Ever wondered how teams determine the worth of Messi or Ronaldo during transfer season? Want to learn how teams determine the worth of each player?

 

You’ve got to catch us live for a power-packed webinar on “Predicting the Transfer Market Value of a Football Player” with Peyman Hassari (Senior Data Scientist at ATB Financial) as he discusses how data science algorithms can be used to determine the transfer prices for the players. This webinar is also an opportunity for you to learn more about MIT Institute for Data, Systems, and Society's Data Science and Machine Learning program. 

 

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Agenda for the session

  • Use data science to predict a footballer’s transfer market value
  • Elements of a good data science and machine learning program
  • The MIT IDSS' program experience - from an expert’s perspective
  • Live Q&A session

About Speakers

Peyman Hessari

Senior Data Scientist, ATB Financial


Peyman Hessari is a seasoned data science professional who has over 10 years of experience and is currently working as a Senior Data Scientist at ATB Financial. Math and computing are his heart and soul, and he has a long history of teaching data science and machine learning at various universities in South Korea and Canada. His areas of expertise include machine learning, functional programming, statistical modeling, and computational mathematics. He is also a mentor and coach for the MIT Institute for Data, Systems, and Society Data Science and Machine Learning program.

Data Science and Machine Learning: Making Data-Driven Decisions Program

The Data Science and Machine Learning: Making Data-Driven Decisions Program has a curriculum carefully crafted by MIT faculty to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions.

This data science program has been designed for the needs of data professionals looking to grow their careers and enhance their data science skills to solve complex business problems. In a relatively short period of time, the program aims to build your understanding of most industry-relevant technologies today such as machine learning, deep learning, network analytics, recommendation systems, graph neural networks, and time series.