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.
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R Markdown
Skills You'll Learn
Critical Thinking, Github, Data Management, Data Analysis, Computer Programming, Data Science, Rstudio, Computer Programming Tools, User Experience, Data Analysis Software, R Programming
Reviews
4.6 (34,011 ratings)
- 5 stars69.45%
- 4 stars23.18%
- 3 stars5.26%
- 2 stars1.11%
- 1 star0.97%
AK
Mar 11, 2020
I would like to thank Coursera and Johns Hopkins University for the course, I learned new things in the course which I think is the next step which can help me to accomplish my future goals.Thank you.
LR
Sep 7, 2017
It was really insightful, coming from knowing almost nothing about statistics or experimental design, it was easy to understand while not feeling shallow. Just the right amount of information density.
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