course material for LV581092
I assume the following
You finished the following DataCamp courses before the start of the BIPM program:
- Intro to Python for Data Science https://www.datacamp.com/courses/intro-to-python-for-data-science
- Intermediate Python for Data Science https://www.datacamp.com/courses/intermediate-python-for-data-science
- Python Data Science Toolbox (Part 1) https://www.datacamp.com/courses/python-data-science-toolbox-part-1
- Python Data Science Toolbox (Part 2) https://www.datacamp.com/courses/python-data-science-toolbox-part-2
- Statistical Thinking in Python (Part 1) https://www.datacamp.com/courses/statistical-thinking-in-python-part-1
- Statistical Thinking in Python (Part 2) https://www.datacamp.com/courses/statistical-thinking-in-python-part-2
Your only excuse to skip courses is if you think you already know the content.
In addition, you might want to take the following courses:
https://www.datacamp.com/courses/supervised-learning-with-scikit-learn
And these ressources are very useful:
RISE: "instantly turn your Jupyter Notebooks into a slideshow. No out-of-band conversion is needed, switch from jupyter notebook to a live reveal.js-based slideshow in a single keystroke, and back." https://github.com/damianavila/RISE
example: http://www.slideviper.oquanta.info/tutorial/slideshow_tutorial_slides.html#/
Free Jupyter Cloud Notebooks on Azure: https://notebooks.azure.com/
and from google:https://colab.research.google.com/
Statistics in Python:http://www.scipy-lectures.org/packages/statistics/index.html
books as free PDFs with Code:
Downey, A. B. (2015). Think Python (2nd ed.). Sebastopol, CA: O’Reilly (free PDF): https://greenteapress.com/wp/think-python-2e/
Downey, A. B. (2014). Think Stats (2nd ed.). Sebastopol, CA: O’Reilly (free PDF):https://greenteapress.com/wp/think-stats-2e/
Raschka book: https://github.com/rasbt/python-machine-learning-book-2nd-edition
An Introduction to Statistical Learning in R
https://github.com/JWarmenhoven/ISLR-python