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Roger D. Peng, PhD,
Jeff Leek, PhD,
and
Brian Caffo, PhD
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Register for this course and see more details by visiting:
OpenCourser.com/course/mtu9ag/r
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What's inside
Syllabus
Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
Read more
Syllabus
Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
Read more
Week 2: Programming with R
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
Week 3: Loop Functions and Debugging
We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
Week 4: Simulation & Profiling
This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches practical statistical programming in R, which is standard in the data science industry
Taught by instructors who are recognized for their work in R programming and statistical computing
Develops programming skills and statistical data analysis techniques, which are core skills for data scientists
Covers loop functions and debugging, which are essential for writing efficient and reliable R code
Includes simulation and profiling, which are advanced techniques for optimizing and evaluating R code
Save this course
Save R Programming to your list so you can find it easily later:
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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 R Programming with these
activities:
Organize and review course materials regularly
Show steps
Maintaining organized and well-reviewed course materials will enhance your ability to recall and retain the information taught in the course.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
File and organize lecture notes, assignments, and other course resources.
-
Review your notes and materials periodically to reinforce your understanding.
-
Use flashcards or summaries to aid in memorization of key concepts.
Practice basic programming skills
Show steps
Sharpening your basic programming skills will make it easier to grasp the R programming concepts introduced in the course.
Show steps
-
Review fundamental programming concepts like variables, data types, and control flow.
-
Complete coding exercises or puzzles to reinforce your skills.
Review statistics concepts before starting the course
Show steps
Refreshing your knowledge of basic statistics concepts will provide a strong foundation for understanding the statistical analysis techniques covered in this course.
Show steps
-
Review your notes or textbooks from previous statistics courses.
-
Focus on concepts such as probability, distributions, and hypothesis testing.
-
Complete practice problems or online quizzes to test your understanding.
One other activity
Expand to see all activities and additional details
Show all four activities
Form a study group with peers
Show steps
Engaging in peer discussions and study sessions can reinforce your understanding, clarify concepts, and provide different perspectives on the course material.
Show steps
-
Identify a few peers who are also taking the course.
-
Schedule regular study sessions to discuss course topics.
-
Take turns presenting concepts, asking questions, and sharing insights.
-
Work together on practice problems and assignments.
Organize and review course materials regularly
Show steps
Maintaining organized and well-reviewed course materials will enhance your ability to recall and retain the information taught in the course.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- File and organize lecture notes, assignments, and other course resources.
- Review your notes and materials periodically to reinforce your understanding.
- Use flashcards or summaries to aid in memorization of key concepts.
Practice basic programming skills
Show steps
Sharpening your basic programming skills will make it easier to grasp the R programming concepts introduced in the course.
Show steps
Show steps
Show steps
- Review fundamental programming concepts like variables, data types, and control flow.
- Complete coding exercises or puzzles to reinforce your skills.
Review statistics concepts before starting the course
Show steps
Refreshing your knowledge of basic statistics concepts will provide a strong foundation for understanding the statistical analysis techniques covered in this course.
Show steps
Show steps
Show steps
- Review your notes or textbooks from previous statistics courses.
- Focus on concepts such as probability, distributions, and hypothesis testing.
- Complete practice problems or online quizzes to test your understanding.
One other activity
Expand to see all activities and additional details
Show all four activities
Form a study group with peers
Show steps
Engaging in peer discussions and study sessions can reinforce your understanding, clarify concepts, and provide different perspectives on the course material.
Show steps
Show steps
Show steps
- Identify a few peers who are also taking the course.
- Schedule regular study sessions to discuss course topics.
- Take turns presenting concepts, asking questions, and sharing insights.
- Work together on practice problems and assignments.
Career center
Learners who complete R Programming will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course can help you get started in this field by teaching you the basics of R programming, which is a widely-used language for data analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a great way to learn the skills you need to succeed as a Data Analyst.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. This course can help you build a foundation for a career in statistics by teaching you the basics of R programming, which is a popular language for statistical computing. You will learn how to perform statistical tests, create visualizations, and build models. This course is a valuable resource for anyone interested in a career in statistics.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. He/she creates predictive models and applies innovative techniques to solve business problems. This course can help you build a foundation for a career in data science by teaching you the basics of R programming, which is a popular language for data science. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a great way to learn the skills you need to succeed as a Data Scientist.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (QAs) develop and implement mathematical and statistical models to analyze financial data. This course can help you build a foundation for a career as a Quantitative Analyst by teaching you the basics of R programming, which is a popular language for financial modeling. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Quantitative Analyst.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to a wide range of topics in biology and medicine. This course can help you build a foundation for a career as a Biostatistician by teaching you the basics of R programming, which is a popular language for biostatistical analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career in biostatistics.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course can help you build a foundation for a career as a Machine Learning Engineer by teaching you the basics of R programming, which is a popular language for machine learning. You will learn how to read and write data, create visualizations, and build models. This course is a great way to learn the skills you need to succeed as a Machine Learning Engineer.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you build a foundation for a career as a Software Engineer by teaching you the basics of R programming, which is a popular language for software development. You will learn how to read and write data, create visualizations, and build models. This course is a valuable resource for anyone interested in a career in software engineering.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and systems. This course can help you build a foundation for a career as a Data Engineer by teaching you the basics of R programming, which is a popular language for data engineering. You will learn how to read and write data, create visualizations, and build models. This course is a valuable resource for anyone interested in a career in data engineering.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course can help you build a foundation for a career as a Business Analyst by teaching you the basics of R programming, which is a popular language for business analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use data to help businesses make informed financial decisions. This course can help you build a foundation for a career as a Financial Analyst by teaching you the basics of R programming, which is a popular language for financial analysis. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Financial Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course can help you build a foundation for a career as an Actuary by teaching you the basics of R programming, which is a popular language for actuarial science. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as an Actuary.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. This course can help you build a foundation for a career as an Epidemiologist by teaching you the basics of R programming, which is a popular language for epidemiology. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as an Epidemiologist.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about markets and consumers. This course can help you build a foundation for a career as a Market Researcher by teaching you the basics of R programming, which is a popular language for market research. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Market Researcher.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to improve the efficiency of organizations. This course can help you build a foundation for a career as an Operations Research Analyst by teaching you the basics of R programming, which is a popular language for operations research. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as an Operations Research Analyst.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks to organizations. This course can help you build a foundation for a career as a Risk Manager by teaching you the basics of R programming, which is a popular language for risk management. You will learn how to read and write data, create visualizations, and perform statistical analyses. This course is a valuable resource for anyone interested in a career as a Risk Manager.
For more career information including salaries, visit:
OpenCourser.com/course/mtu9ag/r
Reading list
We've selected 12 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
R Programming.
Covers a wide range of statistical learning methods, including linear and logistic regression, classification trees, support vector machines, and cluster analysis. It provides a good overview of the field and useful reference for both beginners and experienced practitioners.
Save
Comprehensive guide to using R for data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It good resource for both beginners and experienced R users.
Comprehensive guide to using R for data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It good resource for both beginners and experienced R users.
Provides a comprehensive guide to statistical analysis using R. It covers a wide range of topics, from data exploration and visualization to statistical modeling and hypothesis testing. It good resource for both beginners and experienced R users.
Save
Provides a comprehensive guide to using the ggplot2 package for data visualization. It covers a wide range of topics, from basic plots to complex visualizations. It valuable resource for both beginners and experienced R users.
Save
Comprehensive guide to the R programming language. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It good resource for both beginners and experienced R users.
Provides a comprehensive guide to machine learning using R. It covers a wide range of topics, from supervised learning to unsupervised learning. It good resource for both beginners and experienced R users.
Practical guide to using R for data analysis. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It good resource for both beginners and experienced R users.
Provides a comprehensive guide to data manipulation using R. It covers a wide range of topics, from data import and export to data cleaning and transformation. It valuable resource for both beginners and experienced R users.
More advanced treatment of statistical learning methods, covering topics such as regularization, cross-validation, and model selection. It valuable resource for researchers and practitioners who want to learn more about the theoretical foundations of statistical learning.
Save
Provides an in-depth look at the R programming language. It covers topics such as data structures, object-oriented programming, and high-performance computing. It valuable resource for experienced R users who want to learn more about the language.
Classic text on statistical computing using S-PLUS. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. It valuable resource for experienced R users who want to learn more about the S-PLUS programming language.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mtu9ag/r
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Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Jeff Leek, PhD
Brian Caffo, PhD
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Data Science. It's comprised of 11 courses:
Good to know
Teaches practical statistical programming in R, which is standard in the data science industry
Taught by instructors who are recognized for their work in R programming and statistical computing
Develops programming skills and statistical data analysis techniques, which are core skills for data scientists
Covers loop functions and debugging, which are essential for writing efficient and reliable R code
Includes simulation and profiling, which are advanced techniques for optimizing and evaluating R code
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