Data Analytics for Finance II
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
- Visualization theory
Perception and aesthetics, color, the grammar of graphics
- Static visualizations
Bar charts, scatter plots, pie charts, line carts, Sankey diagrams, Parallel coordinate plots,
- Statistical visualizations
Box and violin plots, qq-plot, histograms, tree maps, forest plot, autocorellograms, Lorenz curves, Venn diagrams,
- Data maps
Dot distribution maps, heat maps, choropleths and alternative maps: cartograms, grid and hexagon maps, statebins
- Interactive visualizations
Basic web technology, user interaction, R shiny
- AI tools for data analysis and
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.
This course will take students from theory to practice in four steps. Students 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.
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
- Data visualizations