This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. Data Mining Methods can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others.
With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
Course 2 of 3 in the Data Mining Foundations and Practice Specialization.
What You Will Learn
- Identify the core functionalities of data modeling in the data mining pipeline
- Apply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work.
- Evaluate data modeling techniques, determine which is most suitable for a particular task, and identify potential improvements.
Syllabus
WEEK 1
Frequent Pattern Analysis
This module starts with an overview of data mining methods, then focuses on frequent pattern analysis, including the Apriori algorithm and FP-growth algorithm for frequent itemset mining, as well as association rules and correlation analysis.
WEEK 2
Classification
This module introduces supervised learning, classification, prediction, and covers several core classification methods including decision tree induction, Bayesian classification, support vector machines, neural networks, and ensemble methods. It also discusses classification model evaluation and comparison.
WEEK 3
Clustering
This module introduces unsupervised learning, clustering, and covers several core clustering methods including partitioning, hierarchical, grid-based, density-based, and probabilistic clustering. Advanced topics for high-dimensional clustering, bi-clustering, graph clustering, and constraint-based clustering are also discussed.
WEEK 4
Outlier Analysis
This module discusses three different types of outliers (global, contextual, and collective) and how different methods may be used to identify and analyze such outliers. It also covers some advanced methods for mining complex data, as well as the research frontiers of the data mining field.
