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Introduction to Data Science 1MS041

You can download the Lecture notes here.

Precision Recall survey here

Introductory Jupyter .ipynb Notebooks

These notebooks contain the basic theory of how to work with python and BASH, that will be needed in this course.

A01. A01-BASH_Unix_Shell

Individual Jupyter .ipynb lecture Notebooks

These notebooks are numbered according to which lecture they coincide with and will be updated after the lectures. Before the lecture they can be considered preliminary.

1. 01-Probability
2. 02-Random_Variables
3. 02-Random_Variables_examples
4. 03-Random_Variables
5. 04-Concentration_and_Limits
6. 05-Limits
7. 05-Risk
8. 06-Fundamentals_of_estimation
9. 07-Estimation_Likelihood
10. 07-Optimization
11. 07-StandardError
12. 08-PRNG
13. 09-Markov_chains
14. 10-Pattern_Recognition
15. 11-Training_Testing_Metrics
16. 12-Regression
17. 13-High_Dimension
18. 14-Dimensionality_Reduction
19. 15-Extra_Topics

Problem Solving Sessions

These notebooks are numbered according to which problem solving session they coincide with.

1. 02-ProbSS1
2. 03-ProbSS2
3. 07-ProbSS4_Estimation
4. 08-ProbSS5
5. 11-ProbSS06
6. 12-ProbSS07

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 *.ipynb, this way all pathways will be the same for all of us.

Assignment notebooks (Will be empty until it is time)

1. Assignment_1
2. Assignment_2
3. Assignment_3
4. Assignment_4