Author: Richard Foltyn, University of Glasgow
This introductory course consists of several units. Each unit corresponds to one interactive Jupyter notebook, which is also available as a static PDF file. Alternatively, you can download the entire course as a single PDF.
| Unit | Title | Google Colab | |
|---|---|---|---|
| 1 | Language and NumPy basics |  |
|
| 2 | Control flow and list comprehensions | |
|
| 3 | Reusing code - Functions, modules and packages | |
|
| 4 | Plotting | |
|
| 5 | Advanced NumPy | |
|
| 6 | Handling data with pandas | |
|
| 7 | Data input and output | |
|
| 8 | Random number generation and statistics | |
|
| 9 | Introduction to unsupervised learning | |
|
| 10 | Introduction to supervised learning | |
|
| 11 | Error handling (optional) | |
Detailed slides on how to set up your working environment are available here.
This material is licensed under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License,
except for the data files contained in the data/ folder, which
fall under the terms imposed by the original content creators.
Special thanks go to Jonna Olsson for reading through all units and suggesting various improvements.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent