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Chris Shockley
In this 2-hour long project-based course, you will learn one of the most powerful data analysis tools of the experts: the DPLYR package. By learning the six main verbs of the package (filter, select, group by, summarize, mutate, and arrange), you will have the knowledge and tools to complete your next data analysis project or data transformation.
Read more
In this 2-hour long project-based course, you will learn one of the most powerful data analysis tools of the experts: the DPLYR package. By learning the six main verbs of the package (filter, select, group by, summarize, mutate, and arrange), you will have the knowledge and tools to complete your next data analysis project or data transformation.
Read more
In this 2-hour long project-based course, you will learn one of the most powerful data analysis tools of the experts: the DPLYR package. By learning the six main verbs of the package (filter, select, group by, summarize, mutate, and arrange), you will have the knowledge and tools to complete your next data analysis project or data transformation.
By the end of this project, you will be able to:
Use the six main dplyr verbs
Understand the dplyr package and its capabilities
Get hands-on practice using R and dplyr functions
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, this means instant access to a cloud desktop with R and the appropriate packages installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/0x1c78/build
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What's inside
Syllabus
Intro to dplyr
Welcome to the first module! In this module, you will be taken to Rhyme where a Virtual Machine with R, R Studio and dplyr awaits. Once there you will begin the Project where you will be introduced to the Rhyme Interface and subsequently learn the dplyr verbs through hands on exercises. Come in and get experience using R and the dplyr functions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes dplyr, which is a cornerstone tool utilized by data analysts
Provides practical R and dplyr function implementation
Designed for an immersive, hands-on learning experience through Rhyme
Access to pre-configured cloud desktops simplifies the learning environment
Save this course
Save Build Data Analysis tools using R and DPLYR to your list so you can find it easily later:
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Reviews summary
Well received beginners guide to dplyr
Learners say this beginner-level course is useful for those who want to learn more about using DPLYR with R for data analysis. Many students say the lectures are well explained and easy to follow, making it a great choice for those who are new to the subject.
Nicely structured for beginners.
"nicely structured for beginner.i liked it."
"Great guided project. Pretty much explained"
"It is a great learning course for a beginner."
Well taught and easy to understand.
"Well summarized and articulated."
"Really mindful and easy to learn!"
"Very explanatory and well taught."
Good intro course for DPLYR.
"Nice for revision"
"Nice course for beginners."
"A good introduction to dplyr."
Platform for this course can be problematic.
"The platform for this course is really cumbersome and distracting."
"Course is very nice and crisp. Only problem was the Rhyme server used for practice. Faced lots of issues."
Very basic with limited learning outcome.
"Very basic, very short. Near zero learning outcome."
"Nice intro, wished there was more complex examples"
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 Build Data Analysis tools using R and DPLYR with these
activities:
Review Data Science with R Book
Show steps
Review the Data Science with R book to strengthen your understanding of the dplyr package and its functions.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Follow Tutorials on dplyr
Show steps
Explore tutorials on dplyr to expand your knowledge and discover advanced techniques.
Browse courses on
Dplyr Package
Show steps
-
Search for online tutorials
-
Follow the steps and try out the examples
Join a Study Group on dplyr
Show steps
Collaborate with peers in a study group to discuss concepts, share insights, and learn from each other.
Browse courses on
Dplyr Package
Show steps
-
Find or create a study group
-
Establish a regular meeting schedule
-
Take turns presenting and discussing topics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Practice Exercises on dplyr
Show steps
Engage in practice drills on dplyr to reinforce your understanding of its syntax and functionality.
Browse courses on
Dplyr Package
Show steps
-
Find online exercises or use the resources provided in the course
-
Solve the exercises
-
Review your answers
Compile a Resource List on dplyr
Show steps
Create a comprehensive list of resources on dplyr, including tutorials, documentation, and examples.
Browse courses on
Dplyr Package
Show steps
-
Search for resources online
-
Categorize and organize the resources
-
Share the list with others
Create a Project using dplyr Functions
Show steps
Develop a project that utilizes dplyr functions to enhance your proficiency in data manipulation and analysis.
Browse courses on
Dplyr Package
Show steps
-
Identify a suitable dataset
-
Load the dataset into R
-
Apply dplyr functions to explore and transform the data
-
Summarize and visualize the results
Participate in a Data Science Competition
Show steps
Challenge yourself in a data science competition to test your skills and gain practical experience.
Browse courses on
Dplyr Package
Show steps
-
Find an appropriate competition
-
Download the dataset
-
Develop a solution using dplyr functions
-
Submit your solution
Mentor a Beginner on dplyr
Show steps
Share your knowledge by mentoring a beginner and guiding them through the concepts of dplyr.
Browse courses on
Dplyr Package
Show steps
-
Find a beginner to mentor
-
Establish a regular communication schedule
-
Provide guidance and support
Review Data Science with R Book
Show steps
Review the Data Science with R book to strengthen your understanding of the dplyr package and its functions.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
Follow Tutorials on dplyr
Show steps
Explore tutorials on dplyr to expand your knowledge and discover advanced techniques.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Search for online tutorials
- Follow the steps and try out the examples
Join a Study Group on dplyr
Show steps
Collaborate with peers in a study group to discuss concepts, share insights, and learn from each other.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Find or create a study group
- Establish a regular meeting schedule
- Take turns presenting and discussing topics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Practice Exercises on dplyr
Show steps
Engage in practice drills on dplyr to reinforce your understanding of its syntax and functionality.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Find online exercises or use the resources provided in the course
- Solve the exercises
- Review your answers
Compile a Resource List on dplyr
Show steps
Create a comprehensive list of resources on dplyr, including tutorials, documentation, and examples.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Search for resources online
- Categorize and organize the resources
- Share the list with others
Create a Project using dplyr Functions
Show steps
Develop a project that utilizes dplyr functions to enhance your proficiency in data manipulation and analysis.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Identify a suitable dataset
- Load the dataset into R
- Apply dplyr functions to explore and transform the data
- Summarize and visualize the results
Participate in a Data Science Competition
Show steps
Challenge yourself in a data science competition to test your skills and gain practical experience.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Find an appropriate competition
- Download the dataset
- Develop a solution using dplyr functions
- Submit your solution
Mentor a Beginner on dplyr
Show steps
Share your knowledge by mentoring a beginner and guiding them through the concepts of dplyr.
Browse courses on
Dplyr Package
Show steps
Show steps
Show steps
- Find a beginner to mentor
- Establish a regular communication schedule
- Provide guidance and support
Career center
Learners who complete Build Data Analysis tools using R and DPLYR will develop knowledge and skills
that may be useful to these careers:
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make recommendations on investments and financial decisions. R and DPLYR are commonly used by Financial Analysts to analyze financial data, and this course would be particularly helpful to someone entering this field since it provides a hands-on approach to using both of these tools. The six main verbs taught in this course: filter, select, group by, summarize, mutate, and arrange are all essential to the work done by a financial analyst.
Data Scientist
Data Scientist
Data Scientists combine programming and analytical skills to extract insights from data, and this course would aid an individual who wants to become a Data Scientist by teaching them some of the tools and techniques used by those in the field. R and DPLYR are among the most commonly used tools by Data Scientists, and the six verbs taught in this course are essential to the work done by a Data Scientist.
Investment Analyst
Investment Analyst
Investment Analysts evaluate potential investments, make investment recommendations, and manage investment portfolios. R and DPLYR are commonly used by Investment Analysts to analyze investment data, and this course would be particularly helpful to someone entering this field since it provides a hands-on approach to using both of these tools. The six main verbs taught in this course: filter, select, group by, summarize, mutate, and arrange are all essential to the work done by an investment analyst.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks. R and DPLYR are commonly used by Risk Analysts to analyze risk data, and this course would be particularly helpful to someone entering this field since it provides a hands-on approach to using both of these tools. The six main verbs taught in this course: filter, select, group by, summarize, mutate, and arrange are all essential to the work done by a Risk Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts apply statistical and mathematical models and techniques to data to evaluate potential investment strategies and developments. The knowledge of R and DPLYR that this course provides may help build a foundation for becoming a Quantitative Analyst, as these tools can be used to make basic and high-level calculations that are often the first step taken by a Quantitative Analyst.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data, working in a wide range of industries like insurance, finance, market research, public policy, and health care. This course would be helpful for a student aspiring to become a Statistician since it teaches R and DPLYR, two tools commonly used by those in this field. The six verbs taught in this course are particularly relevant to the role, as they are used by Statisticians to organize their data in a way that makes performing statistical analysis more accessible and efficient.
Epidemiologist
Epidemiologist
Epidemiologists investigate the occurrence of diseases and other health problems in populations. They use a variety of tools to analyze data, including R and DPLYR. This course could be a great stepping stone for someone looking to become an Epidemiologist, as it would provide a foundation in using both of these tools. Additionally, the course's focus on hands-on learning would be beneficial for someone who wants to gain practical experience in using R and DPLYR.
Biostatistician
Biostatistician
Biostatisticians apply statistics to biological or medical data to solve scientific questions and to develop and evaluate health-related products and services. R and DPLYR are commonly used by Biostatisticians to analyze their data, and this course would be particularly helpful to someone entering this field since it provides a hands-on approach to using these two tools. The six main verbs taught in this course are often the first steps taken by a Biostatistician.
Business Analyst
Business Analyst
Business Analysts analyze business data to identify opportunities for improvement. This course could be helpful for someone who wants to become a Business Analyst, as it would help them learn how to use R and DPLYR, two tools commonly used by Business Analysts. Additionally, the course's focus on hands-on learning would be beneficial for someone who wants to gain practical experience using these tools.
Market Researcher
Market Researcher
Market Researchers gather and analyze data about markets, products, and customers. This course could be useful for someone who wants to become a Market Researcher, as it would help teach them how to use R and DPLYR, two tools commonly used to analyze market data. Additionally, the course's focus on hands-on learning would be beneficial for someone who wants to gain practical experience using these tools.
Data Analyst
Data Analyst
Data Analysts collect data from a wide variety of sources and formats, and convert it into a form that can be analyzed by other members of the team. This course would help someone become a Data Analyst by teaching how to use R and DPLYR to sort, filter, and perform rudimentary analysis on data, which is nearly always the first step taken by a Data Analyst. The six verbs taught by this course: filter, select, group by, summarize, mutate, and arrange are all essential to this first step in data analysis.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. This course would provide a foundation for someone who wants to become a Database Administrator, as it would teach them how to use R and DPLYR, two programming languages commonly used by Database Administrators. Additionally, the course's focus on hands-on learning would be beneficial for someone who wants to gain practical experience using these languages.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. This course would be useful for someone who wants to become a Data Engineer, as it would help teach them how to use R and DPLYR, two tools commonly used by Data Engineers. Additionally, the course's focus on hands-on learning would be beneficial for someone who wants to gain practical experience using these tools.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course would provide a foundation for someone who wants to become a Software Engineer, as it would teach them how to use R and DPLYR, two programming languages commonly used by Software Engineers. Additionally, the course's focus on hands-on learning would be beneficial for someone who wants to gain practical experience using these languages.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course would provide a basic introduction to R and DPLYR, which Project Managers can use to analyze data and make better decisions. Additionally, the course may help build a foundation for a Project Manager who wishes to learn more advanced data analysis techniques in the future.
For more career information including salaries, visit:
OpenCourser.com/course/0x1c78/build
Reading list
We've selected 15 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
Build Data Analysis tools using R and DPLYR.
Provides a comprehensive introduction to the R programming language and its use in data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. This book valuable resource for anyone who wants to learn more about R and its applications.
Provides a comprehensive introduction to the ggplot2 package, a grammar of graphics for R. It covers a wide range of topics, from basic syntax to advanced techniques. This book valuable resource for anyone who wants to learn more about ggplot2 and its applications.
Save
Provides a comprehensive introduction to the R programming language and its use in data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. This book valuable resource for anyone who wants to learn more about R and its applications.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. This book valuable resource for anyone who wants to learn more about statistical learning and its applications.
Provides a comprehensive overview of the R programming language and its use in data science. It covers a wide range of topics, from basic syntax to advanced statistical techniques. This book valuable resource for anyone who wants to learn more about R and its applications.
Provides a comprehensive introduction to the R programming language. It covers a wide range of topics, from basic syntax to advanced statistical techniques. This book valuable resource for anyone who wants to learn more about R and its applications.
Provides a practical introduction to the R programming language. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. This book valuable resource for anyone who wants to learn more about R and its applications.
Save
Provides a comprehensive introduction to the R programming language and its use in data science. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. This book valuable resource for anyone who wants to learn more about R and its applications.
Save
Provides a collection of recipes for creating R Markdown documents. It covers a wide range of topics, from basic syntax to advanced techniques. This book valuable resource for anyone who wants to learn more about R Markdown and its applications.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. This book valuable resource for anyone who wants to learn more about statistical learning and its applications.
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, from supervised learning to unsupervised learning to reinforcement learning. This book valuable resource for anyone who wants to learn more about machine learning and its applications.
Save
Comprehensive reference guide to the R programming language. It covers a wide range of topics, from basic syntax to advanced statistical techniques. This book valuable resource for anyone who wants to learn more about R and its applications.
Provides a step-by-step guide to data manipulation with R. It covers a wide range of topics, from data import and cleaning to data transformation and visualization. This book valuable resource for anyone who wants to learn more about data manipulation with R.
Save
Provides a collection of recipes for solving common problems in R. It covers a wide range of topics, from data manipulation and visualization to statistical modeling and machine learning. This book valuable resource for anyone who wants to learn more about R and its applications.
Save
Provides a comprehensive overview of advanced R programming techniques. It covers a wide range of topics, from data structures and algorithms to statistical modeling and machine learning. This book valuable resource for anyone who wants to learn more about advanced R programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0x1c78/build
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Build Data Analysis and Transformation Skills in R using...
Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Language
English
Good to know
Emphasizes dplyr, which is a cornerstone tool utilized by data analysts
Provides practical R and dplyr function implementation
Designed for an immersive, hands-on learning experience through Rhyme
Access to pre-configured cloud desktops simplifies the learning environment
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