Create a Github repository
Explain essential study design concepts
Set up R, R-Studio, Github and other useful tools
Understand the data, problems, and tools that data analysts work with
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.
完成时间为 2 小时
During Week 1, you'll learn about the goals and objectives of the Data Science Specialization and each of its components. You'll also get an
overview of the field as well as instructions on how to install R.
16 个视频 （总计 51 分钟）, 5 个阅读材料, 1 个测验
完成时间为 1 小时
Week 2: Installing the Toolbox
This is the most lecture-intensive week of the course. The primary goal is to get you set up with R, Rstudio, Github, and the other tools we will
use throughout the Data Science Specialization and your ongoing work as a data scientist.
9 个视频 （总计 51 分钟）, 1 个测验
完成时间为 1 小时
Week 3: Conceptual Issues
The Week 3 lectures focus on conceptual issues behind study design and turning data into knowledge. If you have trouble or want to explore issues in more depth, please seek out answers on the forums. They are a great resource! If you happen to be a superstar who already gets it, please take the time to help your classmates by answering their questions as well. This is one of the best ways to practice using and explaining your skills to others. These are two of the key characteristics of excellent data scientists.
4 个视频 （总计 35 分钟）, 1 个测验
完成时间为 2 小时
Week 4: Course Project Submission & Evaluation
In Week 4, we'll focus on the Course Project. This is your opportunity to install the tools and set up the accounts that you'll need for the rest of
the specialization and for work in data science.