Syllabus

Introduction to Data Science (Professor Zambom)

1. Supervised vs. Unsupervised Learning
2. Review of Linear Algebra
3. Random Variables 
4. Introduction to R
5. Review of Elementary Statistics
6. Time Series Analysis
7. Test, Training, and Cross Validation
8. Linear, Multiple Linear, Polynomial Regression
9. Unsupervised Learning

Introduction to Graph Theory and Applications (Professor Kerobyan)​

1. Introduction to Graphs
2. Graph concepts
3. Page rank algorithm
4. Markov processes
5. Graph based clustering

Machine Learning in Python (Professor Shapiro)

1. Installing Python, understanding Jupyter notebooks. command shells
2. Rapid introduction to Python
3. Least squares and multiple Linear Regression; train/test split; using scikit-learn
4. Polynomial regression; plotting with matplotlib; histograms and boxplots
5. Anscombes quartet; plotting multiple axes; splines; nonlinear regression; gradient desscent
6. Binary classification; logistic regression; classification metircs; ROC curves.
7. Probabilistic methods: KNN, LDA, QDA, Naive Bayes; One-Hot encoding.
8. Classification and Regression Trees
9. Neural Networks
10. Principal Component Analysis
11. Support Vector Machines
12. Tree Ensembles including Boosting, Bagging and Random Forests
13. KMeans, Hierarchical, and DBScan clustering Algorithms
14. Programming challenges on Kaggle (two days)

Introduction to Data Mining (Professors Liu and Wang)

1. Relational Databases
2. Installing and Using MySQL
3. Structure Query Languages
4. Big Data
5. Data pre-processing
6. Feature Engineering
7. Frequent pattern mining
8. Introduction to Cloud Computing using AWS
   
9. Projects (three days)

​Guest Lectures

1. Data Analysis at Kaiser Permanente, Brianna Amador
2. Introduction to Latex and Beamer, Miriam Ramirez
3. Graph Clustering in the Game of Thrones, Richard Wolff
4. Visualizing Student Success, Jorge Martinez
5. Q-Learning, Jorge Martinez
6. Overview of RL, CV, NLP, Andrew MIller and Seyed Sajjadi