Why should I follow this course?
Programming and data analysis are more important today than ever before and as a student as TU Delft, you are bound to deal with it at one point: in a course, for a project, for your graduation project or when you start your career.
But how do you handle your data? With Excel, with Python, with MatLab? How to quickly build a program that exactly solves your problem? The one library you thought could help does not compile and the code form this paper does not cover your edge case. Help!
In this course (new in 2014/2015) we will cover exactly this. The course can be chosen by all TU Delft MsC students and prepares you for all coding and data tasks during and after your MSc.
At the end of this course, you will be able to understand, visualize and process data better.
How does the course look like?
This course consists of a little bit of algorithmics, a dash of data science and a lot of hands-on experience on problems you care about.
The preliminary syllabus per week looks as follows:
- (Week 1.2) Data analysis with Excel: data tables and range names, lookup tables, pivot tables
- (Week 1.3) Data analysis with Python
- (Week 1.4) Modern BI tools: PowerPivot and PowerQuery (guest lecture)
- (Week 1.5) Deadline assignment report
- (Week 1.6) Visualizing data (guest lecture)
- (Week 1.7) Data analysis in practice (guest lecture)
- (Week 1.8) Beyond tables: Graphs and trees
- (Week 1.9) Deadline project reports
For the project, students will work on a problem from your own field with the techniques learned. You can pick your own problem and/or data set, and build any tool or analysis on it you want. The only requirement is that you demonstrate what you learned in the previous lectures. We close the course with presentations on your projects, as well as an exam.
About the professor: Felienne Hermans is assistant professor in the Software Engineering Research Group. Having written a dissertation on spreadsheet problems, she knows about the problems that non-programmers run in to when working with data.
Some useful links