Microcredential ekomex Introduction to Python

A crash course on learning Python, from variables to basic data analysis.

What Is This Course About?
This 2-day online course is designed to provide an introductory overview of using the Python programmatic language for basic programming tasks, as well as for data analysis and visualization of quantitative (social science) data. Lecture sessions present the theoretical and technical background of data analysis. We have additional practical sessions that allow participants to directly apply acquired knowledge with code in the Python programming language. The course will enable participants to learn basic programming concepts such as variables and data structures. It shows them how they can use Python to load, preprocess, analyse, and visualise data.

Learning Goals

• Become familiarized with introductory programming concepts and the way they are used in Python
• Have general knowledge of the Python programming language
• Code simple data analysis scripts using the Python Data Science Stack and Jupyter Notebooks
• Do data exploration and preprocessing with Pandas
• Create visualizations with Python for quantitative data analysis

Assignments for the Course
We will have several small in-class exercises to be solved individually or in groups of two lasting 15-20 mins each on all days of the course

Schedule

• Lecture + Hands-on In-class Exercises: Online 10 AM – 12:00 PM, 1:30 PM – 2:30 PM
• Supervised learning: 2:45-3:45 PM

Recommended Readings for the Course

• https://www.python.org/about/gettingstarted/
• A Byte of Python, by Swaroop C.H. An introductory text for beginners and experienced programmers looking to learn Python.
• Python Cheat Sheet by Mosh Hamedani https://programmingwithmosh.com/wp-content/uploads/2019/02/Python-Cheat-Sheet.pdf

Who Is Your Instructor?
Indira Sen is a Postdoc at the Political Science department at the University of Konstanz. Her research is about understanding and characterizing the measurement quality of social science constructs like political attitudes and abusive content from digital traces. Her work with NLP and measurement theory. You can reach her at @indiiigosky or indiiigo.github.io/