The course, Master The Data Scientist’s Toolbox gives an overview of the data, questions, and tools that data analysts and data scientists work with.
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About this Course
In this course, Master The Data Scientist’s Toolbox you will get an introduction to the main tools and ideas in the data scientist’s toolbox. 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.
What You Will Learn
- Set up R, R-Studio, Github, and other useful tools
- Understand the data, problems, and tools that data analysts use
- Explain essential study design concepts
- Create a Github repository
Skills You Will Gain
- Data Science
- Github in Master The Data Scientist’s Toolbox
- R Programming
- Rstudio
Syllabus
WEEK 1
5 hours to complete
Master The Data Scientist’s Toolbox: Data Science Fundamentals
In this module, we’ll introduce and define data science and data itself. We’ll also go over some of the resources that data scientists use to get help when they’re stuck.
5 videos (Total 40 min), 2 readings, 5 quizzes
WEEK 2
5 hours to complete
Master The Data Scientist’s Toolbox: R and RStudio
In this module, we’ll help you get up and running with both R and RStudio. Along the way, you’ll learn some basics about both and why data scientists use them.
5 videos (Total 34 min)
WEEK 3
4 hours to complete
Master The Data Scientist’s Toolbox: Version Control and GitHub
During this module, you’ll learn about version control and why it’s so important to data scientists. You’ll also learn how to use Git and GitHub to manage version control in data science projects.
4 videos (Total 28 min)
WEEK 4
5 hours to complete
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.
4 videos (Total 34 min)
