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Learn R By Intensive Practice.
R is known to have a steep learning curve and the explanations in most tutorials are often vague and high level. But this course is different. The concepts are structured in a step-by-step fashion where one concept leads to the next logical topic and build on it. All topics close with an associated coding challenge similar to what you’ll encounter in real world.
By the end of the course, you will not only understand how they work but you will feel comfortable to do any sort of data manipulation you can imagine. This kind of ability requires a lot of practice. Studies show that if you practice what you learnt within 24 hours of learning it, your understanding lasts longer and you gain the ability to instinctively apply what you learnt in the real world.
That is why at the end of most lessons, you are posed a coding challenge and asked you to solve before moving to the next topic. I sincerely hope you take these challenges seriously. It matters less if you get the answer in a minute or an hour. What matters is that you make an honest attempt. Besides, I reveal the answer at the end of the videos.
What are the requirements?
- High school level math skills will be good.
- This course is for everyone, right from college students using R for a project to statisticians, programmers from other platforms, or pure beginners without any prior programming experience who want to become data analysts or data scientists.
What am I going to get from this course?
- Do any sort of manipulation with datasets
- Create and master the manipulation of vectors, lists, dataframes, and matrices
- Write conditional control structures, debug and efficiently handle errors
- Confidently write apply() functions and design any logic within the apply function.
- Handle dates using lubridate and manipulate strings with stringr package
- Melt, reshape, aggregate, and make pivot tables from dataframes
Who is the target audience?
- If you are a college student working on a project using R
- If you are a statistician, but you don’t have prior programming experience
- If you are a programmer coming from other platform (such as python, SAS, SPSS) and you are looking to get your way around in R
- You have a software / DB background, and would like to expand your skills into data science and advanced analytics
- You are a beginner with no stats background whatsoever, but have a critical analytical mind and have a keen interest in analytical problem solving.