Python for Data Analysis and Visualization

About

According to the 2022 annual IEEE Spectrum survey of the top programming languages (source: https://spectrum.ieee.org/top-programming-languages-2022/ieee-spectrums-top-programming-languages-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.

In this course, 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 interactive plots with Plotly

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

Course leader

Dina Deifallah

Target group

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)

Course aim

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

Fee info