R Programming : Data Analysis and Visualisations using R
This course will teach you R programming and how to program R in R Studio along with its usage for effective data analysis. You will be able to master the basics of R, including the lists, vectors, and data frames.
The course provides practical knowledge about programming in R, reading and writing data files into R, loading and installing the R packages, loading and working upon various datasets, data transformation techniques, creating and handling various data types, data analysis, and visualization by creating various kinds of plots. R is very actively used for statistical computing and designing. It is one of the most widely used languages in the data science sector.
Some of the big shot industries like Google, LinkedIn, and Facebook, rely on R for many of their operations. Many of the data-driven businesses and companies are using R programming as their core platform and are recruiting trained R programmers. It is a very powerful data visualization tool. So make sure you are up with the software trends.
This course was designed to be focused on the practical side of coding in R – instead of teaching you every function and method out there, I’ll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects.
This course is truly step-by-step. In every new tutorial, we build on what had already been 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.
By end of this course, you will be able to solve Industry Data Science projects in R starting including model building, model diagnostics, and presenting actionable business insights
Who this course is for:
- For the Beginners who want to learn the R programming language to perform data analysis and Visualization. It will be helpful for those who are curious about Data Science, as it is part of data science.
- Data Analysts
- Software Engineering Undergraduates
- Data Scientists
- Data Engineers
- R learners
- If you want to learn R by doing
- If you like exciting challenges