ΠΕΡΙΓΡΑΦΗ

The principles and applications of digital technologies are developing at a fast pace. They have the capacity to improve the efficiency and potential of many current business processes and computational aspects in finance. Experienced and new professionals in the field of finance can benefit by improving their programming and scientific programming skills to be able to take advantage of this potential and follow progress.

This course addresses this need by introducing the most widely used and developed programming language in the field of scientific, financial, and data science, namely Python. The course also introduces the main scientific libraries of Python and third-party libraries to manage databases, visualize data, and perform efficient computations. It also gives several examples of financial computing, such as time-series analysis for the stock market, Monte-Carlo simulations to value investments, as well as linear and non-linear optimization for pricing.

ΣΚΟΠΟΣ ΣΕΜΙΝΑΡΙΟΥ

• Python language at the intermediate level and some advanced aspects
• Programming language principles applied to the Python language
• The standard math library of Python
• Third-party libraries of Python for scientific computing

ΣΕ ΠΟΙΟΥΣ ΑΠΕΥΘΥΝΕΤΑΙ

Professionals, directors, GMs, VPs, managers, with responsibility in the following functional areas:

• Data science
• Software engineering
• Business & Financial Analysts
• Industry Consultants
• Heads of Business Units & Business Planners Professionals who need to enhance their programming skills to improve business processes
• Any professional seeking to improve their programming skill set

ΠΕΡΙΣΣΟΤΕΡΕΣ ΠΛΗΡΟΦΟΡΙΕΣ

Benefits for you

• To perform Numerical computing in Python
• To use Monte-carlo simulations in Python
• Manage databases and visualize financial data
• Programming for simple natural language processing tasks
• Select appropriate programming methodology considering time and space requirements

Trainer

Dr. Stathis Hadjidemetriou

Dr. Stathis Hadjidemetriou received his Ph.D. in computer science also from Columbia University. His thesis was in the fields of image analysis and computer vision. He has also a B.Eng. (Honours) in Electrical Engineering from McGill University in Montreal and an M.Sc. in Electrical Engineering from Columbia University in the City of New York. As post-doctoral fellow at Yale University and as research scientist at University hospitals he specialized in bio-medical image analysis. He has worked on image analysis for biological microscopy related to cancer. He has also worked on organ level biomedical MRI for neurological conditions of the brain as well as for extending the use of quantitative MRI for the whole body.