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Data Mining Methods

Data Mining Methods

This course is part of Data Mining Foundations and Practice Specialization

Instructor: Qin (Christine) Lv

10,140 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

72 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

72 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

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

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

91%

of learners achieved a positive career outcome

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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
Earn a shareable career certificate

There are 4 modules in this course

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

Module details

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 videos6 readings1 assignment1 programming assignment1 discussion prompt

15 videosTotal 152 minutes

Meet Your Instructor!5 minutes
Preparing for this Specialization9 minutes
Data Mining: Technique View12 minutes
Frequent Pattern Analysis, Apriori Algorithm 12 minutes
Apriori Algorithm Example, Details9 minutes
Example: Apriori Frequent Pattern Analysis9 minutes
Apriori Algorithm Challenges and Improvements14 minutes
FP-growth Algorithm, Example17 minutes
Association Rule, Example8 minutes
Correlation, Example13 minutes
Other Correlation Measures10 minutes
Example: FP-growth Frequent Pattern Analysis9 minutes
Example: Monotonic and Anti-monotonic Constraints10 minutes
Example: Lift Correlation7 minutes
Example: X^2 Correlation7 minutes

6 readingsTotal 46 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for Your Work! 10 minutes
Course Support10 minutes
About This Course10 minutes
Assessment Expectations5 minutes
AI Citation and Acknowledgement10 minutes

1 assignmentTotal 5 minutes

AI Policy Quiz5 minutes

1 programming assignmentTotal 240 minutes

Frequent Pattern Analysis240 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself!10 minutes

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

9 videosTotal 126 minutes

Introduction to Classification 15 minutes
Decision Tree Induction, Example20 minutes
Bayesian Classification, Example17 minutes
Example: Decision Tree Induction Classification 10 minutes
Example: Bayesian Classification9 minutes
Support Vector Machines9 minutes
Neural Network 12 minutes
Ensemble, Model Evaluation20 minutes
Model Selection14 minutes

1 programming assignmentTotal 240 minutes

Classification 240 minutes

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

8 videosTotal 106 minutes

Introduction to Clustering12 minutes
Partitioning Methods16 minutes
Hierarchical and Grid Based Clustering16 minutes
Density-Based Clustering10 minutes
Probabilistic Clustering11 minutes
EM Clustering11 minutes
High Dimensional, Bi-Clustering, Graph Clustering17 minutes
Constraint Based Clustering13 minutes

1 readingTotal 10 minutes

EM Clustering: Further Explanation 10 minutes

1 programming assignmentTotal 240 minutes

Clustering240 minutes

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

8 videosTotal 110 minutes

Types of Outliers13 minutes
Anomaly Detection Methods 116 minutes
Anomaly Detection Methods 216 minutes
Anomaly Detection Examples 13 minutes
Sequence and Time Series Data14 minutes
Graph and Online Social Network Data10 minutes
Web Data, KDD Conference 14 minutes
Data Mining Research Frontiers14 minutes

1 peer reviewTotal 180 minutes

Peer Review: Outlier Analysis, Research Frontiers180 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

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