Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2
- Learn to program in R at a good level
- Learn how to use R Studio
- Learn the core principles of programming
- Learn how to create vectors in R
- Learn how to create variables
- Learn about integer, double, logical, character and other types in R
- Learn how to create a while() loop and a for() loop in R
- Learn how to build and use matrices in R
- Learn the matrix() function, learn rbind() and cbind()
- Learn how to install packages in R
- Learn how to customize R studio to suit your preferences
- Understand the Law of Large Numbers
- Understand the Normal distribution
- Practice working with statistical data in R
- Practice working with financial data in R
- Practice working with sports data in R
- No prior knowledge or experience needed. Only a passion to be successful!
Learn R Programming by doing!
There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!
This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.
After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.
This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
I can’t wait to see you in class,
- This course is for you if you want to learn how to program in R
- This course is for you if you are tired of R courses that are too complicated
- This course is for you if you want to learn R by doing
- This course is for you if you like exciting challenges
- You WILL have homework in this course so you have to be prepared to work on it
Created by Kirill Eremenko, SuperDataScience Team
Last updated 12/2016
Size: 3.93 GB