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Data Mining Methods

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Data Mining Methods

This course is part of Data Mining Foundations and Practice Specialization

Instructor: Qin (Christine) Lv

9,653 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

71 reviews

Intermediate level

Recommended experience

Flexible schedule

3 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.

71 reviews

Intermediate level

Recommended experience

Flexible schedule

3 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

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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

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 videos Total 152 minutes

Meet Your Instructor! 5 minutes
Preparing for this Specialization 9 minutes
Data Mining: Technique View 12 minutes
Frequent Pattern Analysis, Apriori Algorithm 12 minutes
Apriori Algorithm Example, Details 9 minutes
Example: Apriori Frequent Pattern Analysis 9 minutes
Apriori Algorithm Challenges and Improvements 14 minutes
FP-growth Algorithm, Example 17 minutes
Association Rule, Example 8 minutes
Correlation, Example 13 minutes
Other Correlation Measures 10 minutes
Example: FP-growth Frequent Pattern Analysis 9 minutes
Example: Monotonic and Anti-monotonic Constraints 10 minutes
Example: Lift Correlation 7 minutes
Example: X^2 Correlation 7 minutes

6 readings Total 55 minutes

Earn Academic Credit for Your Work! 10 minutes
Course Support 10 minutes
About This Course 10 minutes
Assessment Expectations 5 minutes
Introducing Honorlock for For-Credit Exam Proctoring 10 minutes
AI Citation and Acknowledgement 10 minutes

1 assignment Total 5 minutes

AI Policy Quiz 5 minutes

1 programming assignment Total 240 minutes

Frequent Pattern Analysis 240 minutes

1 discussion prompt Total 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 videos Total 126 minutes

Introduction to Classification 15 minutes
Decision Tree Induction, Example 20 minutes
Bayesian Classification, Example 17 minutes
Example: Decision Tree Induction Classification 10 minutes
Example: Bayesian Classification 9 minutes
Support Vector Machines 9 minutes
Neural Network 12 minutes
Ensemble, Model Evaluation 20 minutes
Model Selection 14 minutes

1 programming assignment Total 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 videos Total 106 minutes

Introduction to Clustering 12 minutes
Partitioning Methods 16 minutes
Hierarchical and Grid Based Clustering 16 minutes
Density-Based Clustering 10 minutes
Probabilistic Clustering 11 minutes
EM Clustering 11 minutes
High Dimensional, Bi-Clustering, Graph Clustering 17 minutes
Constraint Based Clustering 13 minutes

1 reading Total 10 minutes

EM Clustering: Further Explanation 10 minutes

1 programming assignment Total 240 minutes

Clustering 240 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 videos Total 110 minutes

Types of Outliers 13 minutes
Anomaly Detection Methods 1 16 minutes
Anomaly Detection Methods 2 16 minutes
Anomaly Detection Examples 13 minutes
Sequence and Time Series Data 14 minutes
Graph and Online Social Network Data 10 minutes
Web Data, KDD Conference 14 minutes
Data Mining Research Frontiers 14 minutes

1 peer review Total 180 minutes

Peer Review: Outlier Analysis, Research Frontiers 180 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

Instructor

Instructor ratings

(16 ratings)

Qin (Christine) Lv

University of Colorado Boulder

3 Courses 16,854 learners

Offered by

University of Colorado Boulder

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Frequently asked questions

A cross-listed course is offered under two or more CU Boulder degree programs on Coursera. For example, Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 for the MS-CS and DTSA 5503 for the MS-DS.

· You may not earn credit for more than one version of a cross-listed course.

· You can identify cross-listed courses by checking your program’s student handbook.

· Your transcript will be affected. Cross-listed courses are considered equivalent when evaluating graduation requirements. However, we encourage you to take your program's versions of cross-listed courses (when available) to ensure your CU transcript reflects the substantial amount of coursework you are completing directly in your home department. Any courses you complete from another program will appear on your CU transcript with that program’s course prefix (e.g., DTSA vs. CSCA).

· Programs may have different minimum grade requirements for admission and graduation. For example, the MS-DS requires a C or better on all courses for graduation (and a 3.0 pathway GPA for admission), whereas the MS-CS requires a B or better on all breadth courses and a C or better on all elective courses for graduation (and a B or better on each pathway course for admission). All programs require students to maintain a 3.0 cumulative GPA for admission and graduation.

Yes. Cross-listed courses are considered equivalent when evaluating graduation requirements. You can identify cross-listed courses by checking your program’s student handbook.

You may upgrade and pay tuition during any open enrollment period to earn graduate-level CU Boulder credit for << this course/ courses in this specialization>>. Because << this course is / these courses are >> cross listed in both the MS in Computer Science and the MS in Data Science programs, you will need to determine which program you would like to earn the credit from before you upgrade.

MS in Data Science (MS-DS) Credit: To upgrade to the for-credit data science (DTSA) version of << this course / these courses >>, use the MS-DS enrollment form. See How It Works.

MS in Computer Science (MS-CS) Credit: To upgrade to the for-credit computer science (CSCA) version of << this course / these courses >>, use the MS-CS enrollment form. See How It Works.

If you are unsure of which program is the best fit for you, review the MS-CS and MS-DS program websites, and then contact datascience@colorado.edu or mscscoursera-info@colorado.edu if you still have questions.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.