting

9 am - 12 pm: Data Organization in Spreadsheets

12 pm - 1 pm: Lunch* (included)

1 pm - 4:30 pm: Data Management with OpenRefine

Day Two

8:30 am - 9 am: Software Installation Troubleshooting

9 am - 12 pm: Introduction to R and RStudio

12 pm - 1 pm: Lunch* (included)

1 pm - 4:30 pm: Reproducible Research with R and RStudio

4:30 pm - 5 pm: Workshop Feedback and Closing

*if you have any special dietary requirements or allergies, please email the workshop facilitators

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Date and Time

Location

Memorial University of Newfoundland Engineering Building

40 Arctic Avenue

EN2100, EN2116

St. John's, NL A1B 3X7

Canada

View Map

Refund Policy

Refunds up to 1 day before event

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