├── Part1_Chapter_1_Graphical_exploratory_data_analysis.pdf ├── Part1_Chapter_2_Quantitative_exploratory_data_analysis.pdf ├── Part1_Chapter_3_Discrete_variables.pdf ├── Part1_Chapter_4_Continuous_variables.pdf ├── Part1_notes_solutions_answers_Statistical_Thinking_in_Python_(Part_1).pdf ├── Part2_Chapter_1_Parameter_estimation_by_optimization.pdf ├── Part2_Chapter_2_Bootstrap_confidence_intervals.pdf ├── Part2_Chapter_3_Introduction_to_hypothesis_testing.pdf ├── Part2_Chapter_4_Hypothesis_test_examples.pdf ├── Part2_Chapter_5_Putting_it_all_together_a_case_study.pdf ├── Part2_notes_solutions_answers_Statistical_Thinking_in_Python_(Part_2).pdf └── README.md /Part1_Chapter_1_Graphical_exploratory_data_analysis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JNYH/DataCamp_Statistical_Thinking_in_Python/3629b42848881fe8d197eb6d2f00c612eeb5b807/Part1_Chapter_1_Graphical_exploratory_data_analysis.pdf 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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # (DataCamp) Statistical Thinking in Python (Part 1) 2 | 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: 3 | 4 | Chapter 1. Graphical exploratory data analysis 5 | 6 | Chapter 2. Quantitative exploratory data analysis 7 | 8 | Chapter 3. Thinking probabilistically – Discrete variables 9 | 10 | Chapter 4. Thinking probabilistically – Continuous variables 11 | 12 | ## Personal Notes: 13 | https://towardsdatascience.com/statistical-thinking-in-python-part-1-58b5ae8c0f6f 14 | 15 | 16 | ------ 17 | 18 | # (DataCamp) Statistical Thinking in Python (Part 2) 19 | 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: 20 | 21 | Chapter 1. Parameter estimation by optimization 22 | 23 | Chapter 2. Bootstrap confidence intervals 24 | 25 | Chapter 3. Introduction to hypothesis testing 26 | 27 | Chapter 4. Hypothesis test examples 28 | 29 | Chapter 5. Putting it all together: a case study 30 | 31 | ## Personal Notes: 32 | https://towardsdatascience.com/statistical-thinking-in-python-part-2-496c4b0d00f6 33 | 34 | --------------------------------------------------------------------------------