Brian’s Notes

Notes for Part 1 — Scientific Data Analysis

Mostly in the form of Jupyter notebooks for each chapter of Pasha:

• May 21 — §5.2 A Simple Plot, §5.4 Subplots, §5.5 Adjusting Marker Properties, §5.6 Adjusting Ticks, §5.7 Adjusting Fonts and Font Sizes, §5.8 Multiple Subplots, §5.9 Subplot Mosaic, §5.10 Research Example: Displaying a Best Fit, §5.11 Error Bars, §5.12 Plotting n-Dimensional Data, §5.13 Color Bars — PS02
• May 25 — §6.5 Research Example: An Exoplanet Transit
• May 28 — §7.2 Numerical Integration, §7.3 Optimization, §7.4 Statistics) — PS03
• June 1 — §8.2 Units and Constants, §8.3 Cosmological Calculations, §8.4 Coordinates, §8.6 Research Example: Automatic Offsets, §8.7 Research Example: Handling Astronomical Images — PS04

Notes for Part 2 — Data Science and Machine Learning

Code, tests, and examples in a PyCharm project with some files for each chapter of Grus:

• June 3 — Ch. 3: Visualizing Data examples
• June 6 — Ch. 4: Linear Algebra implementation and tests — Ch. 5: Statistics implementation, tests, and examples — Ch. 6: Probability implementation and examples
• June 11 — Ch. 7: Hypothesis and Inference implementation and examples — Ch. 8: Gradient Descent implementation and examples — Also, here is some other straightforward gradient descent code: quadratic fit and quadratic fit example
• June 15 — Ch. 9: Getting Data implementation and examples — Ch. 10: Working with Data implementation and examples
• June 19 — Ch. 11: Machine Learning implementation and examples — Ch. 13: Naive Bayes implementation and examples
• June 21 — Ch. 14: Simple Linear Regression implementation, tests, and examples — Ch. 15: Multiple Regression implementation and examples
• June 23 — Ch. 18: Neural Networks implementation, tests, and examples
• June 25 — A screenshot of the project structure that you will have when you are done with Grus’s live coding session
• June 26 — Ch. 19: Deep Learning (only up to and including the section titled “Softmaxes and Cross-Entropy”) implementation, tests, and examples: XOR Neural Net, Fizz Buzz Common Code, Fizz Buzz Neural Net and Fizz Buzz Softmax Neural Net