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James Bird
Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!
Read more
Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!
Read more
Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Statistical Learning Introduction
Introduction to overarching and foundational concepts in Statistical Learning.
Accuracy
Exploration into assessing models in different situations. How do we define a "best" model for given data?
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Syllabus
Statistical Learning Introduction
Introduction to overarching and foundational concepts in Statistical Learning.
Accuracy
Exploration into assessing models in different situations. How do we define a "best" model for given data?
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores core concepts in Statistical Learning, such as supervised and unsupervised learning, model selection, and performance evaluation
Covers a wide range of topics, including regression, classification, trees, resampling, and unsupervised techniques
Provides hands-on experience with statistical modeling using the R programming language
Taught by experienced instructors from the University of Colorado Boulder who are actively involved in research and practice in statistical learning
Offers the opportunity to earn academic credit as part of the University of Colorado Boulder's Master of Science in Data Science degree
Requires a strong foundation in statistics and mathematics
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Reviews summary
Foundational statistical learning for data science
According to learners, this course offers a solid introduction to fundamental statistical learning concepts like regression and classification. It is seen as a well-structured program from the University of Colorado Boulder, ideal for those starting a data science career or pursuing an MS-DS degree. Students appreciate the focus on understanding when and how to apply models, which is crucial for practical work. While it provides a strong conceptual foundation, some learners with prior experience might find the depth not sufficient for advanced applications. It is generally regarded as a rigorous first step in mastering statistical modeling.
Potential for varying opinions on the balance of theory and hands-on work.
"The course felt quite theoretical at times; I would have loved more coding exercises and practical labs."
"I appreciated the strong theoretical grounding, which helps understand the 'why' behind the models, not just the 'how'."
"While the theory was well-explained, I needed to supplement with external resources for more hands-on coding practice."
Well-suited for newcomers to data science or MS-DS program candidates.
"Perfect for someone like me who's relatively new to data science and looking to get a strong start in statistical learning."
"Joining this course as part of the MS-DS program, I found it perfectly aligned with the foundational knowledge required."
"I didn't have much prior experience, and this course guided me through the basics without overwhelming me."
Emphasizes effective application and tuning of statistical models.
"The lectures didn't just teach theory; they really showed me when and how to use different models, which is exactly what I needed."
"I found the discussions on model tuning and trade-offs incredibly useful for my data science projects."
"This course helped me connect theoretical knowledge to practical application in statistical analysis."
Delivers a robust and clear introduction to core statistical learning.
"I really appreciate how this course laid out the basic concepts of statistical learning clearly. It helped me build a solid base."
"The way they explained regression and classification models made complex ideas much easier to grasp for a beginner like me."
"As an introduction, this course provided an excellent understanding of how to approach statistical modeling problems."
Might not cover enough advanced detail for experienced practitioners.
"While good for beginners, I already knew much of the material and wished it went into more advanced techniques."
"I was hoping for deeper dives into specific algorithms or optimization methods, but it stayed at an introductory level."
"For someone with prior exposure to statistical modeling, this course might feel a bit too basic."
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 Regression and Classification with these
activities:
Review 'Introduction to Statistical Learning' by Gareth James et al.
Show steps
Gain a deeper understanding of the course concepts by reviewing this recommended text.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes on the key concepts.
-
Work through the practice problems.
Review basic probability and statistics
Show steps
Review these foundational concepts to ensure you have a strong base before starting the course.
Browse courses on
Probability
Show steps
-
Review your notes from a previous probability and statistics course.
-
Take practice problems on probability and statistics.
-
Watch online tutorials or videos on probability and statistics.
Participate in peer study groups
Show steps
Enhance your understanding by collaborating with peers and engaging in discussions.
Browse courses on
Collaboration
Show steps
-
Form a study group with other students in the course.
-
Meet regularly to discuss the course material.
-
Work together on practice problems and projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on linear regression
Show steps
Supplement your understanding of linear regression by following guided tutorials.
Browse courses on
Linear Regression
Show steps
-
Find online tutorials on linear regression.
-
Follow the steps in the tutorials to learn how to perform linear regression.
-
Practice using the techniques you learn in the tutorials.
Complete practice problems on classification models
Show steps
Strengthen your comprehension of classification models through practice problems.
Browse courses on
Classification Models
Show steps
-
Find practice problems on classification models.
-
Solve the practice problems.
-
Review your solutions and identify areas where you need improvement.
Mentor junior students in statistical learning
Show steps
Strengthen your understanding by teaching others and providing guidance to junior students.
Browse courses on
Mentoring
Show steps
-
Identify junior students who need assistance in statistical learning.
-
Offer your help and support.
-
Provide guidance and feedback on their work.
Start a project on data visualization
Show steps
Apply your knowledge of statistical learning to a practical project involving data visualization.
Browse courses on
Data Visualization
Show steps
-
Identify a dataset to visualize.
-
Choose appropriate data visualization techniques.
-
Create a data visualization using the techniques you chose.
-
Interpret the results of your data visualization.
Create a presentation on a selected statistical learning technique
Show steps
Solidify your learning by creating a presentation and sharing it with peers or instructors.
Browse courses on
Presentation
Show steps
-
Choose a statistical learning technique to present on.
-
Research the technique and gather information.
-
Create a presentation that explains the technique clearly.
-
Practice your presentation.
-
Deliver your presentation to your peers or instructors.
Review 'Introduction to Statistical Learning' by Gareth James et al.
Show steps
Gain a deeper understanding of the course concepts by reviewing this recommended text.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes on the key concepts.
- Work through the practice problems.
Review basic probability and statistics
Show steps
Review these foundational concepts to ensure you have a strong base before starting the course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review your notes from a previous probability and statistics course.
- Take practice problems on probability and statistics.
- Watch online tutorials or videos on probability and statistics.
Participate in peer study groups
Show steps
Enhance your understanding by collaborating with peers and engaging in discussions.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Form a study group with other students in the course.
- Meet regularly to discuss the course material.
- Work together on practice problems and projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on linear regression
Show steps
Supplement your understanding of linear regression by following guided tutorials.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find online tutorials on linear regression.
- Follow the steps in the tutorials to learn how to perform linear regression.
- Practice using the techniques you learn in the tutorials.
Complete practice problems on classification models
Show steps
Strengthen your comprehension of classification models through practice problems.
Browse courses on
Classification Models
Show steps
Show steps
Show steps
- Find practice problems on classification models.
- Solve the practice problems.
- Review your solutions and identify areas where you need improvement.
Mentor junior students in statistical learning
Show steps
Strengthen your understanding by teaching others and providing guidance to junior students.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify junior students who need assistance in statistical learning.
- Offer your help and support.
- Provide guidance and feedback on their work.
Start a project on data visualization
Show steps
Apply your knowledge of statistical learning to a practical project involving data visualization.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset to visualize.
- Choose appropriate data visualization techniques.
- Create a data visualization using the techniques you chose.
- Interpret the results of your data visualization.
Create a presentation on a selected statistical learning technique
Show steps
Solidify your learning by creating a presentation and sharing it with peers or instructors.
Browse courses on
Presentation
Show steps
Show steps
Show steps
- Choose a statistical learning technique to present on.
- Research the technique and gather information.
- Create a presentation that explains the technique clearly.
- Practice your presentation.
- Deliver your presentation to your peers or instructors.
Career center
Learners who complete Regression and Classification will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
"Data Scientists" interpret data and use statistical techniques to build predictive models. This course helps build a foundation for a career in this role. It teaches how to use multiple models to make predictions on data. The course teaches techniques like linear regression that a "Data Scientist" needs to use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill commonly used by "Data Scientists".
Machine Learning Engineer
Machine Learning Engineer
"Machine Learning Engineers" build and maintain machine learning systems. This course helps build a foundation for a career in this role. It teaches how to use statistical models to represent data. The course teaches techniques like linear regression that a "Machine Learning Engineer" needs to use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill commonly used by "Machine Learning Engineers".
Data Analyst
Data Analyst
"Data Analysts" clean, analyze, and present data. This course helps build a foundation for a career in this role. It teaches how to evaluate different statistical models and techniques. The course teaches techniques like linear regression that a "Data Analyst" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Data Analysts".
Statistician
Statistician
"Statisticians" develop and implement complex statistical tests. This course helps build a foundation for a career in this role. It teaches how to use different statistical models and techniques. The course teaches techniques like linear regression that a "Statistician" needs to use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Statisticians".
Business Analyst
Business Analyst
"Business Analysts" analyze data to help businesses make better decisions. This course helps build a foundation for a career in this role. It teaches how to apply statistical models to business problems. The course covers topics like linear regression, which a "Business Analyst" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Business Analysts".
Quantitative Analyst
Quantitative Analyst
"Quantitative Analysts" use mathematical and statistical models to assess risk. This course helps build a foundation for a career in this role. It teaches how to develop and implement statistical models. The course covers topics like linear regression and classification, which a "Quantitative Analyst" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Quantitative Analysts".
Software Engineer
Software Engineer
"Software Engineers" design, develop, and maintain software systems. This course helps build a foundation for a career in this role. It teaches how to use statistical models to improve software performance. The course covers topics like machine learning and classification, which a "Software Engineer" needs to understand. It can help you take the next step in your career.
Product Manager
Product Manager
"Product Managers" lead the development and launch of new products. This course helps build a foundation for a career in this role. It teaches how to use statistical models to understand customer needs. The course covers topics like linear regression, which a "Product Manager" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Product Managers".
Actuary
Actuary
"Actuaries" evaluate risk and uncertainty in financial and insurance industries. This course helps build a foundation for a career in this role. It teaches how to use statistical models to develop insurance policies. The course covers topics like linear regression and classification, which an "Actuary" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Actuaries".
Financial Analyst
Financial Analyst
"Financial Analysts" analyze financial data to make investment recommendations. This course helps build a foundation for a career in this role. It teaches how to use statistical models to assess financial risk. The course covers topics like linear regression, which a "Financial Analyst" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Financial Analysts".
Market Researcher
Market Researcher
"Market Researchers" conduct research to understand customer needs. This course helps build a foundation for a career in this role. It teaches how to use statistical models to analyze market data. The course covers topics like linear regression, which a "Market Researcher" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Market Researchers".
Operations Research Analyst
Operations Research Analyst
"Operations Research Analysts" use mathematical and statistical models to improve operational efficiency. This course helps build a foundation for a career in this role. It teaches how to use statistical models to solve operational problems. The course covers topics like linear regression and classification, which an "Operations Research Analyst" may use. It can help you take the next step in your career. The program also teaches the fundamentals of machine learning, which is a skill sometimes used by "Operations Research Analysts".
Data Engineer
Data Engineer
"Data Engineers" design and build systems to store and process data. This course helps build a foundation for a career in this role. It teaches how to use statistical models to optimize data systems. The course covers topics like machine learning and classification, which a "Data Engineer" needs to understand. It can help you take the next step in your career.
Software Developer
Software Developer
"Software Developers" design, develop, and maintain software applications. This course helps build a foundation for a career in this role. It teaches how to use statistical models to improve software performance. The course covers topics like machine learning and classification, which a "Software Developer" needs to understand. It can help you take the next step in your career.
Database Administrator
Database Administrator
"Database Administrators" design, build, and maintain databases. This course helps build a foundation for a career in this role. It teaches how to use statistical models to optimize database performance. The course covers topics like machine learning and classification, which a "Database Administrator" needs to understand. It can help you take the next step in your career.
For more career information including salaries, visit:
OpenCourser.com/course/t42sj1/regression
Reading list
We've selected nine 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
Regression and Classification.
Provides an excellent foundation for the concepts and techniques covered in the course. It offers a comprehensive overview of statistical learning methods, including regression, classification, and unsupervised learning.
An accessible and practical introduction to statistical learning methods. It covers topics such as regression, classification, and model selection, with a focus on applications in data science.
A comprehensive and authoritative textbook on machine learning. It provides a deep understanding of the theoretical foundations of regression and classification algorithms.
Presents a probabilistic approach to machine learning. It offers a rigorous treatment of regression and classification models from a Bayesian perspective.
Save
While not directly relevant to the course, it provides a valuable introduction to deep learning techniques, which are becoming increasingly important in statistical learning.
A comprehensive textbook on statistical methods. It provides a strong foundation for the statistical concepts and techniques used in regression and classification.
Provides a comprehensive overview of regression analysis methods. It covers both linear and nonlinear regression models, with a focus on practical applications.
Save
A broad overview of data mining techniques, including regression, classification, and clustering. It provides a good introduction to the concepts and methods used in statistical learning.
A thought-provoking and accessible introduction to statistical modeling. It provides a good foundation for understanding the concepts and assumptions underlying regression and classification models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t42sj1/regression
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Effort
Approx. 34 hours
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
James Bird
Language
English
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This course belongs to a
collection
called
Statistical Learning for Data Science. It's comprised of three courses:
- Regression and Classification (viewing)
- Resampling, Selection and Splines
- Trees, SVM and Unsupervised Learning
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