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.
Offered By
Johns Hopkins UniversityAbout this Course
2,121,792 recent views
Learner Career Outcomes
39%
started a new career after completing these courses
35%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 13 hours to complete
English
Subtitles: English, Korean
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 ScienceGithubR ProgrammingRstudio
Learner Career Outcomes
39%
started a new career after completing these courses
35%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 13 hours to complete
English
Subtitles: English, Korean
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
3 hours to complete
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.
3 hours to complete
5 videos (Total 40 min), 2 readings, 5 quizzes
5 videos
What is Data Science?9m
What is Data?6m
Getting Help10m
The Data Science Process9m
2 readings
Welcome5m
A Note of Explanation2m
5 practice exercises
What is Data Science?6m
What is Data?6m
Getting Help Quiz6m
Data Science Process8m
Module One Summative Quiz30m
3 hours to complete
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.
3 hours to complete
5 videos (Total 34 min)
6 practice exercises
Installing R8m
Installing R Studio4m
RStudio Tour8m
R Packages10m
Projects in R6m
Module Two Summative Quiz30m
2 hours to complete
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.
2 hours to complete
4 videos (Total 28 min)
4 videos
Github and Git8m
Linking Github and R Studio4m
Projects under Version Control4m
5 practice exercises
Version Control6m
GitHub and Git10m
Linking Git/GitHub and RStudio6m
Projects under Version Control8m
Module Three Summative Quiz30m
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.
5 hours to complete
4 videos (Total 34 min)
5 practice exercises
R Markdown10m
Types of Data Science Questions6m
Experimental Design14m
Big Data6m
Module Four Summative Quiz30m
Reviews
TOP REVIEWS FROM THE DATA SCIENTIST’S TOOLBOX
by SRMar 14, 2017
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.
by LRSep 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.
by CCJun 10, 2016
A good basic class and collection of the tools. I wish there had been a little more explanation of what we would use the software for, but I found the lecture parts to be both concise and informative.
by USOct 12, 2017
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.
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