Data Tidying (The Course)
Data science is one of the most exciting and fastest growing careers in the world. The goal of this series is to help people with no background and limited resources transition into data science. It would be helpful to have already taken our Organizing Data Science Projects, Version Control and Introduction to R courses. We guide you through the rest!
After taking this course you will be able to:
- Explain what raw and tidy data are
- Transform messy data sets into tidy data sets
- Work with strings, factors and dates in R
Things you need to do this course
This course is designed for people with no background with Chromebooks. It would be helpful if you had already taken our Organizing Data Science Projects, Version Control and Introduction to R courses. This should be a great introduction to data tidying for high-school students or people looking for a career change into the tech industry. The only requirements are:
- A computer with a web browser and an internet connection
- The ability to type and follow instructions.
- The accounts that you have set up in previous courses
How you will be graded
The course has a series of short quizzes, one for each chapter. You will get two attempts at each quiz and your best score for each quiz will count toward your final score. If you receive more than 70% of the points across all quizzes you will pass. If you receive more than 90% of the points across all quizzes you will pass with honors. You get two attempts at the class with each class purchase.
How to report an error
If you find a bug, typo, or issue in the material, feel free to contact us using this form.
- 1 What is data?
- 2 Data in R
- 3 Tidy Data
- 4 Untidy Data
- 5 Reshaping Data
- 6 Tidying Data
- 7 Working with Strings
- 8 Working with Factors
- 9 Working with Dates
- 10 Data Tidying Project
- 12 References
- About this Course
- About the Authors
Jeff is a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he has helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and Mortimer Spiegelman Award recipient.
Leslie obtained her PhD in biostatistics from the Johns Hopkins Bloomberg School of Public Health and is currently an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at Macalester College.
This course has a private forum for learners who are taking this course.
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