Python for Financial Data Science

Learn the basics of Python, Jupyter, NumPy, pandas, matplotlib, plotly & more for financial data science.

This training is a part-time online training taking place over the course of about three weeks. The training is comprised of 6 live sessions of about 3 hours each. This represents the equivalent of 3 full training days.

The price for this training is
799 EUR (net of VAT).
The e-book "Python for Finance" (O'Reilly) is included.
Receive a discount of 10% (15%) when booking two (three) major trainings.

This training is available in the form of
self-paced video classes.

The Basics

Introduction to major tools and basic Python usage:
Module 1 — Quant Platform (cf., Jupyter Notebook (cf., first steps with Python
Module 2 — control structures in Python, selected Python idioms, data types & structures

Arrays & Visualization

Working with (large) arrays and visualizing data:
Module 3 — basics of NumPy ndarray objects, simple operations, vectorized code
Module 4 — visualization of numerical data with matplotlib, customizing plots, seaborn for statistical plotting


Using pandas for data science:
Module 5 — basics of pandas, DataFrame objects, Series objects, basic operations, basic plotting
Module 6 — advanced operations on DataFrame objects (e.g. join, merge, groupby), performance topics

Interactive Plots & IO Operations

Using plotly for interactive visualization and performant IO operations with Python:
Module 7 — basics of working with plotly, using cufflinks to combine plotly with pandas
Module 8 — basic input-output (IO) operations with Python, using HDF5, performance issues

Performance & Mathematics

Making Python fast and doing mathematics:
Module 9 — different Python idioms and their performance impact, performance libraries, static and dynamic compiling
Module 10 — approximation, convex optimization, integration, symbolic computation

Stochastics & Statistics

Basic stochastics and statistics with Python:
Module 11 — random numbers, Monte Carlo simulation, option valuation, stochastic risk measures
Module 12 — normality tests, portfolio optimization, PCA analysis