DSBA 6162: Data Mining
University of North Carolina, Charlotte
Explores�knowledge�discovery�and data mining algorithms for data analysis, with an emphasis on techniques suitable for large datasets. Topics to be covered include Data Operations (fusion, reduction, sanitization, balancing), Association/Representative Rules, Clustering, High Dimensional & Distributed Data Mining, Dimensionality Reduction, Link Analysis, and Actionability (Action and Meta-Action Rules). Students also examine a diverse range of data mining areas such as Mining Data Streams, Web Mining, Web Advertising,�Knowledge-Based Recommendation Systems, and Personalization. Throughout the course, a special focus will be placed on practical applications, showcasing diverse examples from fields like healthcare and business.
Average GPA: 3.80
Grade distribution records: 439 students across 25 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 342 | 77.9% |
| B | 78 | 17.8% |
| C | 3 | 0.7% |
| W | 12 | 2.7% |
Based on 439 student grade records across 25 terms and 6 professors.
Instructors
- Xi Niu 174 students, Average GPA 3.72
- Pamela Thompson 95 students, Average GPA 3.91
- Zbigniew Ras 76 students, Average GPA 3.74
- Siddharth Krishnan 63 students, Average GPA 3.88
- Angelina Tzacheva 29 students, Average GPA 3.93
- Atif Farid Mohammad 2 students, Average GPA 4.00