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.
About this Course
Learner Career Outcomes
34%
32%
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
Learner Career Outcomes
34%
32%
Offered by

Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
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.
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.
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.
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.
Reviews
TOP REVIEWS FROM THE DATA SCIENTIST’S TOOLBOX
It would be better if we can attempt the assignments even if we are not enrolled to the course. It would really help us to evaluate ourselves about the extent to which we have understood the concepts.
As a business student from Bangladesh who is aspiring to be a data analyst in near future, I love this course very much. The quizzes and assessments were the places to check how much I exactly learnt.
Great course for a beginner. But, since I knew most of the content from previous works, there was not much new learning for me. I am confident the later courses will enhance my knowledge a great deal.
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.
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