DSI-6010 Predictive Analytics 1 - Machine Learning Tools - with Python
In this course, students will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models or, in some cases, to fine tune the model), and test data (data used to predict the performance of the final model). The course includes hands-on work with Python, a free software environment with statistical computing capabilities. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
Credits: 3
Delivery Methods: Online
Please contact the schools for availability.
Preview the Online Syllabus 
(Please visit the University bookstore to view the correct materials for each course by semester as the contents of the actual online syllabus may differ from the preview due to updates or revisions)
DSI-6040 Predictive Analytics 1 - Machine Learning Tools - with R
In this course, students will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models or, in some cases, to fine tune the model), and test data (data used to predict the performance of the final model). The course includes hands-on work with R, a free software environment for statistical computing.
Credits: 3
Delivery Methods: Online
Please contact the schools for availability.
Preview the Online Syllabus
(Please visit the University bookstore to view the correct materials for each course by semester as the contents of the actual online syllabus may differ from the preview due to updates or revisions)