In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
R Markdown
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Skills You'll Learn
Data Science, Github, R Programming, Rstudio
Reviews
4.6 (33,855 ratings)
- 5 stars69.45%
- 4 stars23.20%
- 3 stars5.26%
- 2 stars1.10%
- 1 star0.95%
KC
Jan 3, 2021
This course spoon-fed me as a beginner, which was great. The quizzes were well-done because they brought to light any weakness or gaps in my understanding so I could go back and review the processes.
AI
Apr 23, 2018
This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.
From the lesson
R Markdown, Scientific Thinking, and Big Data
During this final module, you'll learn to use R Markdown and get an introduction to three concepts that are incredibly important to every successful data scientist: asking good questions, experimental design, and big data.
Taught By
Jeff Leek, PhD
Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics in Public Health Sciences
Roger D. Peng, PhD
Professor of Statistics and Data Sciences
Brian Caffo, PhD
Professor, Biostatistics