Whether you’re an aspiring data scientist/analyst or a seasoned data science professional, my data science tutorials are designed to improve your R and Python coding skills. I assist aspiring and accomplished data scientists by offering tailored content based on the most up-to-date coding best practices. I strive to get you up and running and empower you to excel in your data science pursuits. Explore my tutorials and join my vibrant community of learners dedicated to continuous skill improvement.
Kyoto Cherry Blossom Trend (812-2015): Recreating Our World in Data’s Visualization
This tutorial aims to recreate an intriguing visual by Our World in Data. My objective is to practice and improve my visualization skills and help those new to R programming and the ggplot2 library (I could have done this with the pandas and seaborn libraries; however, I am more comfortable with the ggplot2).
Showcasing the dplyr case_when() and case_match() Functions
While reading Effective Pandas 2 by Matt Harrison, I learned about the new .case_when method in Pandas 2, which is a total life changer for pandas users who have longed for such a function for a long time. However, as an R programmer, I decided to review the dplyr case_when() documentation and learned that this “lifesaving” function has been updated.
Solving R 4 Data Science, 2nd-edition: Section 25.3.5 Exercises.
To practice R programming through hands-on exercises, as it is the best way to enhance your programming skills. In this tutorial, we will solve problems from Section 25.3.5 of the famous R 4 Data Science by Hadley Wickham et al.