NYU, Oxford and Queen Mary University faculty unveil alternative data algorithms in sessions on investment data for fintech professionals

NEW YORK, April 5, 2022 /PRNewswire/ — NYU, Oxford Universityand QMUL professors team up to reveal alternative data algorithms in sessions on investing data (investmentdata.org).

Limited enrollment workshops provide hands-on training for fintech analysts in a live teaching format. Participants assemble custom workflows with proven code to create institutional investment products.

A unique emphasis on real-time format, advanced data analysis and an interactive tutorial environment are at the heart of the investment data sessions.

Workshop participants explore datasets such as consumer transactions, satellite imagery, and social media for investment decision making. They will learn how to solve everyday fintech challenges, including:

  • Image detection
  • Natural Language Processing (NLP)
  • Ticker Mapping
  • Revenue modeling
  • Transaction data debiasing

The organization is led by Said Amen (Cuemacro, Turnleaf Analytics, QMUL), Alexander Denev (TurnLeaf analysis, University of Oxford), Ekster gene (NYUAlternative Data Group), and advised by Petter Kolm (NYU)

“This is the first time a class has exposed the intricate details of how institutional investors leverage non-traditional datasets to improve performance,” said Ekster genewho teaches alternative data in the Master of Mathematics in Finance program at New York University’s Courant Institute.

Read the full program at investmentdata.org

Media Contact:
Ekster gene
[email protected]

SOURCE Investment Data Sessions

Sharon D. Cole