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This repository contains an evaluation of QLSC612 Course Material on a 1-5 rating scale randomly generated from a hundred students. Below are the course materials that were evaluated.

With this lecture, you will get a general introduction to reproducible - or irreproducible - life sciences

A link to the training material

Basic Statistics

90 mins

No

PDF Slides

In this module we’ll take a look at the the BourneAgainSHell (BASH), the default command line in most Linux systems.

A link to the training material

Bash Shell

90 mins

Yes

Video

This lecture is designed to get students up and running with Python. It is expected that Python 3 (preferably 3.7 or later) is installed, and that the students have some basic previous experience in a scripting language. It will guide students through the fundamental syntax, concepts, and data structures required to code in Python 3. Topics include: Running your code, commenting, variables, arithmetic, logic, strings, lists, tuples, dictionaries, functions, libraries, if statements, loops, exceptions, and classes.

A link to the training material

Python Environment

90 mins

Yes

Jupiter Notebook

This lecture will introduce NumPy and its ndarray data structure, which are at the core of most scientific Python packages. At the end of the lecture, participants will be able to: Understand why NumPy enables efficient computation and what are NumPy arrays. Manipulate arrays of numbers with NumPy

A link to the training material

Python Environment

90 mins

Yes

Slides

Link

• Python environment
• numpy

~90 mins

yes

Web browser

Link

• Python environment
• Numpy

< 2h

No

Web browser

Link

• Python environment
• Numpy

90 min

Premium feature

Video

Git and GitHub are key tools for doing version control in both academia and industry. These tools can help students do more effient, open, and reproducible research. Further, knowing these tools can help prepare students for careers in academia and industry. In this lecture, students will learn What is version control and why has it become so important in science and industry; How to track and share their own work using Git and GitHub; and How to collaborate and contribute to open projects using Git and GitHub.

A link to the training material

Basic Programming

75 mins

Yes

Video

This module is designed to introduce students to the Pandas Python library for manipulating data in tables and time series (not to be confused with the bear of the same name). It aims to build a basic understanding of what happens underneath the hood in Pandas, and arm you with the essential practical knowledge to fearlessly tackle the next CSV file you encounter in the wild.

A link to the training material

Python Environment

90 mins

Yes

Jupiter Notebook

Most of published results still rely on some statistical inference. With this lecture, you will get a reminder of the classical statistical framework and learn about the issues brought by the use of statistical inference learn (or be reminded of) the notion of effect size, power, positive predictive values and the consequences of low powered studies and understand the file drawer effect, p-hacking, and know about some solutions.

A link to the training material

Basic Statistics

60 mins

Yes

Slides

Students will learn how to: Define machine-learning nomenclature Describe basics of the “learning” process Explain model design choices and performance trade-offs Introduce model selection and validation frameworks Explain model performance metrics

A link to the training material

Python Environment

90 mins

Yes

Jupiter Notebook

Students will learn how to: Learn how to properly select a machine-learning model, set hyperparameters, and evaluate prediction performance. Understand the challenges of learning from high-dimensional data and learn about tools to mitigate the issue.

A link to the training material

Python Environment

90 mins

Yes

Slides

Data visualization is an essential skill for scientists. At the grad student level, you’re probably already familiar with basic plots (e.g., bar plot vs pie chart), as well as types of data (e.g, ordered vs categorical).

A link to the training material

Python Environment

90 mins

Yes

Slides

Learn why containerization and virtualization are important for research projects. Have an overview of different solutions to create isolated environments. Get some basic hands on experience with and Docker.

A link to the training material

Python Environment

90 mins

Yes

Slides

Learn why containerization and virtualization are important for research projects. Have an overview of different solutions to create isolated environments. Get some basic hands on experience with and Docker.

A link to the training material

Docker

105 mins

Yes

Slides

Learn the key facts about High Performance Computing (HPC) and Cloud computing Understand the advantages and the constraints of HPC Learn the key concepts and practical bash commands to get started on the Compute Canada HPC

A link to the training material

Python Environment

90 mins

Yes

Slides