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.
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Data Mining Foundations and Practice SpecializationUniversity of Colorado BoulderAbout this Course
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Course 2 of 3 in the
Intermediate Level
data science professionals or domain experts, some experience working with data, successful completion of Data Mining Pipeline
Approx. 22 hours to complete
English
Subtitles: English
What you will 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 will gain
outlier analysisclusteringclassificationmodel evaluationfrequent pattern analysis
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 3 in the
Intermediate Level
data science professionals or domain experts, some experience working with data, successful completion of Data Mining Pipeline
Approx. 22 hours to complete
English
Subtitles: English
Offered by
University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Start working towards your Master's degree
This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder.
If you are admitted to the full program, your courses count towards your degree learning.
Syllabus - What you will learn from this course
6 hours to complete
Frequent Pattern Analysis
This module starts with an overview of 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.
6 hours to complete
2 videos (Total 94 min)
6 hours to complete
Classification
This module 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.
6 hours to complete
2 videos (Total 106 min)
6 hours to complete
Clustering
This module introduces 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.
6 hours to complete
2 videos (Total 105 min), 1 reading, 1 quiz
5 hours to complete
Outlier Analysis
This module 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.
5 hours to complete
2 videos (Total 108 min)
About the Data Mining Foundations and Practice Specialization
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.
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