Course - Complementary Training - Page 5

• # Athlete Monitoring: Data Analysis and Visualization – Hello World in R

Athlete Monitoring: Data Analysis and Visualization Hello World in R As the tradition demands, when learning a new programming language, one needs to create the Hello World program. In this version of the Hello World, I am explaining the atomic vectors classes in R, as well some of the specific of the language (such as vectorized variables, vectors recycling...

• # Athlete Monitoring: Data Analysis and Visualization – Aggregating data in Excel

Athlete Monitoring: Data Analysis and Visualization Aggregating data in Excel We are going to use R and R-Studio thorough the rest of this course (as well as a little bit of Microsoft Excel), so in this lecture, I will show you how to install them on your computer. About Mladen Jovanovic Mladen Jovanovic is a physical preparation coach from...

• # Athlete Monitoring: Data Analysis and Visualization – Module 3

We are going to use R and R-Studio thorough the rest of this course (as well as little bit of Microsoft Excel), so in this lecture I will show you how to install them on you computer.

• # Athlete Monitoring: Data Analysis and Visualization – Comparing Individual to the Group

When we have multiple individuals sharing the same context we can check what is happening to the group but more importantly to check how is a given individual differing from a group.

• # Athlete Monitoring: Data Analysis and Visualization – Normalizing Individuals

In this lecture I will explain the few techniques that can be used to make the comparison more “fair”, particularly for the purpose of figuring out the change in individual monitoring.

• # Athlete Monitoring: Data Analysis and Visualization – Nominal Rolling Functions

In this lecture, I continue with the solution to the problem of dealing with nominal variables using rolling functions. The presented solution is to use dummy coding and aggregating session using means, which gives us rolling proportions.

• # Athlete Monitoring: Data Analysis and Visualization – Rolling Functions

Rolling functions are useful in describing trends over time. These can be any type of descriptive functions, like means, medians, standard deviations, and so forth.

• # Athlete Monitoring: Data Analysis and Visualization – Aggregation

In this lecture, I explain the basics of data aggregation and a few issues that you might stumble upon, as well as their solution.