ITCS 3162: Introduction to Data Mining
University of North Carolina, Charlotte
The key objectives of this course are two-fold: (1) to teach the basic concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world business applications. �Topics include: Data Collection, Data Preprocessing, Data Exploration, Feature Engineering, Prediction Model, Clustering, Association Analysis, Graph/Network Analysis, Text Mining and Social Media Analysis, and Anomaly Detection.
Average GPA: 3.45
Grade distribution records: 1,745 students across 27 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 1,112 | 63.7% |
| B | 298 | 17.1% |
| C | 123 | 7.0% |
| D | 25 | 1.4% |
| F | 69 | 4.0% |
| W | 84 | 4.8% |
Based on 1,745 student grade records across 27 terms and 10 professors.
Instructors
- Aileen Benedict 625 students, Average GPA 3.62
- Siddharth Krishnan 375 students, Average GPA 3.17
- Pamela Thompson 294 students, Average GPA 3.46
- Thomas Polk 135 students, Average GPA 3.51
- Qiong Cheng 76 students, Average GPA 3.44
- Angelina Tzacheva 75 students, Average GPA 3.64
- Zbigniew Ras 61 students, Average GPA 3.53
- Michael Korvink 44 students, Average GPA 3.15
- Yong Ge 39 students, Average GPA 2.86
- Li-Shiang Tsay 21 students, Average GPA 3.00