Chevron Left
Zurück zu Foundations of Sports Analytics: Data, Representation, and Models in Sports

Kursteilnehmer-Bewertung und -Feedback für Foundations of Sports Analytics: Data, Representation, and Models in Sports von University of Michigan

4.5
Sterne
33 Bewertungen
11 Bewertungen

Über den Kurs

This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket). This course does not simply explain methods and techniques, it enables the learner to apply them to sports datasets of interest so that they can generate their own results, rather than relying on the data processing performed by others. As a consequence the learning will be empowered to explore their own ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer. While the course materials have been developed using Python, code has also been produced to derive all of the results in R, for those who prefer that environment....

Top-Bewertungen

KY
26. Aug. 2021

Great course. Although this course focuses on sports analysis, the analyzing process I learned from it can apply to any other areas of analysis.

KJ
5. Juli 2021

Best course to interact with data representation programming and libraries, especially for the great sports fan.

Filtern nach:

1 - 11 von 11 Bewertungen für Foundations of Sports Analytics: Data, Representation, and Models in Sports

von Robert B

14. Juni 2021

Great course! The lectures focus on the hands-on application of analytics techniques; with slides of theory and math kept to a minimum. The quizzes are of easy to moderate difficulty, and help reinforce the concepts learnt in the lecture. The material of the course will be of interest to anyone who enjoys extracting insights from data, even if you aren't much of a sports fan. I’m excited for the next course in the specialization!

von Daniel G S

14. Juni 2021

Overall it was a good course. It covers the basic of Sports Analytics and it has a lot of good examples and datasets.

von Manuel F P T

23. Juni 2021

Me parece un excelente curso para un primer vistazo sobre como analizar el rendimiento tanto individual como de equipo en deportes, me pareció muy interesante mezclar habilidades de ciencia de los datos junto con mis gustos por los deportes.

von Henri

5. Sep. 2021

Great material and well paced for people working. One instructor is a bit green though.

von Kevin . H

21. Sep. 2021

T​his course does a very poor job of actually teaching the concepts presented in it. There is a lot of "copy/paste" instruction, with almost no explanation of what is being done or why, at either the technical level (i.e. why are calling this function or method) or a the theoretical level (i.e. why a visualization or data interaction is useful). It does touch on a many basic foundation for interacting with data (which is the only reason I'm not giving it 1 star), but from an actual instruction perspective, this the worst courses I have finished on Coursera.

von Ka P ( Y

27. Aug. 2021

Great course. Although this course focuses on sports analysis, the analyzing process I learned from it can apply to any other areas of analysis.

von Kwanghyun J

6. Juli 2021

B​est course to interact with data representation programming and libraries, especially for the great sports fan.

von Fabrizio L F

13. Juni 2021

I've never been more excited of doing a regression model in my life! Amazing content.

von Aromal K

4. Aug. 2021

verygood

von Hagar S

7. Sep. 2021

perfect

von Cy L

4. Aug. 2021

Directions are not clear when working on programing assignments and quizzes.