This is the assignment/homework question w.r.t. 2 courses Statistical Computing & Introductory Data Science.

Statistical-Computing

For Statistical Computing, please check the outside HW 1~7, each folder contains the slide and the HW solution. The HW questions are in the last few pages of the slides, and only coding problems are uploaded here.

Syllabus:

1. Optimization and Solving Nonlinear Equations
2. EM Optimization Methods
3. Numerical Integration
4. Simulation and Monte Carlo Integration
5. Markov Chain Monte Carlo
6. Bootstrapping

Introductory Data Science

For Introductory Data Science course, they're all in the DS folder. The HW questions and answers are all in the Homework Q&A folder as .ipynb files. Lecture codebook is in the Lecture Codebooks folder, they're all .ipynb files. The rest files in DS folder is for the Final Project, including the code and data. FYI, we used the UJIndoorLoc dataset from the UCI Machine Learning Repository, and you can check the website here: https://archive.ics.uci.edu/dataset/310/ujiindoorloc

Syllabus:

1. Python Programming: Basics/numpy and Pandas/OOP/Visualization/simulations

2. Regression: Linear regression/model selection/cross validation/bootstrap

3. Classification: Logistic regression/KNN/naïve Bayes/decision tree/random forest

4. Neural network: Overview/SGD/CNN

5. Causal inference (if time permits): Potential outcomes/randomized experiments/observational data