ML for Financial Data Structures

ML for Financial Data Structures

Course Description

This course is devoted to applications of machine learning for typical data structures that appear in financial scenarios. Due to the stochastic nature of this data, straightforward fitting machine learning to “predict something in the future” will inevitably fail and lead to money losses. This course will help finance and economics experts to apply ML methods correctly in their practice, and will broaden the horizons of their data science colleagues, and will introduce to them exceptional financial methods that are rarely discussed in classic machine learning materials. Topics:
  • The business of investments using science
  • Portfolio management with classic algorithmic methods
  • Portfolio management with ML-based investment methods
  • Typical mistakes did at financial forecasting
  • Correct ML pipeline for financial forecasting
  • Research directions and development plan
  • Exercises and “tournament”

Course tools

  • General Python scientific computing stack (NumPy, pandas, scipy, scikit-learn)


  • Intermediate applied mathematics skills
  • Intermediate scientific computing skills
  • Moderate understanding of financial markets is welcomed, but not necessary
Level of complexity of course Intermediate


Mr. Alex Honchar ML Director and Partner at Neurons Lab Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University. Fields of interests: Entrepreneurship, Computer Science, Life Sciences, Nature Sciences, Economics, Leadership Contacts:  

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