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Roger D. Peng, PhD
and
Brooke Anderson
R Programming Capstone
Register for this course and see more details by visiting:
OpenCourser.com/course/cfysh5/mastering
What's inside
Syllabus
Obtain and Clean the Data
The overall goal of the capstone project is to integrate the skills you have developed over the courses in this Specialization and to build a software package that can be used to work with the NOAA Significant Earthquakes dataset.
Read more
Syllabus
Obtain and Clean the Data
The overall goal of the capstone project is to integrate the skills you have developed over the courses in this Specialization and to build a software package that can be used to work with the NOAA Significant Earthquakes dataset.
Read more
Building Geoms
Show us when earthquakes occurred in different countries, their magnitude, and their toll on human life.
Building a Leaflet Map
Show and annotate the earthquake epicenters.
Documentation and Packaging
Documentation is one of the most important and most commonly overlooked steps when writing software, but you're not going to let that happen in your project.
Deployment
The moment of truth. It's time to push your package to GitHub.
Final Assessment
It's time to submit your deployed package for evaluation and to evaluate the work of a few of your classmates.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides relevant, marketable skills for applying to jobs in the tech sector
Provides a certification upon completion, which may enhance credibility
Taught by experienced instructors, Roger D. Peng, PhD, and Brooke Anderson
Covers essential data science concepts and techniques
Provides hands-on experience through practical projects
Save this course
Save Mastering Software Development in R Capstone to your list so you can find it easily later:
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Reviews summary
R software development practice
Learners say that this software development course is excellent for practicing essential coding skills. The programming assignment is helpful for writing and deploying a package in R. However, some students found the grading process of assignments to be painful and hope the course provides a more learner-friendly environment.
The programming assignment helps develop important coding skills for writing and deploying a package in R.
"This programming assignment got me to practice essential coding skills for writing and deploying a package in R, which is super helpful."
The course is open only a few times a year.
"This course is excellent! I truly learned a lot about software development with R. The only problem is that it is open a few times a year, due to the small number of participants."
The grading process for assignments is needlessly painful.
"The most painful process of this course including previous ones except the first one is to wait for assignments to be graded."
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 Mastering Software Development in R Capstone with these
activities:
Review R basics
Show steps
Helps prepare for the course by reviewing fundamental concepts of R programming.
Browse courses on
R Programming
Show steps
-
Review online tutorials or documentation on R basics.
-
Complete practice exercises.
-
Install R and RStudio.
Read 'R for Data Science' by Hadley Wickham and Garrett Grolemund
Show steps
Provides a comprehensive foundation in R programming and data science, covering topics such as data manipulation, visualization, and modeling.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read and understand the concepts presented in the book.
-
Complete the exercises and examples provided in the book.
-
Apply the techniques learned to personal projects.
Discuss course concepts with peers
Show steps
Helps clarify concepts and deepen understanding by discussing course material with peers.
Browse courses on
Data Science
Show steps
-
Find a study partner or group.
-
Review course materials together.
-
Discuss concepts and ask questions.
-
Work on projects together.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on data analysis
Show steps
Helps reinforce concepts learned in the course by following guided tutorials on data analysis and visualization.
Browse courses on
Data Analysis
Show steps
-
Find tutorials on data analysis and visualization.
-
Follow tutorials and complete exercises.
-
Apply techniques learned to personal projects.
Practice working with data
Show steps
Helps solidify knowledge of R Programming by completing repetitive exercises and drills.
Browse courses on
R Programming
Show steps
-
Load data into R.
-
Clean and prepare data.
-
Create visualizations to explore data.
Create a data visualization
Show steps
Helps apply skills learned in the course by creating a data visualization that communicates insights from the NOAA Significant Earthquakes dataset.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset.
-
Clean and prepare data.
-
Select appropriate visualization type.
-
Create visualization using R.
-
Interpret and communicate results.
Write a blog post or article on data analysis
Show steps
Helps consolidate knowledge and improve communication skills by writing a blog post or article that shares insights from the NOAA Significant Earthquakes dataset.
Browse courses on
Data Analysis
Show steps
-
Choose a topic related to data analysis and visualization.
-
Research and gather data.
-
Write a clear and engaging article.
-
Publish and promote your article.
Contribute to an open-source R package
Show steps
Helps apply and extend skills learned in the course by contributing to the development of an open-source R package related to data analysis or visualization.
Browse courses on
Open Source
Show steps
-
Find an open-source R package to contribute to.
-
Identify an issue or feature to work on.
-
Write code and submit a pull request.
-
Review and respond to feedback.
-
Merge changes and release new version of package.
Review R basics
Show steps
Helps prepare for the course by reviewing fundamental concepts of R programming.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review online tutorials or documentation on R basics.
- Complete practice exercises.
- Install R and RStudio.
Read 'R for Data Science' by Hadley Wickham and Garrett Grolemund
Show steps
Provides a comprehensive foundation in R programming and data science, covering topics such as data manipulation, visualization, and modeling.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read and understand the concepts presented in the book.
- Complete the exercises and examples provided in the book.
- Apply the techniques learned to personal projects.
Discuss course concepts with peers
Show steps
Helps clarify concepts and deepen understanding by discussing course material with peers.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a study partner or group.
- Review course materials together.
- Discuss concepts and ask questions.
- Work on projects together.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on data analysis
Show steps
Helps reinforce concepts learned in the course by following guided tutorials on data analysis and visualization.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find tutorials on data analysis and visualization.
- Follow tutorials and complete exercises.
- Apply techniques learned to personal projects.
Practice working with data
Show steps
Helps solidify knowledge of R Programming by completing repetitive exercises and drills.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Load data into R.
- Clean and prepare data.
- Create visualizations to explore data.
Create a data visualization
Show steps
Helps apply skills learned in the course by creating a data visualization that communicates insights from the NOAA Significant Earthquakes dataset.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset.
- Clean and prepare data.
- Select appropriate visualization type.
- Create visualization using R.
- Interpret and communicate results.
Write a blog post or article on data analysis
Show steps
Helps consolidate knowledge and improve communication skills by writing a blog post or article that shares insights from the NOAA Significant Earthquakes dataset.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a topic related to data analysis and visualization.
- Research and gather data.
- Write a clear and engaging article.
- Publish and promote your article.
Contribute to an open-source R package
Show steps
Helps apply and extend skills learned in the course by contributing to the development of an open-source R package related to data analysis or visualization.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source R package to contribute to.
- Identify an issue or feature to work on.
- Write code and submit a pull request.
- Review and respond to feedback.
- Merge changes and release new version of package.
Career center
Learners who complete Mastering Software Development in R Capstone will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data to create visual representations that help people understand complex information. The Software Development in R Capstone course is a great choice for aspiring Data Visualization Specialists. It teaches how to use the R programming language to obtain, clean, and analyze data, as well as build geoms and a Leaflet map. These skills are essential for Data Visualization Specialists.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts use data to create maps and other visualizations that help people understand the spatial relationships between different features. The Software Development in R Capstone course could be useful for aspiring Geospatial Analysts. This course covers how to obtain, clean, and analyze data, build geoms, and build a Leaflet map. These skills are all essential for Geospatial Analysts.
Software Developer
Software Developer
A Software Developer is responsible for designing, developing, and maintaining software applications. The Software Development in R Capstone course can help you become a Software Developer by teaching you the R programming language. The course covers data cleaning, building geoms, building a Leaflet map, and more. This gives you the foundational knowledge to start a career in Software Development.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. The Software Development in R Capstone course can be useful for aspiring Machine Learning Engineers. It provides a foundation in the R programming language, data cleaning, building geoms, and building a Leaflet map. These skills are essential for Machine Learning Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. If you want to become a Quantitative Analyst, the Software Development in R Capstone course could be useful. This course provides a foundation in the R programming language, data cleaning, building geoms, and building a Leaflet map. These skills are essential for Quantitative Analysts.
Technical Writer
Technical Writer
Technical Writers create documentation and other materials that explain technical information. The Software Development in R Capstone course could be useful for aspiring Technical Writers. This course covers how to write documentation for software, which is an important skill for Technical Writers to have. Additionally, the course teaches how to obtain, clean, and analyze data, as well as how to build geoms and a Leaflet map with R.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and inform the public. The Software Development in R Capstone course can be useful for aspiring Data Journalists. This course covers how to obtain, clean, and analyze data, as well as how to build geoms and a Leaflet map with R. These skills are essential for Data Journalists to have.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. The Software Development in R Capstone course could be useful for aspiring Business Analysts. The course teaches how to obtain, clean, and analyze data, as well as how to build geoms and a Leaflet map with R. These skills are essential for Business Analysts.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. The Software Development in R Capstone course could be useful for aspiring Data Analysts. In this course, you will learn how to obtain data, build geoms, and build a Leaflet map. These skills are essential for Data Analysts.
Product Manager
Product Manager
Product Managers are responsible for overseeing the development and launch of new products. The Software Development in R Capstone course might be useful for aspiring Product Managers. This course covers how to obtain, clean, and analyze data, as well as how to build geoms and a Leaflet map with R. These skills can be helpful for Product Managers who need to understand the data about their products.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data infrastructure. If you want to become a Data Engineer, the Software Development in R Capstone course may be helpful. This course covers topics such as data cleaning, building geoms, and building a Leaflet map, all of which are important for a Data Engineer to know.
Software Architect
Software Architect
Software Architects design and oversee the development of software systems. The Software Development in R Capstone course might be useful for aspiring Software Architects. This course covers how to obtain, clean, and analyze data, as well as how to build geoms and a Leaflet map with R. These skills are all essential for Software Architects to have.
Research Scientist
Research Scientist
Research Scientists use data to conduct research and develop new knowledge. The Software Development in R Capstone course may be useful for aspiring Research Scientists. This course covers how to obtain, clean, and analyze data, build geoms, and build a Leaflet map. These skills are essential for Research Scientists to have.
Statistician
Statistician
If you want to become a Statistician, the Software Development in R Capstone course may be useful. This course focuses on the R programming language, a popular and powerful tool for working with data. You will learn how to obtain and clean data, build geoms, build a Leaflet map, document, and package your software. These skills are essential for a Statistician to have.
Data Scientist
Data Scientist
Data Science has become one of the most prominent and in-demand career roles in the 21st century. The Software Development in R Capstone course may be useful if your career goal is to become a Data Scientist, as it teaches the R syntax and the statistical programming language. This course also teaches how to build geoms, build a Leaflet map, and write documentation. These are important skills for a Data Scientist to have.
For more career information including salaries, visit:
OpenCourser.com/course/cfysh5/mastering
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
Mastering Software Development in R Capstone.
Provides a comprehensive and hands-on introduction to R programming. It covers all the essential concepts of R, from data manipulation and analysis to visualization and modeling. It valuable resource for both beginners and experienced R users.
Save
Provides a practical and concise introduction to R for data science. It covers all the essential concepts and techniques for data analysis, visualization, and modeling. It must-read for anyone who wants to learn R for data science.
Save
Provides an in-depth look at advanced R programming techniques. It covers topics such as object-oriented programming, data structures, and algorithms. It valuable resource for experienced R users.
Save
Provides a collection of recipes for common R tasks. It covers a wide range of topics, from data manipulation and analysis to visualization and modeling. It valuable resource for anyone who uses R for data science.
Save
Provides a comprehensive guide to the ggplot2 package, which powerful R package for data visualization. It covers all the essential concepts and techniques for creating beautiful and informative graphs.
Save
Provides a practical introduction to data analysis with R. It covers all the essential concepts and techniques for data cleaning, manipulation, and analysis. It valuable resource for anyone who wants to learn how to use R for data analysis.
Save
Provides a comprehensive guide to R Markdown, which powerful tool for creating dynamic and interactive documents. It covers all the essential concepts and techniques for using R Markdown for reports, presentations, and websites.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, from linear models and regression to classification and clustering. It valuable resource for anyone who wants to learn the basics of statistical learning.
Provides a more advanced look at statistical learning. It covers topics such as support vector machines, decision trees, and ensemble methods. It valuable resource for anyone who wants to learn more about the latest advances in statistical learning.
Provides a practical introduction to machine learning with R. It covers all the essential concepts and techniques for building machine learning models. It valuable resource for anyone who wants to learn how to use R for machine learning.
Provides a practical introduction to data science for business. It covers all the essential concepts and techniques for using data to make better decisions. It valuable resource for anyone who wants to learn how to use data science for business.
Provides a practical introduction to deep learning with R. It covers all the essential concepts and techniques for building deep learning models. It valuable resource for anyone who wants to learn how to use R for deep learning.
Save
Provides a comprehensive introduction to time series analysis with R. It covers all the essential concepts and techniques for working with time series data. It valuable resource for anyone who wants to learn how to use R for time series analysis.
Provides a comprehensive introduction to spatial data analysis with R. It covers all the essential concepts and techniques for working with spatial data. It valuable resource for anyone who wants to learn how to use R for spatial data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cfysh5/mastering
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Johns Hopkins University
Instructors
Roger D. Peng, PhD
Brooke Anderson
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This course belongs to a
collection
called
Mastering Software Development in R. It's comprised of five courses:
- The R Programming Environment
- Building R Packages
- Mastering Software Development in R Capstone (viewing)
- Building Data Visualization Tools
- Advanced R Programming
Good to know
Provides relevant, marketable skills for applying to jobs in the tech sector
Provides a certification upon completion, which may enhance credibility
Taught by experienced instructors, Roger D. Peng, PhD, and Brooke Anderson
Covers essential data science concepts and techniques
Provides hands-on experience through practical projects
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