Data science with R training in Dhaka, Bangladesh
R is not only one of the most popular programming languages for data analysis and statistics but it is also an open-source interactive tool specifically designed to support data science. If you spend a lot of time slogging through Excel, it’s time to learn R.
To learn more about R and how people use it in their day to day work, meet our very own Data Scientist
When and why did you learn R?
I moved over to R after college when I lost access to Stata (a statistics software). At the time, I was working as a data analyst and I needed to use software that was free to use. My primary motivation came after I saw examples of the data visualization and statistical modeling in R; it was beyond the capabilities of any other software I had used in the past.
What are the benefits of R over other languages?
Compared to other statistical software programs, R is great because it is free and open source. Open source languages tend to be well supported with online documentation, which made it much easier for me to look up code examples. Compared to other programming languages, R has built-in functionality for statistics and a wide ecosystem of packages. This means you can do virtually any type of statistical analysis you can imagine in R. You might’ve heard about Python for data science. As a data scientist, it’s beneficial to learn a little bit of both R and Python, but choose whichever language works best for you to get started.
How do you use R on the day to day?
I use R to clean and analyze our user data. We have millions of users and we use data to help us understand how to better serve our learners. I use R Studio and the packages I use most often are dplyr for data cleaning and ggplot for data visualization. R has allowed me to automate workflows, create interactive data visualizations, forecast business metrics, and detect breakages on our website.
Any tips for learning R?
1) Learn the tidyverse! This will make your code a lot cleaner and easy to read. Techvila’s Learn R course will walk you through some tidyverse code to get you started.
2) Use data that’s interesting and/or meaningful to you. Using data you find interesting will make the learning process much more entertaining.
Our Learn R course covers key R syntax such as variables and vectors and how to expand the capability of your work space by importing a package. In the second lesson, we will tackle data cleaning and wrangling – two key prerequisites for performing sound data analysis.
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