1MS041-2020

Introduction to Data Science 1MS041

Individual SageMath Jupyter .ipynb Notebooks

1. Introduction
2. BASH crash
3. Numbers, Strings, Booleans and Sets
4. Map, Function, Collection, and Probability
5. Conditional Probability, Random Variables, Loops and Conditionals
6. Random Variables, Expectations, Data, Statistics, Arrays and Tuples, Iterators and Generators
7. Statistics and List Comprehensions with New Zealand Earthquakes
8. Modular Arithmetic, Linear Congruential Generators, and Pseudo-Random Numbers
9. Pseudo-Random Numbers, Simulating from Some Discrete and Continuous Random Variables
10. Estimation, Likelihood, Maximum Likelihood Estimators and Symbolic Expressions
11. Convergence of Limits of Random Variables, Confidence Set Estimation and Testing
12. Non-parametric Estimation and Testing
13. Linear Regression
14. Markov Chains and Random Structures
15. Supervised Learning & what is machine learning
16. Supervised learning and Learning Theory
17. High Dimensional geometry and probability
18. Singular Value Decomposition
19. Putting it all together

Assignments

1. Assignment 1
2. Assignment 2
3. Assignment 3
4. Assignment 4
5. Assignment 5

Starting package

• Download the Starting package
• Unzip this into a folder that you will use as the base folder
• Whenever you download the next lectures as ipynb files, you put them in the same place as 00.ipynb and 01.ipynb, this way all pathways will be the same for all of us.

Other files

If you want to do everything yourself, here is the data-zip file, Only Data. In case you have images that dont show up, here is the images-zip file, Only Images