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
This course is part of Data Mining Foundations and Practice Specialization

Instructor: Qin (Christine) Lv
Access provided by Cyprus University of Technology
8,596 already enrolled
(66 reviews)
Recommended experience
What you'll 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.
Skills you'll gain
- Scalability
- Data Science
- Supervised Learning
- Statistical Analysis
- Bayesian Statistics
- Machine Learning Algorithms
- Exploratory Data Analysis
- Big Data
- Artificial Neural Networks
- Machine Learning
- Data Mining
- Unsupervised Learning
- Analysis
- Classification And Regression Tree (CART)
- Data Analysis
- Anomaly Detection
- Algorithms
Details to know

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There are 4 modules in this course
This week starts with an overview of this course, 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.
What's included
15 videos5 readings1 programming assignment1 discussion prompt
This week 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.
What's included
9 videos1 programming assignment
This week introduces you to 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.
What's included
8 videos1 reading1 programming assignment
This week 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.
What's included
8 videos1 peer review
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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