Chartered Financial Data Scientist
Become a professional expert in Data Science
Start: 24. November 2022
Early Bird: 30. September 2022
Price: from 5.900 € plus VAT
Data is the new oil
Creating value in financial services depends critically on the ability to draw reliable inferences from large quantities of heterogeneous data. Against this background, the Chartered Financial Data Science programme will provide you with essential tools and techniques for processing, analysing, and visualizing such information. You will learn to analyse time series and cross-sectional data in order to make forecasts, assess risks, perform simulations, and assign probabilities to possible events. Each of the relevant methods and modelling strategies is explained by one or more specific examples using real-world financial data. A comprehensive introduction to Python, one of the most popular programming languages for data science, will enable you to implement, extend and vary the techniques learned in accordance with the individual aims and needs.
You will significally enhance your abilities and career prospects in six distinct ways:
After passing the exam and conducting a three month project work, successful candidates are granted the title
CFDS® – Chartered Financial Data Scientist
Maschine Learning & AI
1. Introduction: Data Science
2. Exploratory Data Analysis
3. Multiple Linear Regression and Related Topics
4. Models with Binary Dependent Variables
5. Time-Varying Volatility and the Likelihood of Extreme Loss Events
6. Smoothed Bootstrap
7. Statistics with Python
1. Introduction to Machine Learning
2. Introduction to Python and ML Libraries
3. Supervised Learning and Logistic Regression
4. Unsupervised Learning and K-Means
5. Neural Networks and Deep Learning
6. Convolutional Neural Networks
7. Recurrent Neural Networks and Long Short Term Memory
8. Natural Language Processing
9. Reinforcement Learning
10. Data Centric AI
11. AI Canvas
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
The CFDS program consists of 2 in-class blocks. One Kick off at the beginning and a presentation at the end. In between you will have 15 live webinars as well as video material for your self-study. (Study time: approx. 145 hours).
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.
Managers and employees from the following segments
- Data Analytics
- Data Management
- Risk Management
- interface product management/ project management and IT
Sikandar Siddiqui is an economist and financial analyst specializing in asset valuation, risk management, and applied econometrics. Having gathered more than twenty years of professional experience in the fields of finance and management consulting, he currently serves as Head of Quantitative Methods at Deloitte Audit Analytics Germany.
Sikandar has authored or co-authored several articles on economic, statistical and financial issues. He holds a doctoral degree in economics from the University of Konstanz and has qualified as a CFA Charterholder.
Natalie Packham is Professor of Mathematics and Statistics at Berlin School of Economics and Law and Principal Researcher within the International Research Training Group “High Dimensional Nonstationary Time Series” (IRTG 1792) at Humboldt University Berlin. Natalie has several years of industry experience as a front office software engineer at an investment bank, and is frequently involved in industry-related research and consulting projects. Her research expertise includes Mathematical Finance, Financial Risk Management and Computational Finance, and her academic work has been published in Mathematical Finance, Finance & Stochastics, Quantitative Finance, Journal of Applied Probability and many other academic journals. She is associate editor of “Methodology and Computing in Applied Probability” and “Digital Finance” and co-chair of the GARP Research Fellowship Advisory Board. Natalie holds an M.Sc. in Computer Science from the University of Bonn, a Master’s degree in Banking & Finance from Frankfurt School, and a Ph.D. in Quantitative Finance from Frankfurt School.