Related Readings

Not all of these will be directly applicable to this course, but all will help you be a better coder and/or think better quantitatively.

Computational and Inferential Thinking

Ani Adhikari and John DeNero

A free, online, interactive textbook was developed at Berkeley for their introductory data science course, which also uses Python and is based heavily on Jupyter Notebook. You can read the text here.

Thinking: Fast and Slow

Daniel Kahneman

Kahneman explores the psychological aspects of statistical thinking, and explains a variety of cognitive biases that may arise when we estimate.

For example, humans are better at averaging than summing. Implication: if you're trying to convince somebody of something, it's 3 good arguments alone are better than 3 good arguments plus 2 mediocre arguments (the latter increases the sum but drops the average). Fast+Slow has many such examples that will help you be more persuasive and avoid making common cognitive mistakes about probabality.

goodreads>>

PyMOTW-3

Python Module of the Week is a blog that covers various interesting Python libraries. Past posts are described at https://pymotw.com/3/.