We may earn an affiliate commission when you visit our partners.
Qin (Christine) Lv
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.
Read more
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.
Read more
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
Register for this course and see more details by visiting:
OpenCourser.com/course/3qqgu7/data
What's inside
Syllabus
Frequent Pattern Analysis
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.
Read more
Syllabus
Frequent Pattern Analysis
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.
Read more
Classification
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.
Clustering
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.
Outlier Analysis
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers a wide range of essential data mining methods
Taught by highly-accredited instructors
Introduces students to both supervised and unsupervised learning
Suitable for both academic credit and professional development
May require prerequisites for students new to data mining
Save this course
Save Data Mining Methods to your list so you can find it easily later:
Save
Save
Reviews summary
Practical course in data mining methods
According to students, Data Mining Methods is a practical course that is largely positive. However, this course is characterized by long lectures and few opportunities for interaction with the instructor.
Course focuses on practical methods.
"very useful course
"
"Overall this is a very practical course."
Limited interaction with instructor.
Lectures are long.
"Long lectures and poor explanations. "
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Mining Methods with these
activities:
Read 'Data Mining: Concepts and Techniques' by Han and Kamber
Show steps
Gain a comprehensive understanding of data mining concepts, techniques, and applications
View
Data Mining: Concepts and Techniques
on Amazon
Show steps
-
Read selected chapters relevant to the course topics
-
Summarize key concepts and algorithms discussed in the book
Review linear algebra
Show steps
Develop a better understanding of linear algebra, matrices, vector algebra, and related concepts before the course
Browse courses on
Linear Algebra
Show steps
-
Review lecture notes from previous coursework on linear algebra
-
Solve practice problems on matrices, vector operations, and linear equations
-
Use online resources and simulations to visualize linear algebra concepts
Complete Codecademy courses on data mining
Show steps
Enhance your understanding of data mining techniques and algorithms through interactive exercises
Show steps
-
Register for Codecademy
-
Enroll in relevant data mining courses
-
Complete coding exercises and quizzes
Three other activities
Expand to see all activities and additional details
Show all six activities
Participate in online discussion forums
Show steps
Connect with peers, exchange ideas, and clarify concepts related to the course
Show steps
-
Join online discussion forums for the course
-
Actively participate in discussions, ask questions, and share insights
Complete coding challenges
Show steps
Practice implementing data mining algorithms and techniques through coding challenges
Browse courses on
Coding
Show steps
-
Solve coding challenges on platforms like HackerRank or LeetCode
-
Implement data mining algorithms from scratch in your preferred programming language
Develop a data mining project
Show steps
Apply data mining techniques to solve a real-world problem and showcase your understanding
Browse courses on
Data Analysis
Show steps
-
Identify a problem or dataset that you want to explore
-
Choose appropriate data mining algorithms and techniques
-
Implement and evaluate your solution
-
Write a report or create a presentation to summarize your findings
Read 'Data Mining: Concepts and Techniques' by Han and Kamber
Show steps
Gain a comprehensive understanding of data mining concepts, techniques, and applications
View
Data Mining: Concepts and Techniques
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters relevant to the course topics
- Summarize key concepts and algorithms discussed in the book
Review linear algebra
Show steps
Develop a better understanding of linear algebra, matrices, vector algebra, and related concepts before the course
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review lecture notes from previous coursework on linear algebra
- Solve practice problems on matrices, vector operations, and linear equations
- Use online resources and simulations to visualize linear algebra concepts
Complete Codecademy courses on data mining
Show steps
Enhance your understanding of data mining techniques and algorithms through interactive exercises
Show steps
Show steps
Show steps
- Register for Codecademy
- Enroll in relevant data mining courses
- Complete coding exercises and quizzes
Three other activities
Expand to see all activities and additional details
Show all six activities
Participate in online discussion forums
Show steps
Connect with peers, exchange ideas, and clarify concepts related to the course
Show steps
Show steps
Show steps
- Join online discussion forums for the course
- Actively participate in discussions, ask questions, and share insights
Complete coding challenges
Show steps
Practice implementing data mining algorithms and techniques through coding challenges
Browse courses on
Coding
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like HackerRank or LeetCode
- Implement data mining algorithms from scratch in your preferred programming language
Develop a data mining project
Show steps
Apply data mining techniques to solve a real-world problem and showcase your understanding
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a problem or dataset that you want to explore
- Choose appropriate data mining algorithms and techniques
- Implement and evaluate your solution
- Write a report or create a presentation to summarize your findings
Career center
Learners who complete Data Mining Methods will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to work with large datasets and to deploy models into production.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Business Analyst
Business Analyst
A Business Analyst uses data to help businesses make better decisions. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to help businesses understand their customers and markets. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to solve business problems. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Statistician
Statistician
A Statistician collects and analyzes data to help businesses make informed decisions. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to identify and assess risks. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Actuary
Actuary
An Actuary uses data to assess and manage financial risks. This course can help you build a foundation in data mining techniques, which are essential for success in this role. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and tests software applications. This course can help you build a foundation in data mining techniques, which are increasingly used in software development. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to work with large datasets and to deploy models into production.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course can help you build a foundation in data mining techniques, which are increasingly used in database management. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to work with large datasets and to deploy models into production.
Web Developer
Web Developer
A Web Developer designs and develops websites. This course can help you build a foundation in data mining techniques, which are increasingly used in web development. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to work with large datasets and to deploy models into production.
Data Architect
Data Architect
A Data Architect designs and manages data systems. This course can help you build a foundation in data mining techniques, which are increasingly used in data architecture. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to work with large datasets and to deploy models into production.
Computer Scientist
Computer Scientist
A Computer Scientist conducts research on new computer technologies. This course can help you build a foundation in data mining techniques, which are essential for success in this field. You will learn how to identify patterns and trends in data, as well as how to build models to predict future outcomes. This course can also help you develop the skills needed to communicate your findings to stakeholders.
For more career information including salaries, visit:
OpenCourser.com/course/3qqgu7/data
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Mining Methods.
Save
Provides a comprehensive overview of data mining concepts and techniques. It covers data preprocessing, data mining algorithms, and data mining applications.
Save
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on supervised learning.
Provides a comprehensive overview of reinforcement learning algorithms and techniques.
Provides a comprehensive overview of frequent pattern mining algorithms and techniques.
Provides a comprehensive overview of clustering algorithms and techniques.
Provides a comprehensive overview of outlier analysis algorithms and techniques.
Save
Provides a comprehensive overview of deep learning algorithms and techniques.
Save
Provides a comprehensive overview of data mining techniques using the R programming language.
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on statistical and probabilistic approaches.
Provides a comprehensive overview of data mining techniques for business intelligence applications.
Provides a gentle introduction to data mining concepts and techniques for non-technical readers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3qqgu7/data
Share
Similar courses
Here are nine courses similar to
Data Mining Methods.
Data Mining Project
OpenCourser.com/course/1mbx5y/data
Data Mining Project
Most relevant
Data Mining Pipeline
OpenCourser.com/course/5kgzpb/data
Data Mining Pipeline
Most relevant
Dynamic Programming, Greedy Algorithms
OpenCourser.com/course/likefg/dynamic
Dynamic Programming, Greedy Algorithms
Most relevant
Applications of Software Architecture for Big Data
OpenCourser.com/course/qrqr45/applications
Applications of Software Architecture for Big Data
Most relevant
Fundamentals of Software Architecture for Big Data
OpenCourser.com/course/1ctl08/fundamentals
Fundamentals of Software Architecture for Big Data
Most relevant
When to Regulate? The Digital Divide and Net Neutrality
OpenCourser.com/course/5pqber/when
When to Regulate? The Digital Divide and Net Neutrality
Most relevant
Advanced Data Structures, RSA and Quantum Algorithms
OpenCourser.com/course/ite7ih/advanced
Advanced Data Structures, RSA and Quantum Algorithms
Most relevant
Fundamentals of Data Visualization
OpenCourser.com/course/t6pf79/fundamentals
Fundamentals of Data Visualization
Most relevant
Software Architecture Patterns for Big Data
OpenCourser.com/course/qgl8j5/software
Software Architecture Patterns for Big Data
Most relevant
Effort
six to seven hours a week for four weeks
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Qin (Christine) Lv
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
Data Mining Foundations and Practice. It's comprised of two courses:
- Data Mining Pipeline
- Data Mining Methods (viewing)
Good to know
Covers a wide range of essential data mining methods
Taught by highly-accredited instructors
Introduces students to both supervised and unsupervised learning
Suitable for both academic credit and professional development
May require prerequisites for students new to data mining
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser