DANL 300 - Advanced Data Analytics

Credit(s): 3
Lecture: 3
Non-Lecture: 0

This course is an advanced level course that aims to provide an extensive coverage of business data analytics modeling methods. Topics include (1) supervised and unsupervised modeling techniques in Python, (2) support vector machines (SVMs), (3) neural networks, (4) recommender systems such as collaborative filtering, association rules, and matrix factorization (5) time series analysis, and (6) text mining and sentiment analysis using social media platforms such as Twitter, Facebook, etc. Computing is done in Python. This course will also introduce advanced topics, such as big data, deep learning, and transfer learning.

Prerequisite(s): DANL 101 and DANL 210
Offered: Fall Semester