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
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Learner Career Outcomes
12%
got a tangible career benefit from this course
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 18 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
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
- R Programming
- Rstudio
Learner Career Outcomes
12%
got a tangible career benefit from this course
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 18 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
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
5 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.
5 hours to complete
5 videos (Total 40 min), 2 readings, 5 quizzes
5 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.
5 hours to complete
5 videos (Total 34 min)
4 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.
4 hours to complete
4 videos (Total 28 min)
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)
Reviews
- 5 stars69.40%
- 4 stars23.30%
- 3 stars5.30%
- 2 stars1.09%
- 1 star0.89%
HIGHLIGHTS
"Foundational tools" in 243 reviews
"Introductory course" in 1056 reviews
TOP REVIEWS FROM THE DATA SCIENTIST’S TOOLBOX
by MKOct 14, 2018
This course is perfect for pure beginners, because it introduces the student to what Data Science really is and what a skills and knowledge a student should possess in order to be a Data Scientist.
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 WCSep 25, 2017
I really don't know much about this stuff, I think the jury's still out on whether the last four weeks will be helpful in the future. We'll see how much I think I've learned at the end of the course
by MWSep 10, 2019
Very clear and concise and is very easy to follow for those who aren't very experienced with setting up a dev environment or git. A little on the easy side but I'm sure more challenges are to follow!
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