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Data Analytics for Finance II

People

Gruber P.

Course director

Description

The second part of the course Data Analytics for Finance is available as elective course to all students in the Master of Finance. This half-course takes place in the second half of the semester and focuses entirely on  Data Visualization, one of the most powerful tools for analyzing data and communicating your results

  1. Visualization theory
    – Human perception
    – The grammar of graphics
    – Aesthetics, color,
  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. Visualization workflow and refinement

Objectives

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 to provide the students with the tools and thinking framework to accomplish one of these tasks: data visualization.

Teaching mode

In presence

Learning methods

This course will take students from theory to practice in four steps. Students preparewith 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.

Programming is heavily centred around AI tools, using Github Copilot. Based on these AI tools, several programming languages  will be used throughout the course: the R and Python languages, as well as Tableau (free student version). 

Students are free to use any language or tool for the assignments. Students are required to bring a laptop to every class. Students will also need a ChatGPT Plus licese for the second part of the course. Occasionally, we will work with LINUX. Student can access a server made available by the lecturer.

 

 

Examination information

100% – 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
  • Several data visualizations

Education

Prerequisite