Introduction to Data Science 1MS041

Individual SageMath Jupyter .ipynb Notebooks

• 00. Introduction
• 01. BASH crash
• 02. Numbers, Strings, Booleans and Sets
• 03. Map, Function, Collection, and Probability
• 04. Conditional Probability, Random Variables, Loops and Conditionals
• 05. Random Variables, Expectations, Data, Statistics, Arrays and Tuples, Iterators and Generators
• 06. Data and Statistics: New Zealand Earthquakes, 2018 Swedish National Election and Pubs in Open Street Maps of DL & SE
• 07. Modular Arithmetic, Linear Congruential Generators, and Pseudo-Random Numbers
• 08. Pseudo-Random Numbers, Simulating from Some Discrete and Continuous Random Variables
• 09. Estimation, Likelihood, Maximum Likelihood Estimators and Regressions
• 10. Convergence of Limits of Random Variables, Confidence Set Estimation and Testing
• 10c. Concentration Inequalities
• 11. Non-parametric Estimation and Testing
• 12. Linear Regression
• 13. Markov Chains and Random Structures
• 14. Supervised Learning & what is machine learning?
• 15. Supervised learning continued…
• 16. High-Dimensional Space
• 17. Singular value decomposition

Individual Auto-graded Assignment Preparation

• Assignment 1 assesses comprehension of the lecture companion SageMath-Kernel (9.1+) Jupyter notebooks 00.ipynb,…,05.ipynb
• Assignment 2 assesses comprehension of the lecture companion SageMath-Kernel (9.1+) Jupyter notebooks 06.ipynb,…,12.ipynb

Starting package

• Download the Starting package with all the notebooks so far (latest update Wed Oct 6 22:15-ish hours UTC 2021)
• Unzip this into a folder that you will use as the base folder
• Whenever you download the next or latest or updated 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.