Objectives:

Students will learn concepts, techniques and tools for data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling. Apply basic machine learning algorithms (Linear Regression, k-Nearest Neighbors (k-NN), k-means, Naive Bayes) for predictive modeling.

 

Weekly Lectures (subject to change)

 

  1. Exploratory Data Analysis

  2. Distributions - Density Functions :Introduction to R

  3. Estimation- Hypothesis Testing

  4. Linear Least Squares

  5. Linear Regression- Multiple Linear Regression

  6. Classification:  Logistic Regression, LDA,QDA,KNN

  7.  Midterm

  8. Resampling Methods: Cross-Validation

  9. Resampling Methods: The Bootstrap

  10. Linear Model Selection and Regularization:Ridge, Lasso Regression

  11. Tree- Based Methods

  12. Support Vector Machines

Grading

  • Assignments 20%
  • Midterm 25%
  • Project 20%
  • Final Exam 35%