May 1, 2024
3 minute read
Tidy Data is a set of principles for organizing and manipulating data in a way that makes it easy to read, understand, and analyze. It is based on the idea that data should be arranged in a consistent and predictable manner, so that it can be easily processed by computers and humans alike.
Why Learn Tidy Data?
There are many reasons why you might want to learn Tidy Data. Here are a few:
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Find a path to becoming a Tidy Data. Learn more at:
OpenCourser.com/topic/kynkv3/tidy
Reading list
We've selected 11 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
Tidy Data.
Comprehensive introduction to the tidy data principles and how to apply them using the R programming language. It is written by Hadley Wickham, the creator of the tidyverse, a popular set of R packages for data science.
Practical guide to using R for data science. It covers the basics of R, including data manipulation, visualization, and modeling. It also discusses more advanced topics such as machine learning and tidy data.
Comprehensive guide to Pandas. It covers all aspects of Pandas, from the basics to advanced topics such as data manipulation and visualization.
Comprehensive guide to R programming. It covers all aspects of R, from the basics to advanced topics such as object-oriented programming and parallel computing.
Practical guide to data manipulation with R. It covers the basics of data manipulation, including data import, cleaning, and transformation.
Practical guide to exploratory data analysis with Python. It covers the basics of exploratory data analysis, including data visualization and statistical analysis.
Is an introduction to data science with R. It covers the basics of data science, including data collection, cleaning, and analysis.
Practical guide to data visualization with Python. It covers the basics of data visualization, including matplotlib and seaborn.
Comprehensive guide to machine learning with Python. It covers all aspects of machine learning, from the basics to advanced topics such as deep learning.
Practical guide to exploratory data analysis with R. It covers the basics of exploratory data analysis, including data visualization and statistical analysis.
Practical guide to machine learning with R. It covers the basics of machine learning, including supervised and unsupervised learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/kynkv3/tidy
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