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Data Analytics for Finance I & II


Gruber P.

Course director


The course is organized weekly sessions of four hours. There are two parts:

Part 1: Financial Data – in the first half of the semester, we will study the answer to the following questions

  1. What is data
    – Nature and theory of data, data generating process, measurement, variables, data types
    – Scientific papers that make innovative use of data
    – Which financial questions can be answered with data
  2. Where to get data
    1. Traditional data sources: Computstat, CRSP, Optionmetrics, Markit, Factset, Bloomberg
    2. Alternative data sources: Quandl, microblogging, search engines, blockchain
    3. Historic datasets
    4. How to collect your own data
  3. How to check, organize and store data
  4. How to curate a dataset for AI training
  5. How to analyze data
    – Introduction to relational databases
    – Exploratory data analysis and an introduction to nonparametric statistics
  6. How to extract (even more) meaningful variables
    Derived quantities, aggregation
  7. Which econometric models can overcome data shortcomings
    Robust statistics, seasonality
  8. How to make money with data
    Data business models
  9. How not to make money with data
    Sharing data (when, why, how), open research data principles

Part 2: Data visualization  – the second half of the semester is entirely dedicated to data visualization, one of the most powerful tools for analyzing data and communicating your results

  1. Visualization theory
    Perception and aesthetics, color, the grammar of graphics
  2. Static visualizations
    Bar charts, scatter plots, pie charts, line carts, Sankey diagrams, Parallel coordinate plots,
  3. Statistical visualizations
    Box and violin plots, qq-plot, histograms, tree maps, forest plot, autocorellograms, Lorenz curves, Venn diagrams,
  4. Data maps
    Dot distribution maps, heat maps, choropleths and alternative maps: cartograms, grid and hexagon maps, statebins
  5. Interactive visualizations
    Basic web technology, user interaction, R shiny
  6. AI tools for data analysis and visualization


Tukey (1962) defines Data Analysis to be “Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

The goal of this course is 

  1. to provide the students with the tools and thinking framework for data analysis
  2. to foster creativity in using and exploring existing datasets as well as in creating new datasets
  3. to equip students with techniques to visually communicate their results

Teaching mode

In presence

Learning methods

This course will take students from theory to practice in four steps. Students start prepare with reading a short text or watching a video. Then new topics are introduced in a lecture, which is followed by learning-by-doing laptop session. Students finally apply their new knowledge in individual homeworks, which are collected and presented in a student portfolio.

Several programming languages and tools will be used throughout the course: AI copilots, the R and Python languages, a bit of SQL and Linux. Tableau. Students are free to use any language or tool for the assignments. Students are required to bring a laptop to every class. Occasionally, we will work with LINUX. Student can access a server made available by the lecturer or continue to use their server from Programming in Finance and Economics II.

Examination information

40% – Written midterm exam
60% – Portfolio
Students create portfolios of ca. 20 pages from their individual work throughout the semester, including

  • One discussion of data sets and/or methods
  • One discussion of a paper from the literature
  • Data visualizations