Programming
Contents
Data Science is an emerging interdisciplinary field with the aim to extract information from prevalently unstructured data. A basic skill for every data scientist is programming.
This course sets out to introduce Python, a modern object-oriented programming language, to prospective data scientists. The class covers basic programming skills and provides an introduction to computer science. In the second part, Python libraries and tools are presented that are handy in the daily life of a data scientist, such as Jupyter Notebook, NumPy, Pandas, Matplotlib, Scikit-Learn, and Pyspark.
No prior knowledge of computer science is required, but basic training in mathematics is assumed.
This class will be taught online, through video lectures. Tutorials are offered in form of video conferences.
Literature
- VanderPlas, Jake. (2016). Python data science handbook. Beijing; Boston; Farnham; Sebastopol; Tokyo: O’Reilly: https://jakevdp.github.io/PythonDataScienceHandbook/
- Toomey, Dan. (2017). Jupyter for data science. Birmingham; Mumbai: Packt: https://katalogplus.ub.uni-bielefeld.de/title/2539316
- Ana Bell, Eric Grimson, John Guttag (2016) MIT 6.0001 Introduction to Computer Science and Programming in Python: http://ocw.mit.edu/6-0001F16
- Eric Grimson, John Guttag, Ana Bell (2016) MIT 6.0002 Introduction to Computational Thinking and Data Science: http://ocw.mit.edu/6-0002F16
Time table
| Date | Topic | Exercises/Addenda |
| 22.04.2020 | Introduction, logistics (slides) | Ex01  Tutorial rescheduled to 22.04 14h c.t. |
| 29.04.2020 | Programming & Python Basics II (slides) | Ex02 Due to holiday deadline is delayed to Saturday |
| 06.05.2020 | Programming & Python Basics III (slides) | Ex03, course material |
| 13.05.2020 | Data management & Analysis (slides) | Ex04 (PDF), Ex04 (Jupyter NB), course material |
| 20.05.2020 | Numerical Data Analysis & Visualization (slides) | Ex05 (PDF), Ex05 (Jupyter NB), course material |
| 27.05.2020 | Tabular Data Analysis (slides) | Ex06 (PDF), Ex06 (Jupyter NB), course material |
| (break) | ||
| 17.06.2020 | Applied Machine Learning (slides) | Ex07 (PDF), Ex07 (Jupyter NB), course material |
| 24.06.2020 | Databases & Distributed Computing (slides) | Ex08 (PDF), Ex08 (Jupyter NB), course material |
| 01.07.2020 | Advanced Programming (slides) | Ex09 (PDF), Ex09 (Jupyter NB), course material |
| 08.07.2020 | Algorithms & Complexity | – |
| 15.07.2020 | Exam | |
Examination dates
1st exam: Written exam on Wednesday, July 15, 2020 14:00-16:30 in H1
2nd exam: Oral exam, please schedule date via email