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Julie Pai
Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will begin with an exploration of cluster analysis and segmentation, and discuss how techniques such as collaborative filtering and association rules mining can be applied. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them.
Register for this course and see more details by visiting:
OpenCourser.com/course/hz1gmp/cluster
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What's inside
Syllabus
Cluster Analysis and Segmentation
Welcome to Module 1, Cluster Analysis and Segmentation. In this module we will explore cluster analysis, a popular unsupervised learning algorithm. We will also review the two major styles of cluster analysis, and discuss potential applications to different industries.
Read more
Syllabus
Cluster Analysis and Segmentation
Welcome to Module 1, Cluster Analysis and Segmentation. In this module we will explore cluster analysis, a popular unsupervised learning algorithm. We will also review the two major styles of cluster analysis, and discuss potential applications to different industries.
Read more
Collaborative Filtering, Association Rules Mining (Market Basked Analysis)
Welcome to Module 2, Collaborative Filtering, Association Rules Mining, & Market Basket Analysis. In this module we will begin with an explanation of collaborative filtering and association rules mining, and how these techniques are used to make automatic predictions. We will also take a closer look at the various common applications of market basket analysis.
Classification-Type Prediction Models
Welcome to Module 3, Classification-Type Prediction Models. In this module we will begin with an explanation of how classification-type prediction models are evaluated for performance, and how a confusion matrix can help visualize that performance. We will also discuss the applicability of cluster analysis, and how it can be used to detect rare events such as fraudulent transactions.
Regression-Type Prediction Models
Welcome to Module 4, Regression-Type Prediction Models. In this module we will review how regression analytics are used for both hypothesis testing and prediction, and how a scatter plot can be leveraged to better understand the relationship between two variables. We will also discuss the differences between correlation analysis and a regression analysis, and a look at simple vs multiple regression.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores industry standard techniques and methods
Taught by Julie Pai, an expert in the field of cluster analysis and data mining
Develops skills in cluster analysis, which are core skills for data analysis and business intelligence
Provides hands-on labs and interactive materials
May require prior knowledge of data analysis and statistics
Save this course
Save Cluster Analysis, Association Mining, and Model Evaluation to your list so you can find it easily later:
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Reviews summary
Well-received statistics course
Learners largely praise this introductory-level statistics course. Students who know statistics beforehand or who are familiar with data mining will find the content easy to follow and well explained. Others may find that topics are covered at too high of a level and may miss helpful information.
Introductory and easy to follow
"This course is fairly easy if you know something about statistics for data mining already."
"So well explained and put into practice."
"Well explained topics & also further reading suggestions are given, which is a bonus."
Too high of a level for some
"For me there was a lot of info missing."
"Really high level course"
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 Cluster Analysis, Association Mining, and Model Evaluation with these
activities:
Review Relevant Concepts
Show steps
Sharpen your understanding of the fundamental concepts of unsupervised learning and cluster analysis, which will serve as a strong foundation for this course.
Browse courses on
Unsupervised Learning
Show steps
-
Revise materials from previous courses on machine learning and data mining.
-
Review online resources such as tutorials and articles to refresh your knowledge of unsupervised learning algorithms.
Review cluster analysis
Show steps
Review the basics of cluster analysis to strengthen foundational understanding and prepare for course content.
Browse courses on
Cluster Analysis
Show steps
-
Refer to online tutorials or textbooks to refresh memory on key concepts
-
Summarize the different types of cluster analysis techniques
-
Identify real-world examples where cluster analysis is applied
Read An Introduction to Statistical Learning with Applications in R
Show steps
Familiarize yourself with regression analysis and associated statistical modeling techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the book's introduction and skim Chapter 1.
-
Attempt the end of chapter exercises from Chapter 1.
-
Read Chapters 2 and 3.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Practice data cleaning and exploratory analysis in R
Show steps
Refine your data analysis skills and proficiency in R.
Browse courses on
Exploratory Analysis
Show steps
-
Fork this repo on Github: https://github.com/fivethirtyeight/data.
-
Download the 'checking-a-sports-hypothesis' folder from the repo.
-
Follow along with the data cleaning and exploratory analysis steps found in the 'checking-a-sports-hypothesis' readme.md.
Explore Cluster Analysis Applications
Show steps
Gain practical insights into how cluster analysis and related techniques are applied in real-world scenarios.
Show steps
-
Identify and study case studies where cluster analysis was successfully employed.
-
Follow online tutorials that demonstrate the implementation of collaborative filtering and association rules mining algorithms.
Solve Classification Problems
Show steps
Strengthen your understanding of classification-type prediction models by solving practice problems. This will improve your ability to apply these techniques effectively.
Browse courses on
Classification Problems
Show steps
-
Access online platforms or textbooks that offer practice problems on classification.
-
Solve problems involving decision trees, support vector machines, and other classification algorithms.
Classify data points using K-Means clustering
Show steps
Reinforce understanding of K-Means clustering by applying it to sample datasets.
Browse courses on
K-Means Clustering
Show steps
-
Use online platforms or software with preloaded datasets
-
Implement K-Means clustering algorithm in a programming language
-
Evaluate the results for different values of k
Gather resources on association mining techniques
Show steps
Expand your knowledge and understanding of association mining techniques.
Show steps
-
Search for articles, tutorials, and other resources on association mining techniques.
-
Organize the gathered resources into a document or online repository.
-
Review the resources and synthesize the key concepts of association mining.
Form a study group to review course concepts and assignments
Show steps
Enhance your understanding of course material through peer collaboration and feedback.
Browse courses on
Collaboration
Show steps
-
Identify 2-3 classmates who are interested in forming a study group.
-
Schedule regular meetings to review course material, discuss assignments, and quiz each other.
-
Take turns leading the study sessions and presenting key concepts.
Discuss Regression Analysis Concepts
Show steps
Engage with peers to discuss concepts and applications of regression analysis. This will provide diverse perspectives and enhance your understanding.
Browse courses on
Regression Analysis
Show steps
-
Join or create a study group with classmates.
-
Facilitate discussions on topics such as linear regression, multiple regression, and hypothesis testing.
Attend a workshop on cluster analysis techniques
Show steps
Gain hands-on experience with cluster analysis techniques.
Browse courses on
Cluster Analysis
Show steps
-
Research and locate a workshop on cluster analysis techniques.
-
Register and attend the workshop.
-
Apply the techniques learned in the workshop to a real-world dataset.
Summarize Model Evaluation Techniques
Show steps
Deepen your understanding of model evaluation techniques by creating a concise summary of different approaches, their applications, and strengths and weaknesses.
Browse courses on
Model Evaluation
Show steps
-
Research and gather information on various model evaluation metrics, such as accuracy, precision, and recall.
-
Create a presentation or written document that outlines the key concepts and examples of each technique.
Create a visualization to illustrate market basket analysis concepts
Show steps
Enhance comprehension of market basket analysis by creating visualizations that demonstrate key principles.
Browse courses on
Market Basket Analysis
Show steps
-
Gather data and identify common item combinations
-
Use visualization tools to create charts or diagrams
-
Explain the insights derived from the visualization
Develop a Cluster Analysis Plan
Show steps
Apply your knowledge of cluster analysis by developing a comprehensive plan for segmenting a given dataset. This will enhance your practical skills and decision-making abilities.
Browse courses on
Data Segmentation
Show steps
-
Define the problem statement and objectives of the cluster analysis.
-
Select and prepare the relevant data for analysis.
-
Choose appropriate clustering algorithms and justify your choices.
-
Evaluate the results and interpret the clusters formed.
Attend a workshop on model evaluation techniques
Show steps
Gain practical experience in evaluating the performance of predictive models.
Browse courses on
Model Evaluation
Show steps
-
Research and locate a workshop on model evaluation techniques.
-
Register and attend the workshop.
-
Apply the techniques learned in the workshop to evaluate a predictive model.
Create a presentation on a real-world application of cluster analysis
Show steps
Apply your understanding of cluster analysis to a practical problem.
Browse courses on
Presentation
Show steps
-
Research and identify a real-world business or research problem that can be solved using cluster analysis.
-
Gather and analyze relevant data.
-
Apply cluster analysis techniques to identify patterns and segments within the data.
-
Develop a presentation that clearly communicates your findings and insights.
Review Relevant Concepts
Show steps
Sharpen your understanding of the fundamental concepts of unsupervised learning and cluster analysis, which will serve as a strong foundation for this course.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Revise materials from previous courses on machine learning and data mining.
- Review online resources such as tutorials and articles to refresh your knowledge of unsupervised learning algorithms.
Review cluster analysis
Show steps
Review the basics of cluster analysis to strengthen foundational understanding and prepare for course content.
Browse courses on
Cluster Analysis
Show steps
Show steps
Show steps
- Refer to online tutorials or textbooks to refresh memory on key concepts
- Summarize the different types of cluster analysis techniques
- Identify real-world examples where cluster analysis is applied
Read An Introduction to Statistical Learning with Applications in R
Show steps
Familiarize yourself with regression analysis and associated statistical modeling techniques.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and skim Chapter 1.
- Attempt the end of chapter exercises from Chapter 1.
- Read Chapters 2 and 3.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Practice data cleaning and exploratory analysis in R
Show steps
Refine your data analysis skills and proficiency in R.
Browse courses on
Exploratory Analysis
Show steps
Show steps
Show steps
- Fork this repo on Github: https://github.com/fivethirtyeight/data.
- Download the 'checking-a-sports-hypothesis' folder from the repo.
- Follow along with the data cleaning and exploratory analysis steps found in the 'checking-a-sports-hypothesis' readme.md.
Explore Cluster Analysis Applications
Show steps
Gain practical insights into how cluster analysis and related techniques are applied in real-world scenarios.
Show steps
Show steps
Show steps
- Identify and study case studies where cluster analysis was successfully employed.
- Follow online tutorials that demonstrate the implementation of collaborative filtering and association rules mining algorithms.
Solve Classification Problems
Show steps
Strengthen your understanding of classification-type prediction models by solving practice problems. This will improve your ability to apply these techniques effectively.
Browse courses on
Classification Problems
Show steps
Show steps
Show steps
- Access online platforms or textbooks that offer practice problems on classification.
- Solve problems involving decision trees, support vector machines, and other classification algorithms.
Classify data points using K-Means clustering
Show steps
Reinforce understanding of K-Means clustering by applying it to sample datasets.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- Use online platforms or software with preloaded datasets
- Implement K-Means clustering algorithm in a programming language
- Evaluate the results for different values of k
Gather resources on association mining techniques
Show steps
Expand your knowledge and understanding of association mining techniques.
Show steps
Show steps
Show steps
- Search for articles, tutorials, and other resources on association mining techniques.
- Organize the gathered resources into a document or online repository.
- Review the resources and synthesize the key concepts of association mining.
Form a study group to review course concepts and assignments
Show steps
Enhance your understanding of course material through peer collaboration and feedback.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Identify 2-3 classmates who are interested in forming a study group.
- Schedule regular meetings to review course material, discuss assignments, and quiz each other.
- Take turns leading the study sessions and presenting key concepts.
Discuss Regression Analysis Concepts
Show steps
Engage with peers to discuss concepts and applications of regression analysis. This will provide diverse perspectives and enhance your understanding.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Join or create a study group with classmates.
- Facilitate discussions on topics such as linear regression, multiple regression, and hypothesis testing.
Attend a workshop on cluster analysis techniques
Show steps
Gain hands-on experience with cluster analysis techniques.
Browse courses on
Cluster Analysis
Show steps
Show steps
Show steps
- Research and locate a workshop on cluster analysis techniques.
- Register and attend the workshop.
- Apply the techniques learned in the workshop to a real-world dataset.
Summarize Model Evaluation Techniques
Show steps
Deepen your understanding of model evaluation techniques by creating a concise summary of different approaches, their applications, and strengths and weaknesses.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Research and gather information on various model evaluation metrics, such as accuracy, precision, and recall.
- Create a presentation or written document that outlines the key concepts and examples of each technique.
Create a visualization to illustrate market basket analysis concepts
Show steps
Enhance comprehension of market basket analysis by creating visualizations that demonstrate key principles.
Browse courses on
Market Basket Analysis
Show steps
Show steps
Show steps
- Gather data and identify common item combinations
- Use visualization tools to create charts or diagrams
- Explain the insights derived from the visualization
Develop a Cluster Analysis Plan
Show steps
Apply your knowledge of cluster analysis by developing a comprehensive plan for segmenting a given dataset. This will enhance your practical skills and decision-making abilities.
Browse courses on
Data Segmentation
Show steps
Show steps
Show steps
- Define the problem statement and objectives of the cluster analysis.
- Select and prepare the relevant data for analysis.
- Choose appropriate clustering algorithms and justify your choices.
- Evaluate the results and interpret the clusters formed.
Attend a workshop on model evaluation techniques
Show steps
Gain practical experience in evaluating the performance of predictive models.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Research and locate a workshop on model evaluation techniques.
- Register and attend the workshop.
- Apply the techniques learned in the workshop to evaluate a predictive model.
Create a presentation on a real-world application of cluster analysis
Show steps
Apply your understanding of cluster analysis to a practical problem.
Browse courses on
Presentation
Show steps
Show steps
Show steps
- Research and identify a real-world business or research problem that can be solved using cluster analysis.
- Gather and analyze relevant data.
- Apply cluster analysis techniques to identify patterns and segments within the data.
- Develop a presentation that clearly communicates your findings and insights.
Career center
Learners who complete Cluster Analysis, Association Mining, and Model Evaluation will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical and statistical models for a variety of financial applications. This course provides the foundational knowledge and technical skills required for success as a Quantitative Analyst, including a focus on cluster analysis and association mining methods. This course will be especially useful for Quantitative Analysts working in risk management or portfolio optimization.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of business processes. This course will be especially useful to Operations Research Analysts working on problems involving data analysis and optimization, often using cluster analysis and association mining techniques.
Data Analyst
Data Analyst
Data Analysts leverage their knowledge of data analysis methodologies, and their ability to communicate insights effectively, to help businesses make more informed decisions. This course's focus on cluster analysis, association mining, and model evaluation is directly applicable to the work of a Data Analyst. Topics such as collaborative filtering and market basket analysis are especially relevant to Analysts working in retail or marketing.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models for a variety of purposes. This course provides Machine Learning Engineers with the foundational knowledge and technical skills required for success in the field, including a focus on cluster analysis and association mining techniques. This course will be especially useful for Machine Learning Engineers working on unsupervised learning applications.
Financial Analyst
Financial Analyst
Financial Analysts help businesses make sound investment decisions through the application of statistical and quantitative analysis. Cluster analysis and association mining techniques can help a Financial Analyst to build models to predict market trends and identify undervalued assets.
Actuary
Actuary
Actuaries evaluate and manage financial risks for insurance companies. This course can provide Actuaries with a solid foundation in the quantitative and statistical analysis techniques used in their field. Additionally, Actuaries may find knowledge of cluster analysis and association mining techniques especially valuable when building predictive models for insurance premiums or risk assessment.
Data Scientist
Data Scientist
Data Scientists play a vital role in the development and deployment of machine learning models. The techniques taught in this course, like cluster analysis, will be essential for any aspiring Data Scientist. Analysis of consumer behavior, for example, is a task that Data Scientists can complete using the very methods taught in this course.
Data Engineer
Data Engineer
Data Engineers are responsible for the design, development, and maintenance of data systems. This course provides Data Engineers with a solid foundation in the techniques used for data analysis and model evaluation. They may also find knowledge of cluster analysis and association mining techniques helpful for tasks like data preprocessing and feature engineering.
Market Researcher
Market Researcher
Market Researchers seek to understand the needs and wants of consumers. Applying methods like cluster analysis and association mining can help them to better understand consumer behavior. A Market Researcher can use these insights to help businesses make more informed decisions about product development and marketing strategies.
Marketing Manager
Marketing Manager
Marketing Managers develop and implement marketing campaigns to promote products or services. By providing them with a solid understanding of quantitative techniques for data analysis and model evaluation, this course can help Marketing Managers make more informed decisions about target markets, marketing channels, and campaign effectiveness. Additionally, knowledge of cluster analysis and association mining can be used to gain insights into consumer behavior and purchasing patterns.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software applications. While a background in data analysis is not always required, Software Engineers who are responsible for developing data-driven applications may find this course to be a valuable resource. The focus on cluster analysis and association mining will be especially useful for Software Engineers working on recommendation systems or predictive analytics applications.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and features. This course can be a useful resource for Product Managers who wish to build a deeper understanding of the quantitative techniques used to analyze customer data and evaluate product performance. Knowledge of cluster analysis and association mining, in particular, can provide insights into consumer preferences and trends.
Business Analyst
Business Analyst
A Business Analyst is responsible for gathering and analyzing data in order to make recommendations for improving business processes. By providing them with practical examples of these concepts and mathematical techniques, this course can be a useful companion to a Business Analyst's toolkit. This course will be especially useful for Business Analysts working in retail, finance, or manufacturing.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks for their organizations. By providing them with a deep understanding of quantitative techniques for data analysis, this course may prove helpful for aspiring Risk Managers. Knowledge of cluster analysis and association mining is especially useful when evaluating operational risk or fraud risk.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights for a variety of purposes. This course is an excellent way for Statisticians to refresh and update their knowledge of core statistical techniques. The focus on model evaluation, in particular, will be especially valuable.
For more career information including salaries, visit:
OpenCourser.com/course/hz1gmp/cluster
Reading list
We've selected 13 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
Cluster Analysis, Association Mining, and Model Evaluation.
Save
Provides a comprehensive overview of deep learning, a type of machine learning that is used to solve a variety of problems. This book good resource for students who are interested in learning more about deep learning.
Save
Provides an introduction to generative adversarial networks, a type of machine learning that is used to generate new data. This book good resource for students who are interested in learning more about generative adversarial networks.
Provides a comprehensive overview of association rule mining, including algorithms and applications. This book is good for students who want to learn more about the different association rule mining algorithms and how they can be used in practice.
Comprehensive guide to statistical learning methods, including clustering and association mining. It would be helpful for students who want to learn more about the theoretical foundations of these methods. However, this book is more advanced and may be better used as a reference.
Save
Good introduction to predictive modeling concepts and techniques. This book is helpful for students who want to learn more about the different predictive modeling algorithms and how they can be used in practice.
Provides a practical introduction to machine learning using Python. This book good resource for students who are interested in learning more about how to apply machine learning to real-world problems.
Provides an introduction to reinforcement learning, a type of machine learning that is used to learn how to make decisions in an environment. This book good resource for students who are interested in learning more about reinforcement learning.
Save
Provides an overview of computer vision, a type of machine learning that is used to understand and generate visual data. This book good resource for students who are interested in learning more about computer vision.
Good introduction to regression analysis, which type of predictive modeling. This book is helpful for students who want to learn more about the different regression models and how they can be used in practice.
Provides an introduction to natural language processing, a type of machine learning that is used to understand and generate human language. This book good resource for students who are interested in learning more about natural language processing.
Good introduction to model evaluation concepts and techniques. This book is helpful for students who want to learn more about how to evaluate the performance of their models.
Provides an overview of data mining and data analytics for business professionals. This good book for students who are interested in learning more about how data mining and data analytics can be used in business.
Good introduction to data mining concepts and techniques, including clustering and association mining. This book would be a good starting point for students who are new to data mining. This book is clearly written and provides examples to make the content easier to understand.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hz1gmp/cluster
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Level
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Via
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Institution
University of California, Irvine
Instructor
Julie Pai
Language
English
Transcripts
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This course belongs to a
collection
called
Data Science Fundamentals. It's comprised of four courses:
- Predictive Modeling, Model Fitting, and Regression Analysis
- Natural Language Processing and Capstone Assignment
- Intro to Analytic Thinking, Data Science, and Data Mining
- Cluster Analysis, Association Mining, and Model Evaluation (viewing)
Good to know
Explores industry standard techniques and methods
Taught by Julie Pai, an expert in the field of cluster analysis and data mining
Develops skills in cluster analysis, which are core skills for data analysis and business intelligence
Provides hands-on labs and interactive materials
May require prior knowledge of data analysis and statistics
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