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Josh Bernhard ,
Karl Krueger,
Derek Steer,
Richard Kalehoff,
and
Juno Lee
Enroll in Udacity's Intro to R Programming Online Training Course and gain the skills to work with R data types, variables, loop statements, and more. Join Now.
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OpenCourser.com/course/931psx/introduction
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What's inside
Syllabus
In this lesson, you will learn about why R is so popular for Data Analysis, learn how to set up R on your own machine, and see how you can use R directly within the classroom.
Read more
Syllabus
In this lesson, you will learn about why R is so popular for Data Analysis, learn how to set up R on your own machine, and see how you can use R directly within the classroom.
Read more
In this lesson, gain an understanding of how to interact with the R interpreter and the different data types available in R.
Learn some of the foundational programming concepts in R syntax. These concepts include control flow, loops, and functions.
In this lesson, you will get hands on with creating plots in R. This is where using R really shines, as you build amazing visualizations with only a few lines of code.
You will now use your R skills to explore bikeshare data and report your findings!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by experts in the field: Josh Bernhard, Karl Krueger, Derek Steer, Richard Kalehoff, Juno Lee
Builds foundational programming concepts in R syntax: control flow, loops, and functions
Develops practical skills in creating plots in R, which are highly relevant in data analysis
Provides hands-on experience with bikeshare data analysis, enhancing practical application
May require prior programming knowledge for optimal understanding
Assumes familiarity with the R interpreter and data types, which may pose a challenge for complete beginners
Save this course
Save Introduction to 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 Introduction to R Programming with these
activities:
Review Introductory R Programming Concepts
Show steps
Review the basics of R programming, including data types, variables, and control flow, to ensure a solid foundation for the course.
Browse courses on
R Programming
Show steps
-
Read and understand the course syllabus to gain an overview of the course content.
-
Review online tutorials or documentation on R programming basics.
-
Install R on your computer and familiarize yourself with its interface.
Attend a Study Group
Show steps
Join a study group to engage with other learners, discuss course material, and clarify concepts.
Browse courses on
Collaborative Learning
Show steps
-
Find a study group or create one with classmates or online peers.
-
Meet regularly to discuss course material and work on problems together.
-
Share notes, resources, and ideas with the group.
Complete Coding Challenges
Show steps
Solve coding challenges to practice applying R programming concepts and improve problem-solving skills.
Show steps
-
Find online coding challenges or create your own based on the course material.
-
Attempt to solve the challenges using R code.
-
Review solutions and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an R Workshop
Show steps
Attend an in-person or online workshop to gain practical experience with R programming and enhance your skills.
Show steps
-
Research and identify relevant R workshops in your area or online.
-
Register for the workshop and attend the sessions.
-
Participate actively and ask questions to clarify concepts.
Build a Data Visualization Dashboard
Show steps
Create a data visualization dashboard to apply the plotting techniques learned in the course and enhance data interpretation skills.
Browse courses on
Data Visualization
Show steps
-
Identify a dataset to visualize.
-
Use R to clean and prepare the data for visualization.
-
Create visualizations using appropriate R packages and techniques.
-
Organize the visualizations into a coherent dashboard.
Explore Advanced R Techniques
Show steps
Seek out and follow tutorials to expand knowledge in advanced R techniques, such as machine learning or statistical modeling.
Browse courses on
Statistical Modeling
Show steps
-
Identify specific areas for improvement or topics of interest.
-
Search for online tutorials or documentation on these topics.
-
Follow the tutorials and complete the practice exercises.
Analyze Real-World Data
Show steps
Work on a project that involves analyzing real-world data using R, applying the skills and techniques covered in the course.
Browse courses on
Case study
Show steps
-
Identify a real-world dataset that aligns with the course objectives.
-
Use R to import, clean, and explore the data.
-
Apply statistical techniques and data visualization to analyze the data.
-
Write a report or presentation summarizing the findings.
Contribute to an Open Source R Project
Show steps
Contribute to an open source R project to gain hands-on experience, learn from others, and showcase your skills.
Browse courses on
Community Involvement
Show steps
-
Identify an open source R project that aligns with your interests.
-
Review the project documentation and identify areas where you can contribute.
-
Submit a pull request or create an issue to contribute your changes.
Review Introductory R Programming Concepts
Show steps
Review the basics of R programming, including data types, variables, and control flow, to ensure a solid foundation for the course.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Read and understand the course syllabus to gain an overview of the course content.
- Review online tutorials or documentation on R programming basics.
- Install R on your computer and familiarize yourself with its interface.
Attend a Study Group
Show steps
Join a study group to engage with other learners, discuss course material, and clarify concepts.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Find a study group or create one with classmates or online peers.
- Meet regularly to discuss course material and work on problems together.
- Share notes, resources, and ideas with the group.
Complete Coding Challenges
Show steps
Solve coding challenges to practice applying R programming concepts and improve problem-solving skills.
Show steps
Show steps
Show steps
- Find online coding challenges or create your own based on the course material.
- Attempt to solve the challenges using R code.
- Review solutions and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an R Workshop
Show steps
Attend an in-person or online workshop to gain practical experience with R programming and enhance your skills.
Show steps
Show steps
Show steps
- Research and identify relevant R workshops in your area or online.
- Register for the workshop and attend the sessions.
- Participate actively and ask questions to clarify concepts.
Build a Data Visualization Dashboard
Show steps
Create a data visualization dashboard to apply the plotting techniques learned in the course and enhance data interpretation skills.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset to visualize.
- Use R to clean and prepare the data for visualization.
- Create visualizations using appropriate R packages and techniques.
- Organize the visualizations into a coherent dashboard.
Explore Advanced R Techniques
Show steps
Seek out and follow tutorials to expand knowledge in advanced R techniques, such as machine learning or statistical modeling.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Identify specific areas for improvement or topics of interest.
- Search for online tutorials or documentation on these topics.
- Follow the tutorials and complete the practice exercises.
Analyze Real-World Data
Show steps
Work on a project that involves analyzing real-world data using R, applying the skills and techniques covered in the course.
Browse courses on
Case study
Show steps
Show steps
Show steps
- Identify a real-world dataset that aligns with the course objectives.
- Use R to import, clean, and explore the data.
- Apply statistical techniques and data visualization to analyze the data.
- Write a report or presentation summarizing the findings.
Contribute to an Open Source R Project
Show steps
Contribute to an open source R project to gain hands-on experience, learn from others, and showcase your skills.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify an open source R project that aligns with your interests.
- Review the project documentation and identify areas where you can contribute.
- Submit a pull request or create an issue to contribute your changes.
Career center
Learners who complete Introduction to R Programming will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course will introduce you to the basics of R programming, which is a powerful tool for data analysis. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Data Analyst, and this course will help you get started on your career path.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course will introduce you to the basics of R programming, which is a powerful tool for data science. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Data Scientist, and this course will help you get started on your career path.
Statistician
Statistician
A Statistician applies statistical methods to collect, analyze, interpret, and present data. This course will introduce you to the basics of R programming, which is a powerful tool for statistics. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Statistician, and this course will help you get started on your career path.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course will introduce you to the basics of R programming, which is a powerful tool for quantitative analysis. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Quantitative Analyst, and this course will help you get started on your career path.
Business Analyst
Business Analyst
A Business Analyst uses data analysis to solve business problems and improve decision-making. This course will introduce you to the basics of R programming, which is a powerful tool for business analysis. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Business Analyst, and this course will help you get started on your career path.
Market Researcher
Market Researcher
A Market Researcher uses data analysis to understand customer behavior and make marketing decisions. This course will introduce you to the basics of R programming, which is a powerful tool for market research. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Market Researcher, and this course will help you get started on your career path.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course will introduce you to the basics of R programming, which is a powerful tool for software development. You will learn how to use R to create data structures, algorithms, and user interfaces. These skills are essential for any Software Engineer, and this course will help you get started on your career path.
Web Developer
Web Developer
A Web Developer designs and develops websites and web applications. This course will introduce you to the basics of R programming, which is a powerful tool for web development. You will learn how to use R to create dynamic web pages, handle user input, and build databases. These skills are essential for any Web Developer, and this course will help you get started on your career path.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and data infrastructure. This course will introduce you to the basics of R programming, which is a powerful tool for data engineering. You will learn how to use R to extract, transform, and load data. These skills are essential for any Data Engineer, and this course will help you get started on your career path.
Database Administrator
Database Administrator
A Database Administrator designs, builds, and maintains databases. This course will introduce you to the basics of R programming, which is a powerful tool for database administration. You will learn how to use R to create and manage databases, as well as to perform data analysis and reporting. These skills are essential for any Database Administrator, and this course will help you get started on your career path.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk and uncertainty. This course will introduce you to the basics of R programming, which is a powerful tool for actuarial science. You will learn how to use R to build statistical models, perform risk analysis, and calculate insurance premiums. These skills are essential for any Actuary, and this course will help you get started on your career path.
Financial Analyst
Financial Analyst
A Financial Analyst uses financial data to make investment decisions. This course will introduce you to the basics of R programming, which is a powerful tool for financial analysis. You will learn how to use R to import data, create visualizations, and build statistical models. These skills are essential for any Financial Analyst, and this course will help you get started on your career path.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve business problems. This course will introduce you to the basics of R programming, which is a powerful tool for operations research. You will learn how to use R to build optimization models, simulate systems, and make decisions. These skills are essential for any Operations Research Analyst, and this course will help you get started on your career path.
Risk Manager
Risk Manager
A Risk Manager uses data analysis to identify and manage risks. This course will introduce you to the basics of R programming, which is a powerful tool for risk management. You will learn how to use R to collect data, analyze data, and build risk models. These skills are essential for any Risk Manager, and this course will help you get started on your career path.
Teacher
Teacher
A Teacher educates students in a variety of subjects. This course will introduce you to the basics of R programming, which is a powerful tool for teaching. You will learn how to use R to create interactive visualizations, build simulations, and conduct data analysis. These skills are essential for any Teacher, and this course will help you get started on your career path.
For more career information including salaries, visit:
OpenCourser.com/course/931psx/introduction
Reading list
We've selected 14 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
Introduction to R Programming.
Comprehensive guide to using ggplot2, a popular R package for data visualization. It covers all the basics of ggplot2, from creating simple plots to building complex visualizations. It also includes a number of case studies to help you apply your skills to real-world problems.
Save
Hands-on guide to using R for data science. It covers all the essential topics, from data wrangling and analysis to visualization and modeling. It also includes a number of case studies to help you apply your skills to real-world problems.
Save
Comprehensive introduction to the R programming language. It covers all the basics of R, from data types and variables to loops and functions. It also includes a wealth of examples and exercises to help you learn R.
Practical guide to using R for data analysis. It covers all the essential topics, from data wrangling and analysis to visualization and modeling. It also includes a number of tips and tricks to help you write efficient and effective R code.
Save
Comprehensive guide to using R Markdown, a popular R package for creating dynamic reports. It covers all the basics of R Markdown, from creating simple reports to building complex dashboards. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to using R for data science. It covers all the essential topics, from data wrangling and analysis to visualization and modeling. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to using R for machine learning. It covers all the essential topics, from data preparation and model selection to evaluation and deployment. It also includes a number of case studies to help you apply your skills to real-world problems.
Practical guide to Bayesian statistics using probabilistic programming. It covers all the essential topics, from Bayesian inference and model fitting to Bayesian computation and model checking. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to using R for deep learning. It covers all the essential topics, from neural networks and deep learning architectures to training and evaluating deep learning models. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to Bayesian statistics using R and Stan. It covers all the essential topics, from Bayesian inference and model fitting to Bayesian computation and model checking. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to using Python for data analysis. It covers all the essential topics, from data wrangling and analysis to visualization and modeling. It also includes a number of case studies to help you apply your skills to real-world problems.
Save
Follow-up to R for Data Science. It covers more advanced topics, such as object-oriented programming, functional programming, and data mining. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to causal inference in statistics. It covers all the essential topics, from causal graphs and counterfactuals to causal models and causal inference. It also includes a number of case studies to help you apply your skills to real-world problems.
Comprehensive guide to artificial intelligence for business. It covers all the essential topics, from machine learning and deep learning to natural language processing and computer vision. It also includes a number of case studies to help you apply your skills to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/931psx/introduction
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Effort
4 weeks
Level
Introductory
Via
Udacity
Instructors
Josh Bernhard
Karl Krueger
Derek Steer
Richard Kalehoff
Juno Lee
Language
English
Good to know
Taught by experts in the field: Josh Bernhard, Karl Krueger, Derek Steer, Richard Kalehoff, Juno Lee
Builds foundational programming concepts in R syntax: control flow, loops, and functions
Develops practical skills in creating plots in R, which are highly relevant in data analysis
Provides hands-on experience with bikeshare data analysis, enhancing practical application
May require prior programming knowledge for optimal understanding
Assumes familiarity with the R interpreter and data types, which may pose a challenge for complete beginners
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