Free Introduction to R Programming on Sports Data course

Free for Limited Time Only   Introduction to R Programming on Sports Data

I want to show you how easy it is to create predictive models using sports data


Complete a project that uses NFL data to determine the most important positions
Learn web scraping with R and Python
Read xls files into R
Know the basics of dataframes, along with manipulating, merging, and combining them
Split data into training, validation, and test sets, along with understanding cross-validation
Be aware of the problem of overfitting when generating predictive models
Learn the basics of Linear regression and Lasso regression
Learn the basics of Random Forests
Generate data visualization using ggplot2

Are you interested in learning about data analysis and machine learning, but don’t know where to start? Are you interested in sports and curious to know how analytics can be applied to sports? In the game of football, are you curious as which positions are the most important (other than the quarterback)?

If so, you’ve come to the right course! In this course, I will show you how easy it is to use the statistical software program R Studio in order to use data from the NFL to answer the question of which positions matter the most in the game of football! I will work you through this project so you learn about R by doing, as opposed to watching boring lectures that cover theory without any applications

My hope is that going over this project will provide the interest and motivation necessary for you to answer your own statistics and data-related questions using the concepts I cover in this course. I want you to become proactive instead of just being spectators and consumers

Course Published  By Jerry Kim (Average rating- 3.6/5, Total Ratings- 11 )

Short Biography of Instructor:

Jerry Kim has a MA in Physics from the University of Texas at Austin, along with a BA in Physics and BS in Applied Mathematics from the University of California at Los Angeles. He taught Physics courses for 2 years as a teaching assistant at the University of Texas at Austin. He has experience with programming, computational physics, and data science.
He wants to provide his experience and knowledge of programming and data analysis to others. He currently works as a freelancer for data science projects.

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