Chartered Financial Data Scientist
Become a professional expert in Data Science and learn from the best
Contact
Stefan Schummer
Start date
next start in spring 2022
Downloads
Data is the new oil
You will significally enhance your abilities and career prospects in six distinct ways:
- Understand the implications of the gradual shift from the assumption based decision making of the 20th century to the evidence-based, data-driven decision making of the 21st century.
- Learn to critically assess the information value of a variety of different data sets based on the data source and scientific characteristics.
- Learn to understand asset management as a data–analysis–decision–data process, including general knowledge of the most useful statistical procedures for explaining the variation of asset prices.
- Enjoy a practical session of training in the currently most popular programming language of Financial Data Science: Python.
- Will be introduced into the world of Big Data, machine learning, and deep learning methods to source insights from these data riches.
- Learn how to visualize and communicate valuable insights gained through Financial Data Science.
After passing the exam and conducting a three month project work, successful candidates are granted the title
CFDS® – Chartered Financial Data Scientist
Content
a) Introduction to Financial Data Science
b) Exploring and Analysing Data
c) Data & Asset Management: does the asset create data or is independent data the asset?
d) The Science of Data
e) Understanding Asset Management from a financial data science perspective
f) Statistical Analysis of asset price variation
g) Python for Financial Data Science
h) Big Data Storage and Retrieval
i) Machine Learning
j) Deep Learning
k) Data Visualization and Communication of Outcomes
Timestructure
The CFDS programme consists of 3 in-class blocks of two days each. The first two blocks are workshops addressing the main topics of financial data science and will give an introduction to data analysis using Python. Each of these blocks will be followed by weeks of self study with providedreadings.
This knowledge in financial data science will be subject to a two hour multiple choice exam.
All students passing that exam will then start a three months project work, that means analysing a real or fictive data set using Python. The results of the project work will be presented in a two-day closing session.
Target group
Managers and employees from the following segments
- Data Analytics
- Data Management
- Risk Management
- Marketing/Sales
- Trading
- Compliance/Regulation
- IT
- interface product management/ project management and IT
Our Experts
Professor Andreas Hoepner
- Professor of Operational Risk, Banking & Finance at University College Dublin
- Visiting Professor of Financial Data Science, University of Hamburg
- Visiting Professor of Finance, ICMA Centre, Henley Business School
- Head, Practical Tools research group, Mistra Financial Systems (MFS) research consortium [5 projects, total funding: 58 million SEK ~ US$ 7 million]
- Member, Technical Expert Group on Sustainable Finance, DG FISMA, European Commission, EU
- Invited Fellow, Royal Society of Arts
- Inventor, US Patent 8751357 B1: investment performance measurement
Professor Damian Borth
- 2018 - today Professor of Artificial Intelligence & Machine Learning at University of St. Gallen
- 2012 - today Co-Founder Sociovestix Labs – First in Financial Data Science
- 2018 Deep Learning Research Amplitude Deep Learning Lab
- 2015 - 2018 Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
- 2016 - 2018 PI NVIDIA AI Lab (NVAIL)
- 2016 - 2018 Director Deep Learning Competence Center
- 2015 - 2017 Head of Multimedia Analysis & Data Mining