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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Completing this course will count towards your learning in any of the following programs:
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
Syllabus
WEEK 1
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.
WEEK 2
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.
WEEK 3
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.
WEEK 4
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.
