CS-4654: Intermediate Data Analytics and Machine Learning
Description: A technical analytics course. Covers supervised and unsupervised learning strategies, including regression, generalized linear models, regulations, dimension reduction methods, tree-based methods for classification, and clustering. Upper-level analytical methods shown in practice: e.g., advanced naive Bayes and neural networks.
Pathways: N/A
Course Hours: 3 credits
Sections Taught: 8
Average GPA: 3.26 (B+)
Strict A Rate (No A-) : 34.74%
Average Withdrawal Rate: 5.10%
| Christian Lucero | 2024 | 48.3% | 51.7% | 0.0% | 0.0% | 0.0% | 0.0% | 3.55 | 3 |
| Robert B Gramacy | 2018 | 47.7% | 22.7% | 7.2% | 7.2% | 8.3% | 7.1% | 3.03 | 2 |
| Tajbakhsh Sam Davanloo | 2016 | 22.2% | 22.2% | 22.2% | 22.2% | 0.0% | 11.1% | 2.54 | 1 |
| Jyotishka Datta | 2023 | 75.0% | 12.5% | 12.5% | 0.0% | 0.0% | 0.0% | 3.63 | 1 |
| Deborah B Smith | 2015 | 53.9% | 15.4% | 7.7% | 0.0% | 7.7% | 15.4% | 3.19 | 1 |