Git and GitHub
In this course you will learn the basics of version control for data science with Git. Git is a free and open source distributed version control system designed to handle projects of different size, speed, and efficiency. Git has a tiny footprint with lightning fast performance. Not only that, but also, it’s great for anyone who wants to be working on code, making extra changes and still be able to go back to an earlier version.
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Section 1
Course Introduction
In this section, we look at why you should learn how to use Git for version control. Git is a particularly useful tool that will help you keep track of your projects and all the major changes you make in your code. GitHub, on the other hand, enables you to store your code online so you and others can have access to it to create a truly collaborative project. And if that's not enough, working with Git is also a near requirement for any data scientist today.
Section 2
Introduction to Git
Here, you will learn how to install and configure Git on your machines, whether you’re a MAC fan or you prefer to use Windows. We will go through the basic command that will give you a solid foundation and understanding of how Git works. You’ll learn how to keep a record of all major changes you make to a document. You will get acquainted with the use of notes while working on several versions of your document. In addition, we will show you how to revisit every record that you’ve made, and how to track its evolution. What is more, we will touch on Branching and will teach you how to work with several people on a single document.
Section 3
GitHub
In this section, we will cover GitHub and discuss some of the benefits of saving your documents online.
Section 4
Conclusion
A short overview of what we've discussed so far in the course, some parting words of encouragement and advice... and a sincere thank you!
Programming for Data Science
This course is part of Module 2 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. In contrast to the introductory nature of Module 1, Module 2 is designed to tackle all aspects of programming for data science. You will learn how to work with relational databases and SQL, as well as how to code in Python and R. By the end of this Module, you will have a versatile programming skill set.
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Practice
Real-life project and data. Solve them on your own computer as you would in the office.
Q&A Hub
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Certificates
Earn a verifiable certificate after each completed course. Celebrate your successes and share your progress with your professional network!