Data Visualization and Transformation with R

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.

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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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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

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Create a comprehensive summary of course materials for easy referencing and improved retention of key concepts.

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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

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Build your network and gain insights from experienced practitioners in the field of Data Science.

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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'

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Build a conceptual foundation for visualizing data effectively and identify influential design principles in data visualization.

View Beautiful Evidence on Amazon

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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.

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Practice Plotting Different Data Types with ggplot2

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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

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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

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Demonstrate proficiency in creating interactive data visualizations that communicate insights clearly and effectively.

Browse courses on Data Visualization

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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

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Put your skills to the test in a collaborative environment, fostering problem-solving and critical thinking abilities.

Browse courses on Data Science

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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

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Engage with the broader Data Science community by contributing to Tidyverse packages and sharing your knowledge with others.

Browse courses on Tidyverse

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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.