CMDA-4654: Intermediate Data Analytics and Machine Learning
Description: A technical analytics course. Covers supervised and unsupervised learning strategies, including regression, generalized linear models, regularization, 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: 22
Average GPA: 3.24 (rounds to B+)
Strict A Rate (No A-) : 34.12%
Average Withdrawal Rate: 2.67%
| Christian Lucero | 2024 | 44.3% | 33.4% | 15.7% | 1.4% | 2.1% | 3.0% | 3.17 | 16 |
| Robert B Gramacy | 2022 | 46.8% | 35.8% | 11.4% | 0.0% | 3.5% | 2.5% | 3.25 | 4 |
| Jyotishka Datta | 2023 | 85.4% | 13.4% | 0.0% | 0.0% | 1.1% | 0.0% | 3.83 | 2 |