In this course the learner will be shown how to generate forecasts of game results in professional sports using Python. The main emphasis of the course is on teaching the method of logistic regression as a way of modeling game results, using data on team expenditures. The learner is taken through the process of modeling past results, and then using the model to forecast the outcome games not yet played. The course will show the learner how to evaluate the reliability of a model using data on betting odds. The analysis is applied first to the English Premier League, then the NBA and NHL. The course also provides an overview of the relationship between data analytics and gambling, its history and the social issues that arise in relation to sports betting, including the personal risks.
This course is part of the Sports Performance Analytics Specialization
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Course 3 of 5 in the
Approx. 33 hours to complete
English
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 5 in the
Approx. 33 hours to complete
English
Offered by
Syllabus - What you will learn from this course
10 hours to complete
Week 1
10 hours to complete
8 videos (Total 86 min), 8 readings, 2 quizzes
7 hours to complete
Week 2
7 hours to complete
6 videos (Total 89 min), 3 readings, 1 quiz
9 hours to complete
Week 3
9 hours to complete
7 videos (Total 93 min), 3 readings, 1 quiz
6 hours to complete
Week 4
6 hours to complete
4 videos (Total 69 min), 4 readings, 1 quiz
About the Sports Performance Analytics Specialization

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