Python: Data Analysis Essentials

In recent years Python has been the fastest growing programming language. As a universal programming language Python is used in a huge variety of application domains. Particularly, in scientific and numeric computing Python is becoming one of the most utilized languages.

This course will introduce the essential Python methods for preparing, cleaning, transforming and aggregating data as well as its visualization. For this purpose, we will start with a short wrap-up of data structures in Python before expanding our skills with the built-in functions for data manipulation and the interaction with files. We move on to the add-on libraries pandas and NumPy which are designed specifically for data analysis. Finally, we will introduce basic information visualization techniques.

General information

Duration 12 hours
  • Writing and running Python using Jupyter Notebooks
  • Data import and processing
  • Creating NumPy arrays
  • Indexing and slicing in NumPy
  • Aggregating data in Pandas
  • Basic information visualization
This course is designed for participants who have already written their own small programs. You should have no trouble writing simple if blocks, loops and functions. Furthermore, we recommend having attended the courses “APPB - Python Basics” and “APPI – Intermediate Python” before joining this course.

For your self-assessment, the enclosed Python source code on this page (ZIP file) may help. The code should be understood after 10 minutes at the latest.
Students and employees of the University of Zurich.
Participants of this course will be able to use Python for basic techniques of data analysis. You will learn the essential Python methods for preparing, cleaning, transforming and aggregating data and how to visualize it for your analysis


Code Referents Dates Available seats Place
HS24-APPD1 Bubanja Kaju 11.01.2025 - 18.01.2025 (09:00 - 16:00 o'clock)
Universität Zürich Zentrum Course registration begins on 1 February for the spring semester and on 1 September for the autumn semester.