DSC (0207) 470 - Data Mining and Analytics in Business I

Credits: 3.00

Students will learn the application of supervised learning methods like linear and logistic regression, decision trees, naïve-Bayes and random forests for prediction. Students will gain an understanding of steps in the development of prediction models including data cleaning, balancing, and variable selection using trending software (such as Python and R).

Prerequisite 1: DSC 274
Repeatable: No Grade Type: Regular
Course Learning Goals: Students will

Implement appropriate methods for data cleaning and outlier detection
Implement different data balancing and feature selection methods
Develop different regression and classification models
Access the performance of the regression models using metrics such as r-squared, mean absolute error or root mean square error
Access the performance of the classification models using confusion matrix and area under the curve
Apply design thinking when creating a solution for an analytical problem
Illustrate the experience of using state-of-the-art analytical software