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.
提供方
課程信息
學生職業成果
12%
您將學到的內容有
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
您將獲得的技能
- Data Science
- Github
- R Programming
- Rstudio
學生職業成果
12%
提供方

约翰霍普金斯大学
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.
授課大綱 - 您將從這門課程中學到什麼
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.
審閱
- 5 stars69.40%
- 4 stars23.30%
- 3 stars5.29%
- 2 stars1.08%
- 1 star0.90%
突出顯示
來自数据科学家的工具箱(中文版)的熱門評論
Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.
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
It's a very introductory course and in a sense I don't feel like I learnt something useful, except the part that shows how to install all the tools that are needed for the rest of the Specialization.
Great course content and very much informative with the different options of learning either through text or video. A good introductory course to the Data Science: Foundations Using R Specialization.
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