Difference between revisions of "DataMining/spring2020"
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* [[DataMining/spring2020/hw1| Homework 1]] (due January 30) | * [[DataMining/spring2020/hw1| Homework 1]] (due January 30) | ||
* [[DataMining/spring2020/hw2| Homework 2]] (due February 11) | * [[DataMining/spring2020/hw2| Homework 2]] (due February 11) | ||
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| + | == Projects == | ||
| + | * [[DataMining/spring2020/project1| Project 1]] | ||
Revision as of 09:10, 11 February 2020
Course Information
| Code | CSC3140 |
| Name | Data Mining |
| Credit(s) | 3 |
| Prerequisites | CSC313 and MTH321 |
| Offered | Every Spring |
| Catalog Description | Data mining is concerned with the extraction of information from large amounts of data. This project-based course introduces the concepts, issues, tasks and techniques of data mining. Topics include data preparation and feature selection, classification, clustering, evaluation and validation, and data mining applications. |
| Syllabus | Spring 2020 Syllabus |
| Other Offerings | DataMining/offerings |
Materials and Links
- Data Mining and Analysis Fundamental Concepts and Algorithms] by Mohammed J. Zaki Wagner Meira, Jr.
- Sign Up for a Shell Account
- R-Studio
- Learn R in 15 Minutes
Slides and Examples
- Introduction
- Chapter 1 Slides
- Introduction to R and Iris EDA part 1
- Chapter 2 Slides
- Iris EDA part 2
- Iris Categorical EDA
Homework
- Homework 1 (due January 30)
- Homework 2 (due February 11)
