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Foundations of Sports Analytics

This course outlines the fundamental principles and key methodologies relevant to sports analytics problems. The course charts the significance of workflow and methodological structure in data analysis. Then, the importance of complementing human insight with quantitative methods is covered. We will delve into the quantitative aspects by exploring the nature of descriptive and predictive analytics. Last, we will discuss the role and importance of analytics as an entrepreneurial tool.  Students will learn to identify critical parameters of different analytics questions, understand how to analyze and interpret patterns using various measurement techniques, conduct statistical analyses, and quantify objective relationships present in data. This is the first course offered in the Sports Analytics Certificate, and will constitute part of the ‘core’ offerings that all certificate students will take. For assistance with registration, please contact sps.info@nyu.edu or 212-998-7150.

More details

You'll Walk Away with

  • The ability to demonstrate a basic understanding of analytic techniques, ranging from descriptive analytics to predictive and prescriptive techniques employed by advanced sports analytics professionals.
  • The ability to describe the advantages and weaknesses of the use of quantitative analytic techniques vs. qualitative analysis.
  • The ability to identify best practices for integrating analytic information into larger decision-making processes.
  • The ability to design and conduct research projects using data and modeling techniques to improve on-field and off-field performance.

2 sections

  • Spring 2024

    • Section

      001
    • Semester

      Spring 2024
    • Date

      Jan 29 - May 14
    • Day

      Self-Paced
    • Time

      Self-Paced
      • Online
    • Format

      Online
      • Online
    • Sessions

      N/A
    • Location

      Self-Paced
  • Summer 2024

    • Section

      001
    • Semester

      Summer 2024
    • Date

      May 28 - Aug 16
    • Day

      Self-Paced
    • Time

      Self-Paced
      • Online
    • Format

      Online
      • Online
    • Sessions

      N/A
    • Faculty

      TBA
    • Location

      Self-Paced