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

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. Visualizations in R
   ggPlot and shiny

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.

In presenza

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.

The R programming language (together with a bit of SQL and Linux) will be used for most part of this course. Students are free to use other languages or tools (Python, Tableau, …) for the assignments. Students are required to bring a laptop with R and R Studio installed 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