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 |  |
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| 2 | Control flow and list comprehensions | |
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| 3 | Reusing code - Functions, modules and packages | |
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| 4 | Plotting | |
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| 5 | Advanced NumPy | |
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| 6 | Handling data with pandas | |
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| 7 | Data input and output | |
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| 8 | Random number generation and statistics | |
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| 9 | Introduction to unsupervised learning | |
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| 10 | Introduction to supervised learning | |
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| 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.