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Mine Çetinkaya-Rundel
and
Dr. Elijah Meyer
Welcome to Data Visualization and Transformation, the first course in the Data Science with R Specialization! This course is an introduction to data science and statistical thinking. Learners will gain experience with exploring, visualizing, and analyzing data to understand natural phenomena and investigate patterns, model outcomes, and do so in a reproducible and shareable manner. Topics covered include data visualization and transformation for exploratory data analysis. Learners will be introduced to problems and case studies inspired by and based on real-world questions and data via lecture and live coding videos as well as interactive programming exercises. The course will focus on the R statistical computing language with a focus on packages from the Tidyverse, the RStudio integrated development environment, Quarto for reproducible reporting, and Git and GitHub for version control. The skills learners will gain in this course will prepare them for careers in a variety of fields, including data scientist, data analyst, quantitative analyst, statistician, and much more.
Register for this course and see more details by visiting:
OpenCourser.com/course/ha4ftk/data
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What's inside
Syllabus
Hello World
Hello World! Welcome to your first module in earning your specialization in Data Science with R certificate. In the first module, you will learn about what data science is and how data science techniques are used to make meaning from data and inform data-driven decisions. There is also discussion around the importance of reproducibility in science and the techniques used to achieve this. Next, you will learn the technology languages of R, RStudio, Quarto, and GitHub, as well as their role in data science and reproducibility.
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Syllabus
Hello World
Hello World! Welcome to your first module in earning your specialization in Data Science with R certificate. In the first module, you will learn about what data science is and how data science techniques are used to make meaning from data and inform data-driven decisions. There is also discussion around the importance of reproducibility in science and the techniques used to achieve this. Next, you will learn the technology languages of R, RStudio, Quarto, and GitHub, as well as their role in data science and reproducibility.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches data science to students with no prior experience
Taught by world-renowned data science experts, including Dr. Elijah Meyer and Mine Cetinkaya-Rundel
Develops a foundation in data visualization and transformation, which are crucial skills for data scientists
Uses industry-standard R statistical computing language and Tidyverse packages
Utilizes Quarto for reproducible reporting, which is essential for sharing and collaborating on data science projects
Emphasizes reproducible and shareable research practices, which are vital for scientific integrity
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Reviews summary
Practical r for data science foundation
According to learners, this course provides a solid foundation in data visualization and transformation with R, making it an excellent choice for those pursuing data careers. Many commend its clear instruction on Tidyverse packages, particularly ggplot2 and dplyr, and its strong emphasis on hands-on activities and real-world applications. The integration of Quarto and Git/GitHub for reproducible workflows is frequently highlighted as a significant positive. While some absolute beginners might find the pacing occasionally fast or encounter initial technical setup challenges, the overall sentiment is highly positive, with students gaining practical, applicable skills.
Teaches essential skills using Quarto and Git/GitHub.
"I particularly appreciated the focus on reproducible workflows using `Quarto` and `Git/GitHub`. It really sets a solid foundation for my data science journey."
"Learning about `Quarto` for reproducible reporting was a huge bonus, and I value this aspect greatly."
"I liked how the course integrated `Git` and `Quarto` from the start, emphasizing reproducible research; I consider this a very important skill."
Strong emphasis on practical application through exercises and projects.
"I particularly appreciated the focus on reproducible workflows... The instructor's demos were easy to follow."
"The `interactive programming exercises` really cement the concepts for me. Learning about `Quarto` for reproducible reporting was a huge bonus."
"The `real-world case studies` made the learning relevant for me. `Quarto` and `Git` integration was a smart move for practical application."
"I found the clear, concise `lectures` combined with `hands-on coding` made my learning effective."
Excellent teaching of core R packages like ggplot2 and dplyr.
"The explanations of `ggplot2` and `dplyr` are incredibly clear, with plenty of hands-on exercises."
"I found the content on data transformation using `dplyr` especially useful for my work, providing a very solid introduction to R and the Tidyverse."
"The `Grammar of Graphics` for `ggplot2` was eye-opening for me; I couldn't ask for a better introduction to data visualization in R."
"The way `ggplot2` and `dplyr` are taught is phenomenal; I'm now confidently creating visualizations and manipulating data."
Some learners encountered minor technical issues during setup.
"My only minor gripe is that the initial setup of R/RStudio/Git could be smoother; I encountered a few minor technical hitches..."
"I struggled a lot. The initial setup was a nightmare..."
Pacing can be fast for some absolute beginners.
"I found some `lectures` moved a bit too fast, especially in the visualization module. The `labs` were sometimes challenging to complete without external resources."
"I struggled a lot because the initial setup was a nightmare, and I felt this course requires some prior programming knowledge not clearly stated. It's not for absolute beginners."
"While the `instructor` was knowledgeable, I felt that sometimes the pace was a bit quick for me."
"I found the `assignments` often felt disconnected from the material, and I needed to consult outside tutorials to truly grasp some of the `ggplot2` concepts."
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 Data Visualization and Transformation with R with these
activities:
Summarize Course Materials for Future Reference
Show steps
Create a comprehensive summary of course materials for easy referencing and improved retention of key concepts.
Show steps
-
Go through lecture notes, readings, and other course materials.
-
Identify and extract key concepts, definitions, and examples.
-
Organize the extracted information into a concise summary.
-
Review and update the summary on a regular basis.
Connect with Data Science Professionals
Show steps
Build your network and gain insights from experienced practitioners in the field of Data Science.
Show steps
-
Attend industry meetups and conferences.
-
Join online communities and forums.
-
Reach out to individuals in your professional network.
-
Request informational interviews to learn about their experiences and advice.
Review Tufte's 'Beautiful Evidence'
Show steps
Build a conceptual foundation for visualizing data effectively and identify influential design principles in data visualization.
View
Beautiful Evidence
on Amazon
Show steps
-
Read the book's introduction and chapter 1.
-
Identify and summarize the key principles of data visualization.
-
Examine real-world examples of effective and ineffective data visualizations.
-
Create a short presentation summarizing your key takeaways.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Plotting Different Data Types with ggplot2
Show steps
Master the basics of ggplot2 by creating visualizations for different data types, enhancing your ability to communicate data insights effectively.
Browse courses on
Data Exploration
Show steps
-
Load a dataset into R.
-
Create visualizations for numerical, categorical, and time-series data.
-
Customize plot aesthetics, including colors, shapes, and labels.
-
Save and export your visualizations.
Create a Data Visualization Dashboard
Show steps
Demonstrate proficiency in creating interactive data visualizations that communicate insights clearly and effectively.
Browse courses on
Data Visualization
Show steps
-
Gather and prepare a dataset.
-
Design visualizations that effectively display the key aspects of your data.
-
Use Quarto to create an interactive dashboard.
-
Deploy your dashboard online and share it with others.
Participate in a Data Science Hackathon
Show steps
Put your skills to the test in a collaborative environment, fostering problem-solving and critical thinking abilities.
Browse courses on
Data Science
Show steps
-
Find a data science hackathon that aligns with your interests.
-
Form a team or join an existing one.
-
Develop a solution to the proposed problem.
-
Present your findings and compete for prizes.
Contribute to the Tidyverse Community
Show steps
Engage with the broader Data Science community by contributing to Tidyverse packages and sharing your knowledge with others.
Browse courses on
Tidyverse
Show steps
-
Identify a specific package or feature within the Tidyverse ecosystem that you would like to contribute to.
-
Review the project's documentation and familiarize yourself with its development process.
-
Create a pull request with your proposed improvements.
Summarize Course Materials for Future Reference
Show steps
Create a comprehensive summary of course materials for easy referencing and improved retention of key concepts.
Show steps
Show steps
Show steps
- Go through lecture notes, readings, and other course materials.
- Identify and extract key concepts, definitions, and examples.
- Organize the extracted information into a concise summary.
- Review and update the summary on a regular basis.
Connect with Data Science Professionals
Show steps
Build your network and gain insights from experienced practitioners in the field of Data Science.
Show steps
Show steps
Show steps
- Attend industry meetups and conferences.
- Join online communities and forums.
- Reach out to individuals in your professional network.
- Request informational interviews to learn about their experiences and advice.
Review Tufte's 'Beautiful Evidence'
Show steps
Build a conceptual foundation for visualizing data effectively and identify influential design principles in data visualization.
View
Beautiful Evidence
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and chapter 1.
- Identify and summarize the key principles of data visualization.
- Examine real-world examples of effective and ineffective data visualizations.
- Create a short presentation summarizing your key takeaways.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Plotting Different Data Types with ggplot2
Show steps
Master the basics of ggplot2 by creating visualizations for different data types, enhancing your ability to communicate data insights effectively.
Browse courses on
Data Exploration
Show steps
Show steps
Show steps
- Load a dataset into R.
- Create visualizations for numerical, categorical, and time-series data.
- Customize plot aesthetics, including colors, shapes, and labels.
- Save and export your visualizations.
Create a Data Visualization Dashboard
Show steps
Demonstrate proficiency in creating interactive data visualizations that communicate insights clearly and effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and prepare a dataset.
- Design visualizations that effectively display the key aspects of your data.
- Use Quarto to create an interactive dashboard.
- Deploy your dashboard online and share it with others.
Participate in a Data Science Hackathon
Show steps
Put your skills to the test in a collaborative environment, fostering problem-solving and critical thinking abilities.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science hackathon that aligns with your interests.
- Form a team or join an existing one.
- Develop a solution to the proposed problem.
- Present your findings and compete for prizes.
Contribute to the Tidyverse Community
Show steps
Engage with the broader Data Science community by contributing to Tidyverse packages and sharing your knowledge with others.
Browse courses on
Tidyverse
Show steps
Show steps
Show steps
- Identify a specific package or feature within the Tidyverse ecosystem that you would like to contribute to.
- Review the project's documentation and familiarize yourself with its development process.
- Create a pull request with your proposed improvements.
Career center
Learners who complete Data Visualization and Transformation with R will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/ha4ftk/data
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ha4ftk/data
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Rating
4.8
Effort
12 hours to complete
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Mine Çetinkaya-Rundel
Dr. Elijah Meyer
Language
English
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