This repository is established for the graduate level course titled Fundamentals of Data Science. The contents are the projects mostly written in Python or R as weekly assignment with free choice of a dataset under minimum directions. The topics of each project encompass the following.
| Week | Project | Topic |
|---|---|---|
| 1 | Grim Words | Basic Statistics, Numpy, Pandas |
| 2 | Movie Ratings | Correlation, Heatmap, Boxplot |
| 3 | Olive Oil | Pairplot, Boxplot in R |
| 4 | Prob/Stat Quiz | n/a |
| 5 | Regression Model | OLS, Ridge, Lasso |
| 6 | Midterm | n/a |
| 7 | Rainfall Prediction | Machine Learning Models, Accuracy, AUC |
| 8 | Dewpoint Prediction | Neural Network |
| 9 | Dataset Selection | Project Proposal |
The following links were used for lectures and/or carrying out the projects. They are classified into Textbook, Theory, and Syntax for better lookup. Textbook is free online books that this course partly covered or recommended to read. Theory covers theoretical parts such as statistics and machine learning while systax focuses on general code snippets in Python or R.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent