DATX 5805 - Predictive Modeling Algorithms
Fall 2026 Syllabus, Section 01, CRN 43055,
Credit hours: 3
Course Meeting Times
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Instructor
G. Jay Kerns
Email: gkerns@ysu.edu
Course Description
5805. Predictive Modeling Algorithms. Predictive modeling (also referred to predictive analytics and machine learning) applies statistical techniques in analyzing data to predict outcomes. Through a hands-on approach, this course helps students develop basic skills in predictive analytics. Topics may include (not limited to) k-nearest neighbors, naïve-Bayes, linear and logistic regression models, time-series models, classification and regression trees, Principle Component/Factor Analysis, non-linear models, neural networks, random forests, and cluster analysis among others. Prereq.: Junior standing or higher and GPA of 2.5 or higher. 3 s.h.
Course Readings
| Group | Title | Author | ISBN |
|---|---|---|---|
| Required | An Introduction to Statistical Learning | Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani | 978-1-0716-1420-4 |
The textbook is freely available at https://www.statlearning.com/.
The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.
Assignments/Assessments
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Schedule of Topics and Assignments
| Week of | Reading(s) | Proposed Topic | Due/To Prepare for Class |
|---|---|---|---|
| 8/24 | Ch2: Section 1 Ch2: Section 2 |
Statistical Learning Assessing Model Accuracy |
Quiz 1 |
| 8/31 | Ch2: Section 3 | Intro to R | Assignment 1 |
| 9/7 | Ch3: Section 1 Ch3: Section 2 Ch3: Section 3 |
Simple Linear Regression Multiple Linear Regression Other Considerations |
Quiz 2 |
| 9/14 | Ch3: Section 4 Ch3: Section 5 Ch3: Section 6 |
Marketing Plan Comparison with Nearest Neighbors |
Assignment 2 |
| 9/21 | Ch4: Section 1 Ch4: Section 2 Ch4: Section 3 |
Classification Overview Logistic Regression |
Quiz 3 |
| 9/28 | Ch4: Section 6 Ch4: Section 7 |
Generalized Linear Models | Assignment 3 |
| 10/5 | Ch5: Section 1 Ch5: Section 2 |
Cross Validation Bootstrap |
Assignment 4 |
| 10/12 | Ch5: Section 3 | Midterm Exam | |
| 10/19 | Ch6: Section 1 Ch6: Section 2 |
Subset Selection Shrinkage Methods |
Quiz 4 |
| 10/26 | Ch6: Section 3 Ch6: Section 4 |
Dimension Reduction High Dimensional Considerations |
Assignment 5 |
| 11/2 | Ch8: Section 1 Ch8: Section 2 |
Decision Trees Random Forests |
Quiz 5 |
| 11/9 | Ch8: Section 3 | Assignment 6 | |
| 11/16 | Ch12: Section 1 Ch12: Section 2 |
Principal Components Analysis Clustering Methods |
Quiz 6 |
| 11/23 | Ch12: Section 4 Ch 12: Section 5 |
Unsupervised Learning |
The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning.