ITCS 6162: Data Mining
University of North Carolina, Charlotte
Exploration of�knowledge discovery and data mining algorithms for data analysis, with an emphasis on techniques suitable for large datasets. Topics�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). The curriculum also spans 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: 3.85
Grade distribution records: 2,243 students across 32 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 1,894 | 84.4% |
| B | 303 | 13.5% |
| C | 9 | 0.4% |
| W | 21 | 0.9% |
Based on 2,243 student grade records across 32 terms and 8 professors.
Instructors
- Zbigniew Ras 888 students, Average GPA 3.83
- Angelina Tzacheva 551 students, Average GPA 3.98
- Pamela Thompson 478 students, Average GPA 3.87
- Siddharth Krishnan 168 students, Average GPA 3.63
- Yong Ge 61 students, Average GPA 3.49
- Laurel Powell 55 students, Average GPA 4.00
- Xi Niu 26 students, Average GPA 3.69
- Atif Farid Mohammad 16 students, Average GPA 3.94