We may earn an affiliate commission when you visit our partners.
Di Wu
The "Association Rules and Outliers Analysis" course introduces students to fundamental concepts of unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining. Additionally, students will explore outlier detection methods, with a deep understanding of contextual outliers. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying association rules and outlier detection techniques to diverse datasets.
Read more
The "Association Rules and Outliers Analysis" course introduces students to fundamental concepts of unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining. Additionally, students will explore outlier detection methods, with a deep understanding of contextual outliers. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying association rules and outlier detection techniques to diverse datasets.
Read more
The "Association Rules and Outliers Analysis" course introduces students to fundamental concepts of unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining. Additionally, students will explore outlier detection methods, with a deep understanding of contextual outliers. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying association rules and outlier detection techniques to diverse datasets.
Course Learning Objectives:
By the end of this course, students will be able to:
1. Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection.
2. Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.
3. Explore Apriori algorithms to mine frequent itemsets efficiently and generate association rules.
4. Implement and interpret support, confidence, and lift metrics in association rule mining.
5. Comprehend the concept of constraint-based association rule mining and its role in capturing specific association patterns.
6. Analyze the significance of outlier detection in data analysis and real-world applications.
7. Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.
8. Understand contextual outliers and contextual outlier detection techniques for capturing outliers in specific contexts.
9. Apply association rules and outlier detection techniques in real-world case studies to derive meaningful insights.
Throughout the course, students will actively engage in tutorials and case studies, strengthening their association rule mining and outlier detection skills and gaining practical experience in applying these techniques to diverse datasets. By achieving the learning objectives, participants will be well-equipped to excel in unsupervised learning tasks and make informed decisions using association rules and outlier detection techniques.
Register for this course and see more details by visiting:
OpenCourser.com/course/h7xvvk/association
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Frequent Itemsets
This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.
Read more
Syllabus
Frequent Itemsets
This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.
Read more
Association Rule Mining
This week we will briefly discuss association rule mining, such as closed and maxed patterns.
Apriori and FP Growth Algorithm
This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.
Outliers
Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.
Case Study
The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores unsupervised learning techniques, focusing on association rules and outlier detection
Covers essential concepts, including frequent itemsets, association rules, and outlier detection methods
Provides examples of Apriori algorithms and constraint-based association rule mining for practical implementation
Includes hands-on tutorials and case studies for practical experience in applying these techniques
Suitable for learners seeking a foundational understanding of unsupervised learning methods, particularly association rules and outlier detection
Assumes basic knowledge of data analysis and data mining principles
Save this course
Save Association Rules Analysis to your list so you can find it easily later:
Save
Save
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 Association Rules Analysis with these
activities:
Mathematical background review
Show steps
Sharpen your mathematical skills by reviewing basic concepts like calculus to better understand the course material's mathematical foundations.
Browse courses on
Calculus
Show steps
-
Go through your Calculus 1 notes or textbook
-
Practice solving basic calculus problems
-
Take a practice quiz or exam to test your understanding
Review Probability and Statistics
Show steps
Review basic concepts of probability and statistics to enhance understanding of association rules analysis.
Browse courses on
Probability
Show steps
-
Review probability distribution and hypothesis testing
-
Practice solving statistical problems using techniques like Bayes theorem
Discussion Forum Participation
Show steps
Engage in discussions with peers to clarify concepts, share insights, and gain diverse perspectives on association rules and outlier detection.
Browse courses on
Association Rules
Show steps
-
Post questions or comments on discussion topics
-
Respond to inquiries from fellow students
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Association rule mining exercises
Show steps
Reinforce your understanding of association rule mining by solving practice problems and exercises.
Browse courses on
Association Rule Mining
Show steps
-
Find online resources or textbooks with practice exercises
-
Work through the exercises and check your answers
-
Review the solutions and identify areas for improvement
Apriori Algorithm Exercises
Show steps
Reinforce understanding of the Apriori algorithm by completing exercises that simulate real-world scenarios.
Browse courses on
Association Rule Mining
Show steps
-
Implement the Apriori algorithm in a coding environment
-
Generate association rules from sample datasets
Hands-on data analysis project
Show steps
Apply the concepts learned in the course by embarking on a hands-on project that involves data analysis and mining techniques.
Browse courses on
Data Analysis
Show steps
-
Choose a dataset that aligns with your interests
-
Clean and prepare the data for analysis
-
Apply association rule mining and outlier detection techniques
-
Present your findings and insights
Peer discussion on association rule mining
Show steps
Engage with peers in discussions to clarify concepts, share insights, and strengthen your understanding of association rule mining.
Browse courses on
Association Rule Mining
Show steps
-
Join or create a discussion group
-
Participate in discussions on association rule mining topics
-
Contribute your own insights and questions
Python Tutorial on Association Rule Mining
Show steps
Enhance your practical skills by following a guided tutorial on implementing association rule mining techniques using Python.
Browse courses on
Association Rule Mining
Show steps
-
Install necessary Python libraries
-
Understand and apply Python functions for association rule mining
Outlier detection techniques tutorials
Show steps
Expand your knowledge of outlier detection techniques by exploring guided tutorials and resources.
Browse courses on
Outlier Detection
Show steps
-
Search for tutorials on different outlier detection methods
-
Follow the tutorials and apply the concepts to practice problems
-
Share your understanding and ask questions in course forums
Advanced association rule mining tutorials
Show steps
Delve deeper into advanced association rule mining techniques by exploring online tutorials and resources to enhance your understanding.
Browse courses on
Association Rule Mining
Show steps
-
Search for tutorials on advanced association rule mining algorithms
-
Follow the tutorials and apply the concepts to practice problems
-
Discuss your findings and questions in course forums
Case Study Analysis Report
Show steps
Apply association rule mining and outlier detection techniques to solve a real-world problem and showcase your understanding of these concepts.
Browse courses on
Association Rule Mining
Show steps
-
Identify a suitable dataset for analysis
-
Apply association rule mining to extract insights and identify trends
-
Utilize outlier detection methods to identify and analyze anomalous data points
-
Prepare a comprehensive report summarizing your findings and insights
Mathematical background review
Show steps
Sharpen your mathematical skills by reviewing basic concepts like calculus to better understand the course material's mathematical foundations.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Go through your Calculus 1 notes or textbook
- Practice solving basic calculus problems
- Take a practice quiz or exam to test your understanding
Review Probability and Statistics
Show steps
Review basic concepts of probability and statistics to enhance understanding of association rules analysis.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability distribution and hypothesis testing
- Practice solving statistical problems using techniques like Bayes theorem
Discussion Forum Participation
Show steps
Engage in discussions with peers to clarify concepts, share insights, and gain diverse perspectives on association rules and outlier detection.
Browse courses on
Association Rules
Show steps
Show steps
Show steps
- Post questions or comments on discussion topics
- Respond to inquiries from fellow students
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Association rule mining exercises
Show steps
Reinforce your understanding of association rule mining by solving practice problems and exercises.
Browse courses on
Association Rule Mining
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice exercises
- Work through the exercises and check your answers
- Review the solutions and identify areas for improvement
Apriori Algorithm Exercises
Show steps
Reinforce understanding of the Apriori algorithm by completing exercises that simulate real-world scenarios.
Browse courses on
Association Rule Mining
Show steps
Show steps
Show steps
- Implement the Apriori algorithm in a coding environment
- Generate association rules from sample datasets
Hands-on data analysis project
Show steps
Apply the concepts learned in the course by embarking on a hands-on project that involves data analysis and mining techniques.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests
- Clean and prepare the data for analysis
- Apply association rule mining and outlier detection techniques
- Present your findings and insights
Peer discussion on association rule mining
Show steps
Engage with peers in discussions to clarify concepts, share insights, and strengthen your understanding of association rule mining.
Browse courses on
Association Rule Mining
Show steps
Show steps
Show steps
- Join or create a discussion group
- Participate in discussions on association rule mining topics
- Contribute your own insights and questions
Python Tutorial on Association Rule Mining
Show steps
Enhance your practical skills by following a guided tutorial on implementing association rule mining techniques using Python.
Browse courses on
Association Rule Mining
Show steps
Show steps
Show steps
- Install necessary Python libraries
- Understand and apply Python functions for association rule mining
Outlier detection techniques tutorials
Show steps
Expand your knowledge of outlier detection techniques by exploring guided tutorials and resources.
Browse courses on
Outlier Detection
Show steps
Show steps
Show steps
- Search for tutorials on different outlier detection methods
- Follow the tutorials and apply the concepts to practice problems
- Share your understanding and ask questions in course forums
Advanced association rule mining tutorials
Show steps
Delve deeper into advanced association rule mining techniques by exploring online tutorials and resources to enhance your understanding.
Browse courses on
Association Rule Mining
Show steps
Show steps
Show steps
- Search for tutorials on advanced association rule mining algorithms
- Follow the tutorials and apply the concepts to practice problems
- Discuss your findings and questions in course forums
Case Study Analysis Report
Show steps
Apply association rule mining and outlier detection techniques to solve a real-world problem and showcase your understanding of these concepts.
Browse courses on
Association Rule Mining
Show steps
Show steps
Show steps
- Identify a suitable dataset for analysis
- Apply association rule mining to extract insights and identify trends
- Utilize outlier detection methods to identify and analyze anomalous data points
- Prepare a comprehensive report summarizing your findings and insights
Career center
Learners who complete Association Rules Analysis will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use their knowledge of statistics, machine learning, and data mining to solve complex problems. They work in a variety of industries, including finance, healthcare, and retail. Association Rules Analysis can help aspiring data scientists build a strong theoretical foundation in unsupervised learning, specifically in the areas of association rules and outlier detection. By learning how to identify patterns and relationships in data, they can become more effective at solving problems and making data-driven decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data. They work in a variety of industries, including investment banking, hedge funds, and asset management. Association Rules Analysis can be a useful course for aspiring quantitative analysts, as it teaches them how to find patterns and relationships in data. This knowledge can help them develop more accurate and profitable trading strategies.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, build, and maintain machine learning models. They work closely with data scientists to ensure that models are accurate and efficient. Association Rules Analysis can be a helpful course for machine learning engineers, as it teaches them how to find patterns and relationships in data. This knowledge can help them improve the accuracy and efficiency of their models.
Data Engineer
Data Engineer
Data engineers design, build, and maintain data pipelines. They work closely with data scientists and other data professionals to ensure that data is clean, accurate, and accessible. Association Rules Analysis can be a useful course for aspiring data engineers, as it teaches them how to find patterns and relationships in data. This knowledge can help them design and build more efficient and effective data pipelines.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of industries, including finance, healthcare, and government. Association Rules Analysis can be a useful course for aspiring statisticians, as it teaches them how to find patterns and relationships in data. This knowledge can help them become more effective at analyzing data and making informed decisions.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and analytical techniques to solve problems in a variety of industries, including manufacturing, transportation, and healthcare. Association Rules Analysis can be a useful course for aspiring operations research analysts, as it teaches them how to find patterns and relationships in data. This knowledge can help them identify opportunities for improvement and make better decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of industries, including insurance, pensions, and healthcare. Association Rules Analysis can be a useful course for aspiring actuaries, as it teaches them how to find patterns and relationships in data. This knowledge can help them better understand and manage risk.
Data Analyst
Data Analyst
Data analysts sift through large amounts of data to find patterns and make recommendations. This work requires knowledge of unsupervised learning, data mining, statistics, and predictive modeling. Association Rules Analysis teaches the fundamentals of unsupervised learning, with an emphasis on making discoveries in data. As a result, this course may help an aspiring data analyst prepare for their career by building a foundation in association rules and outlier detection techniques.
Information Security Analyst
Information Security Analyst
Information security analysts protect computer systems and networks from cyberattacks. They work in a variety of industries, including finance, healthcare, and government. Association Rules Analysis can be a useful course for aspiring information security analysts, as it teaches them how to find patterns and relationships in data. This knowledge can help them identify and prevent cyberattacks.
Risk Analyst
Risk Analyst
Risk analysts identify and assess risks to organizations. They work in a variety of industries, including finance, insurance, and healthcare. Association Rules Analysis can be a useful course for aspiring risk analysts, as it teaches them how to find patterns and relationships in data. This knowledge can help them identify potential risks and develop mitigation strategies.
Market Researcher
Market Researcher
Market researchers collect and analyze data about consumers and markets. They use their findings to help companies develop and market products and services. Association Rules Analysis can be a useful course for aspiring market researchers, as it teaches them how to find patterns and relationships in data. This knowledge can help them understand consumer behavior and make better marketing decisions.
Business Analyst
Business Analyst
Business analysts help companies make better decisions by analyzing data. They use their skills in data analysis, statistics, and modeling to identify trends, patterns, and opportunities. The Association Rules Analysis course can be a useful tool for business analysts, as it teaches them how to find patterns and relationships in data. This knowledge can help them make better recommendations and improve the efficiency of their work.
Database Administrator
Database Administrator
Database administrators manage and maintain databases. They work in a variety of industries, including finance, healthcare, and retail. Association Rules Analysis can be a useful course for aspiring database administrators, as it teaches them how to find patterns and relationships in data. This knowledge can help them optimize database performance and ensure data integrity.
Financial Analyst
Financial Analyst
Financial analysts evaluate the financial performance of companies and make recommendations to investors. They use their knowledge of finance, accounting, and economics to identify undervalued stocks and make investment decisions. Association Rules Analysis can be a useful course for aspiring financial analysts, as it teaches them how to find patterns and relationships in data. This knowledge can help them make better investment decisions and improve the performance of their portfolios.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. They work in a variety of industries, including finance, healthcare, and technology. While a software engineer does not need an academic background in unsupervised learning, the skills one learns in Association Rules Analysis, such as data mining and pattern recognition, can be quite useful in certain technology development roles. This course may be particularly helpful for software engineers who are interested in working on data-intensive projects or developing machine learning applications.
For more career information including salaries, visit:
OpenCourser.com/course/h7xvvk/association
Reading list
We've selected six 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
Association Rules Analysis.
Provides a comprehensive overview of frequent pattern mining, covering both theoretical and practical aspects. It valuable reference for researchers and practitioners in the field.
Save
Provides a comprehensive overview of data mining techniques. It valuable reference for researchers and practitioners in the field.
Save
Provides a practical guide to data mining techniques. It valuable resource for practitioners who want to learn more about this topic.
Provides a thought-provoking look at the role of outliers in the world. It valuable read for anyone who is interested in this topic.
Save
Provides a comprehensive overview of extreme value theory. It valuable resource for researchers and practitioners who want to learn more about this topic.
Provides a comprehensive overview of statistical methods for anomaly detection. It valuable resource for researchers and practitioners who want to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h7xvvk/association
Share
Similar courses
Here are nine courses similar to
Association Rules Analysis.
Data Analysis with Python Project
OpenCourser.com/course/b3nagq/data
Data Analysis with Python Project
Most relevant
Advanced analysis of outliers in R and Matlab
OpenCourser.com/course/szhv96/advanced
Advanced analysis of outliers in R and Matlab
Most relevant
Preparing Data for Modeling with scikit-learn
OpenCourser.com/course/zikm2d/preparing
Preparing Data for Modeling with scikit-learn
Most relevant
Evaluating a Data Mining Model
OpenCourser.com/course/wn697s/evaluating
Evaluating a Data Mining Model
Most relevant
Data Mining and the Analytics Workflow
OpenCourser.com/course/ofz017/data
Data Mining and the Analytics Workflow
Most relevant
Data Mining Methods
OpenCourser.com/course/3qqgu7/data
Data Mining Methods
Most relevant
Cluster Analysis, Association Mining, and Model Evaluation
OpenCourser.com/course/hz1gmp/cluster
Cluster Analysis, Association Mining, and Model Evaluation
Most relevant
Coping with Missing, Invalid, and Duplicate Data in R
OpenCourser.com/course/txqm5b/coping
Coping with Missing, Invalid, and Duplicate Data in R
Most relevant
More Data Mining with R
OpenCourser.com/course/v413lh/more
More Data Mining with R
Most relevant
Effort
5-7 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Di Wu
Language
English
This course belongs to a
collection
called
Data Analysis with Python. It's comprised of five courses:
- Association Rules Analysis (viewing)
- Data Analysis with Python Project
- Classification Analysis
- Clustering Analysis
- Regression Analysis
Good to know
Explores unsupervised learning techniques, focusing on association rules and outlier detection
Covers essential concepts, including frequent itemsets, association rules, and outlier detection methods
Provides examples of Apriori algorithms and constraint-based association rule mining for practical implementation
Includes hands-on tutorials and case studies for practical experience in applying these techniques
Suitable for learners seeking a foundational understanding of unsupervised learning methods, particularly association rules and outlier detection
Assumes basic knowledge of data analysis and data mining principles
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