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Data Mining Methods
  • Level Professional
  • Duration 24 hours
  • Course by University of Colorado Boulder
  • Offered by

About

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. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE

Modules

Course Introduction

    • 2 Videos
    • 3 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself!

2 Videos

  • Meet Your Instructor!
  • Preparing for this Specialization

3 Readings

  • Earn Academic Credit for Your Work!
  • Course Support
  • About This Course

Introduction to Apriori Algorithms

    • 4 Videos
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4 Videos

  • Data Mining: Technique View
  • Frequent Pattern Analysis, Apriori Algorithm
  • Apriori Algorithm Example, Details
  • Example: Apriori Frequent Pattern Analysis

FP-growth Algorithm, Association, Correlation

    • 9 Videos
    • 1 Programming
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1 Programming

  • Frequent Pattern Analysis

9 Videos

  • Apriori Algorithm Challenges and Improvements
  • FP-growth Algorithm, Example
  • Association Rule, Example
  • Correlation, Example
  • Other Correlation Measures
  • Example: FP-growth Frequent Pattern Analysis
  • Example: Monotonic and Anti-monotonic Constraints
  • Example: Lift Correlation
  • Example: X^2 Correlation

Decision Tree Induction, Bayesian Classification

    • 5 Videos
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5 Videos

  • Introduction to Classification
  • Decision Tree Induction, Example
  • Bayesian Classification, Example
  • Example: Decision Tree Induction Classification
  • Example: Bayesian Classification

Support Vector Machines, Neural Network, Ensemble, Evaluation

    • 4 Videos
    • 1 Programming
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1 Programming

  • Classification

4 Videos

  • Support Vector Machines
  • Neural Network
  • Ensemble, Model Evaluation
  • Model Selection

Partitioning, Hierarchical, Grid-based, and Density-based Clustering

    • 4 Videos
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4 Videos

  • Introduction to Clustering
  • Partitioning Methods
  • Hierarchical and Grid Based Clustering
  • Density-Based Clustering

Probabilistic, High-dimensional, Bi-clustering, Graph, Constraint-based Clustering

    • 4 Videos
    • 1 Readings
    • 1 Programming
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1 Programming

  • Clustering

4 Videos

  • Probabilistic Clustering
  • EM Clustering
  • High Dimensional, Bi-Clustering, Graph Clustering
  • Constraint Based Clustering

1 Readings

  • EM Clustering: Further Explanation

Types of Outliers, Outlier Detection Methods

    • 4 Videos
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4 Videos

  • Types of Outliers
  • Anomaly Detection Methods 1
  • Anomaly Detection Methods 2
  • Anomaly Detection Examples

Mining Complex Data, Research Frontiers of Data Mining

    • 4 Videos
    • 1 PeerReview
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1 Peer Review

  • Peer Review: Outlier Analysis, Research Frontiers

4 Videos

  • Sequence and Time Series Data
  • Graph and Online Social Network Data
  • Web Data, KDD Conference
  • Data Mining Research Frontiers

Auto Summary

Explore essential data mining techniques in this professional-level course, designed for aspiring data scientists and AI professionals. Led by Coursera, it delves into pattern analysis, classification, clustering, and more. With flexible 8-week sessions, it’s part of CU Boulder's accredited MS in Data Science or Computer Science programs. Ideal for recent grads and working professionals, with pay-as-you-go options.

Qin (Christine) Lv
Instructor

Qin (Christine) Lv