Course Introduction to Data Mining

Syllabus

- Week 1 – Introduction to data mining and tools (python pandas, weka and R)
- Week 2 – Data Types and statistical measures
- Week 3 – Data preprocessing and cleaning 
- Week 4 – Data import to tools from different environments (databases, flat files) 
- Week 5 – Exploring data and visualization 1
- Week 6 – Exploring data and visualization 2
- Week 7 – Classification 1
- Week 8 – Classification 2
- Week 9 – Association rule mining 1
- Week 10 – Association rule mining 2
- Week 11 – Clustering 1
- Week 12 – Clustering 2
- Week 13 – Clustering 3
- Week 14 – Review 

Lessons in 2021

• 2021-10-07
• 2021-10-14
• 2021-10-21
• 2021-10-28 No lessons due to National holiday
• 2021-11-04
• 2021-11-11
• 2021-11-18 This lesson is canceled.
• 2021-11-25
• 2021-12-02 No lessons due to week of the midterms
• 2021-12-09 This lesson is canceled.
• 2021-12-16
• 2021-12-23
• 2021-12-30 This lesson is canceled.
• 2022-01-06
• 2022-01-13
• Recorded Lessons for Canceled ones

Text Books

1. Introduction to Data Mining by Pang-Ning Tan and Michael Steinbach and Anuj Karpatne and Vipin Kumar

Reference Books

1. An R Companion for Introduction to Data Mining by Michael Hahsler

2. Python Data Science Handbook by Jake VanderPlas

3. Data Mining: Practical Machine Learning Tools and Techniques by by Ian H. Witten and Eibe Frank and Mark A. Hall and Christopher J. Pal and