The Data Scientist’s Toolbox
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The Data Scientist’s Toolbox
This course is part of multiple programs.
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34,096 reviews
What you'll 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'll gain
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
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.
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.
What's included
5 videos2 readings5 assignments5 plugins
5 videos•Total 40 minutes
- Why Automated Videos?•5 minutes
- What is Data Science?•9 minutes
- What is Data?•7 minutes
- Getting Help•10 minutes
- The Data Science Process•9 minutes
2 readings•Total 7 minutes
- Welcome•5 minutes
- A Note of Explanation•2 minutes
5 assignments•Total 126 minutes
- Module One Summative Quiz•30 minutes
- What is Data Science?•6 minutes
- What is Data?•30 minutes
- Getting Help Quiz•30 minutes
- Data Science Process•30 minutes
5 plugins•Total 75 minutes
- Why Automated Videos?•15 minutes
- What is data science?•15 minutes
- What Is Data?•15 minutes
- Getting Help•15 minutes
- The Data Science Process•15 minutes
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.
What's included
5 videos6 assignments5 plugins
5 videos•Total 34 minutes
- Installing R•6 minutes
- Installing R Studio•3 minutes
- RStudio Tour•7 minutes
- R Packages•12 minutes
- Projects in R•6 minutes
6 assignments•Total 180 minutes
- Module Two Summative Quiz•30 minutes
- Installing R•30 minutes
- Installing R Studio•30 minutes
- RStudio Tour•30 minutes
- R Packages•30 minutes
- Projects in R•30 minutes
5 plugins•Total 75 minutes
- Installing R•15 minutes
- Installing R Studio•15 minutes
- RStudio Tour•15 minutes
- R Packages•15 minutes
- Projects in R•15 minutes
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.
What's included
4 videos5 assignments4 plugins
4 videos•Total 28 minutes
- Version Control•11 minutes
- Github and Git•9 minutes
- Linking Github and R Studio•4 minutes
- Projects under Version Control•4 minutes
5 assignments•Total 150 minutes
- Module Three Summative Quiz•30 minutes
- Version Control•30 minutes
- GitHub and Git•30 minutes
- Linking Git/GitHub and RStudio•30 minutes
- Projects under Version Control•30 minutes
4 plugins•Total 60 minutes
- Version Control•15 minutes
- GitHub and Git•15 minutes
- Linking GitHub and RStudio•15 minutes
- Projects under version control•15 minutes
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.
What's included
4 videos5 assignments1 peer review4 plugins
4 videos•Total 34 minutes
- R Markdown•8 minutes
- Types of Data Science Questions•10 minutes
- Experimental Design•9 minutes
- Big Data•7 minutes
5 assignments•Total 150 minutes
- Module Four Summative Quiz•30 minutes
- R Markdown•30 minutes
- Types of Data Science Questions•30 minutes
- Experimental Design•30 minutes
- Big Data•30 minutes
1 peer review•Total 60 minutes
- Assemble your toolbox•60 minutes
4 plugins•Total 60 minutes
- R Markdown•15 minutes
- Types of Data Science Questions•15 minutes
- Experimental Design•15 minutes
- Big Data•15 minutes
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Reviewed on Jun 2, 2017
Nice Course. Basics are very well taught in this course.Thank you JHU and Coursera for this course. I have decided to donate 10% of my first salary to coursera once I am complete this and get intern.
Reviewed on Sep 21, 2017
Some of the course material seems a little out of order, and some things I went externally to figure out, but overall I think that it is a great class for someone looking to get into data science.
Reviewed on Aug 23, 2019
It would be helpful for absolute beginners who even have difficulty in installing programs like R and GitHub but otherwise it felt a bit too basic although informative with some of the Git commands
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