max. 24 participants
Registration: closed

Register now!

TU Berlin Summer & Winter School
+49 30 4472 0230
tubsummerschool(at)tubs.de​​​​​​​

According to the 2022 annual IEEE Spectrum survey of the top programming languages (source: spectrum.ieee.org/top-programming-languages-2022/ieee-spectrums-top-programminglanguages-2022), Python remains the most popular programming language in job listings.

In this course, the fundamentals of Python are covered, with a special focus on the skills necessary for in-depth data analyses and data visualization. These two skills are fundamental in a wide range of disciplines, including but not limited to STEM (Sciences, Technology, Engineering and Mathematics) and Humanities fields of study.

We will cover the following:

1. Data types and compound data structures
2. Conditional statements and loops
3. Python functions
4. Importing, exporting and analyze different types of data using pandas
5. Visualizing data using Matplotlib and Seaborn
6. Bonus: developing dashboards using Metabase

At the end of the two weeks course, students will work and present a final personal data analytics and visualization project.

Learning Goals:

In this course, the fundamentals of Python are covered, with a special focus on the skills necessary for in-depth data analyses and data visualization. These skills are fundamental in a wide range of disciplines, including but not limited to STEM (Sciences, Technology, Engineering and Mathematics) and Humanities fields of study.

The learning goals of the course can be summarised in the following points:

1. install and run Python and all other needed external packages
2. write basic python code, including conditional statements, loops and functions
3. import and export data in python
4. analyze different types of data in python using the pandas package
5. create meaningful visualizations in python to summarize different types of data using Matplotlib and seaboarn packages
6. Effectively and clearly present analytical results, i.e. data storytelling

Main course components

In this course, we will cover the following regarding Python as a tool for data analysis and visualization:

1. Data types and compound data structures
2. Conditional statements and loops
3. Python functions
4. Visualizing data using Matplotlib and Seaborn
5. Importing, exporting and analyze different types of data using pandas
6. Bonus: developing dashboards using Metabase

The main learning tools will be:

1. Python jupyter notebooks and .py files shared in class by the instructor
2. Recommended educational material including online ebooks, blog posts, web-based tutorials and videos. All such materials are free for educational use and will be shared with the students via email and in class
3. Recommended books and online courses (not free and these will be optional)

Syllabus:

A detailed syllabus with information on the schedule will be made available to registered participants.
You may find the syllabus useful when discussing with your home university whether the ECTS credits attainable for this course are accepted by them.

Please note this is a full-time, intensive course and participants will be expected to attend lectures (18 hours of class per week) and complete independent study Monday through Friday. Additional study may also be required on weekends. The activities of the cultural program are also shown in the syllabus.

This course can be useful for all disciplines, but especially STEM (Sciences, Technology, Engineering and Mathematics) and other disciplines that have a data analysis component (e.g.Humanities)

Participants of the TU Berlin Summer University must meet the following requirements: (i) B2 level English, or equivalent and (ii) at least one year of university experience. Furthermore, students should know fundamentals of mathematics and statistics as taught in Bachelor programs. 

Some basic programming knowledge, but not necessarily in python, is recommended for this course.
Students are assumed to have their own personal laptops with a Python installation as the main hardware tool required for this course.

Dina Deifallah is a Data Scientist and a Data Science mentor based in Berlin. After 10+ years working in academia as a researcher and lecturer, she switched to working in the Data Science field in 2017, soon after completing her PhD degree in Communications Engineering. Since then, she worked in the field in multiple capacities: as a Data Science consultant for a consulting firm in London, a Data Scientist for a Berlin-based startup and a Business Intelligence Analyst. She is also a founding member of the Berlin based AI Guild and a coorganizer and regular speaker in the Science to Data Science Meetup, which aims to help academics make a successful career change to data professionals. Currently she works as a Data Science coach in SPICED academy; a 3-months bootcamp run online and in-person based in Berlin. In her down time, she enjoys hiking, yoga, playing with her cats and reading crime novels.

Course fees for Python for Data Analysis and Visualization are as follows:

• Student: 950 Euro
• Working professional/Non-student: 1140 Euro

Please note that students will be required to upload proof of their student status (student card/ enrollment information) during the registration process.

Register now!