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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/sports-analytics
课程评论：没有评论
课程名称：体育表现分析 课程概述： 体育分析已成为一个研究领域，因其在现实生活中的成功而日益受到关注，例如畅销书及电影《点球成金》。运动队和运动员表现数据的分析正在彻底改变体育行业的各个方面，包括比赛场地、客厅中的幻影运动玩家以及线上体育博彩。 本课程将利用美职棒（MLB）、美国国家篮球协会（NBA）、美国国家冰球联赛（NHL）、英超联赛（EPL-足球）和印度超级联赛（IPL-板球）的真实数据集，学习如何构建预测模型以预测团队和运动员的表现。您将复制《点球成金》的成功，使用真实的统计模型，利用线性概率模型（LPM）预测竞技比赛中的分类结果变量，探索团队如何通过可穿戴技术收集和组织运动员的表现数据，以及如何在体育分析中应用机器学习。 该课程旨在为体育经理、教练、物理治疗师以及希望了解运动员表现和比赛预测背后科学的体育爱好者提供基础知识。新手Python程序员和数据分析师也可以通过本课程以有趣且实用的方式应用他们的Python、统计学或预测建模技能。 应用学习项目： 学习者将应用所学的方法和技术于体育数据集，生成自己的结果，而不是依赖他人的数据处理。这使学习者能够探索自己对于体育团队表现的想法，通过数据进行测试，从而成为体育分析的生产者，而不是消费者。 证书： 完成课程后，学员将获得可分享的证书。 特点： - 100%在线课程，随时学习 - 灵活的学习时间安排 - 课程持续时间约为7个月，建议学习进度为每周6小时 适合人群： 课程为中级水平，建议学习者在开始前对Python有一定的了解，推荐先修完《Python入门专项》。 详细课程链接及课程列表： 1. 体育分析基础：数据、表现和模型 2. 点球成金及后续分析 3. 体育数据预测模型 4. 可穿戴技术与体育分析 5. 体育分析中的机器学习介绍 如需了解更多信息，请访问Coursera的官方网站。
Course Link: https://www.coursera.org/learn/foundations-sports-analytics
Name:Foundations of Sports Analytics: Data, Representation, and Models in Sports
Description:Offered by University of Michigan. This course provides an introduction to using Python to analyze team performance in sports. Learners will ... Enroll for free.
Course Link: https://www.coursera.org/learn/moneyball-and-beyond
Name:Moneyball and Beyond
Description:Offered by University of Michigan. The book Moneyball triggered a revolution in the analysis of performance statistics in professional ... Enroll for free.
Course Link: https://www.coursera.org/learn/prediction-models-sports-data
Name:Prediction Models with Sports Data
Description:Offered by University of Michigan. In this course the learner will be shown how to generate forecasts of game results in professional sports ... Enroll for free.
Course Link: https://www.coursera.org/learn/wearable-technologies
Name:Wearable Technologies and Sports Analytics
Description:Offered by University of Michigan. Sports analytics now include massive datasets from athletes and teams that quantify both training and ... Enroll for free.
Course Link: https://www.coursera.org/learn/machine-learning-sports-analytics
Name:Introduction to Machine Learning in Sports Analytics
Description:Offered by University of Michigan. In this course students will explore supervised machine learning techniques using the python scikit learn ... Enroll for free.
About this Specialization
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Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball. Analysis of team and player performance data has continued to revolutionize the sports industry on the field, court, and ice as well as in living rooms among fantasy sports players and online sports gambling.
Drawing from real data sets in Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL-soccer), and the Indian Premier League (IPL-cricket), you’ll learn how to construct predictive models to anticipate team and player performance. You’ll also replicate the success of Moneyball using real statistical models, use the Linear Probability Model (LPM) to anticipate categorical outcomes variables in sports contests, explore how teams collect and organize an athlete’s performance data with wearable technologies, and how to apply machine learning in a sports analytics context.
This introduction to the field of sports analytics is designed for sports managers, coaches, physical therapists, as well as sports fans who want to understand the science behind athlete performance and game prediction. New Python programmers and data analysts who are looking for a fun and practical way to apply their Python, statistics, or predictive modeling skills will enjoy exploring courses in this series.
Applied Learning Project
Learners will apply methods and techniques learned to sports datasets to generate their own results rather than relying on the data processing performed by others. As a consequence the learner 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.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Hours to complete
Approximately 7 months to complete
Suggested pace of 6 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Hours to complete
Approximately 7 months to complete
Suggested pace of 6 hours/week
Available languages
English
Subtitles: English
