Pick up some top tips for learning R from Shelmith Kariuki, a certified Tidyverse instructor and a leader in the Africa R community.
If you’re just at the beginning of your journey learning R programming and you're looking for tips, there’s a lot you can learn from Shelmith Kariuki.
Shel has years of professional experience working in data science, and years of experience teaching statistics and data skills to others. She’s a data analyst, an RStuduo certified Tidyverse instructor, and a community leader — a co-organizer of NairobiR as well as a core team member at Africa R, the consortium of Africa-based R user groups.
In other words, she knows a lot about teaching and learning R. She recently took some time out of her schedule to speak with Dataquest about her own R learning journey and the advice she has for R learners today.
Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.
R is an increasingly popular programming language, particularly in the world of data analysis and data science. But learning R can be a frustrating challenge if you’re not sure how to approach it.
Learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.