Focus on relevant content any practitioner in sport needs, to apply your skills to real-world projects sooner.
Test your practical understanding on a weekly projecting, ensuring you grasp each concept before moving on.
Learn on real sport science data, instead of penguin or car data, to give you a more real world experience that translates to sport.
R for Sport Science combines 5+ years of sport science experience at the highest level into a 5 week online course.
I will guide you through each step, teaching you what sport professionals actually use in R. Commit just 5 weeks and you'll gain all the coding skills sport scientists need.
Data wrangling is a crucial step in sports performance analysis, allowing you to transform and manipulate raw data into a manageable dataset. This week, you'll dive into data wrangling techniques using R.
Set up a cloud server to access and upload your data from.
Learn and apply the most common strategies for cleaning and preparing data.
Functions of dplyr package (select, filter, mutate, summarize, and group_by).
Functions of tidyr package (pivot_longer, pivot_wider).
Descriptive statistics allow you to create normative data that can be used for benchmarking players and setting up key performance indicators.
Learn how to compute descriptive statistics for sports data using R.
Analyze key performance metrics and compare player or team performance using statistical measures.
Visualizations play a crucial role in understanding and communicating sports data insights effectively.
Explore data visualization techniques using the ggplot package in R.
Create visually appealing and informative plots to visualize sports performance trends and patterns.
Hi, my name is Daniel. As a sport scientist in the NBA with experience spanning from college to professional sports, I want to help you take the next step in your Sport Science journey!
My R journey involved learning from online tutorials, books, mentorship, and extensive troubleshooting of my own code over hundreds of hours. This course condenses all my education and experience to prioritize exactly what you need to know as a sport scientist, ultimately saving you precious time and providing clear directions.
As a student with little coding background, I didn't know where to begin. Daniel’s expertise and experience in professional sports is invaluable. I was able to build a solid foundation in R, both for sports science specifically and more generally. Now, I feel comfortable experimenting with different datasets, customizing analyses, and troubleshooting issues. This course is perfect for anyone looking to break into sports data science.
Research Coordinator @ WHOOP
"I recently passed my CPSS exam and was looking for ways to expand upon my knowledge in a practical manner. I thought this course was very helpful in providing the framework for how to utilize coding in R to analyze and visualize data on a basic level. I would highly recommend this course, especially if you have minimal experience with R."
Doctor of Physical Therapy Student @ UMiami
"These are extremely relevant tasks and help individuals just getting into coding & sport science get an idea of what the sport scientists are tasked with in the professional realm."
"I’ve been loving this R course. I struggled to get started because other R courses focused on irrelevant data(sales data), and I couldn’t apply those lessons to CMJ and sport science data. This course was exactly what I needed, it’s tailored specifically to sport science and made everything click."
Head of Performance @ Orlando Pride
"I had a great experience learning more about data cleaning and visualization. My first R experience was in graduate school and it was cleaning biological data and I never found it applicable to sport science and how to use it within S&C. Now, this course has narrowed my focus on which packages to use and how to clean data efficiently."
$299
✨ Step by step video tutorials
✨ R and RStudio Installation
✨ Data Workflow & Wrangling Hygiene in Sports
✨ Descriptive Statistics for Profiling Athletes
✨ Data Visualization for Athlete Trends
✨ 4 projects to practice learning
Learning to build data models is a valuable skill when turning data into insights.
You will have lifetime access.
R is a free open source coding language you can download here: https://posit.co/download/rstudio-desktop/. We walk you through it in the first lesson.
This would give you the hard skills to code as a professional sport scientist.
I use a PC to teach this course, but you can also follow along on a Mac!
You will need a computer to code in R & RStudio, but the lessons can be accessed through a tablet (ios and android).
The lessons are pre-recorded for you to go through on your own time. Live video calls are available when you run into problems with code!
You don't need any prior knowledge or experience of R. You can be anything ranging from a beginner to an expert and learn somethin from this course.
Yes you will get a certificate upon completion!