University of Colorado Boulder

Data Mining Methods

This course is part of Data Mining Foundations and Practice Specialization

Taught in English

Some content may not be translated

Qin (Christine) Lv

Instructor: Qin (Christine) Lv

5,117 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.8

(19 reviews)

Intermediate level

Recommended experience

24 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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.

Details to know

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Course

Gain insight into a topic and learn the fundamentals

3.8

(19 reviews)

Intermediate level

Recommended experience

24 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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Build your subject-matter expertise

This course is part of the Data Mining Foundations and Practice Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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 videos3 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

Instructor

Qin (Christine) Lv
University of Colorado Boulder
3 Courses9,516 learners

Offered by

Recommended if you're interested in Data Analysis

Get a head start on your degree

This course is part of the following degree programs offered by University of Colorado Boulder. If you are admitted and enroll, your coursework can count toward your degree learning and your progress can transfer with you.

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