(DataCamp) Statistical Thinking in Python (Part 1)
This is a memo to share what I have learnt in Statistical Thinking in Python (Part 1), capturing the learning objectives as well as my personal notes. The course is taught by Justin Bois from DataCamp, and it includes 4 chapters:
Chapter 1. Graphical exploratory data analysis
Chapter 2. Quantitative exploratory data analysis
Chapter 3. Thinking probabilistically – Discrete variables
Chapter 4. Thinking probabilistically – Continuous variables
Personal Notes:
https://towardsdatascience.com/statistical-thinking-in-python-part-1-58b5ae8c0f6f
(DataCamp) Statistical Thinking in Python (Part 2)
This is a memo to document what I have learnt in Statistical Thinking in Python (Part 2), capturing the learning objectives as well as my personal notes. The course is taught by Justin Bois from DataCamp, and it includes 5 chapters:
Chapter 1. Parameter estimation by optimization
Chapter 2. Bootstrap confidence intervals
Chapter 3. Introduction to hypothesis testing
Chapter 4. Hypothesis test examples
Chapter 5. Putting it all together: a case study
Personal Notes:
https://towardsdatascience.com/statistical-thinking-in-python-part-2-496c4b0d00f6