This is a repository for materials used for a module on quantitative research resources, taught at Glasgow International College at The University of Glasgow.

Python is a powerful programming language used for many different applications. But a huge community around this open source language has created quite a few tools to efficiently work with Python for Data Science. Python is becoming the laguage of choice of anyone interested in analysing data.

In this crash course, we start from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. We'll learn about Python functions and control flow. Plus, we'll look at the world of data visualizations with Python and create our own stunning visualizations based on real data.

If you have access to a Google Account:

1. Login into your Google Account.

2. Go to this link, go to Tutorial and follow the instructions.

If you don't, you can copy this repo in your local environment by following this steps:

1. Download this repo as a zip file and unzip it.

2. You will need to install and setup Python. To install and setup for Mac and Windows, you can follow this tutorial..

3. To open the folder with the Workshop and exercise notebooks, you will need an IDE (Integrated Development Environment). There are many popular IDE environments for Python development. I use Visual Studio Code. If you use Windows, you can follow the instructions on how to install VS Code in your laptop or PC on this link.. Or if you use a Mac, you can follow this link instead..

You also need to install the Jupyter Extension

4. Complete the Workshops and Exercises in order.

5. E-portfolio Evidence: Just focus on doing the exercises notebooks and finish as many as you possibly can. Then, write a short 500-1000 word reflection on the course, what you learned, if you finish all exercises, how was it, if you didn’t, why? And this, accompanied by screenshots of your local setup with the exercises. Or, links to the google colab notebooks if you have access to google.

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