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Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Monday 18 ꟷ Friday 22 July 2022
2 hours of live teaching per day
10:30 ꟷ 12:45 CEST
This course provides a highly interactive online teaching and learning environment, using state-of-the-art online pedagogical tools. It is designed for a demanding audience and capped at a maximum of 16 participants so that the teaching team (the Instructor plus one highly qualified Teaching Assistant) can cater to the needs of each individual.
Python is one of the most popular programming languages of data science, used in natural language processing, machine learning, and artificial intelligence. This five-day Python programming course is designed for social scientists who would like to learn how to conduct data collection and complex data analysis with Python.
The course focuses on hands-on exercises and practical tips to help you start your journey in the world of Python. This course covers a wide variety of topics in a short period of time, so you will complete after-class (homework) assignments from Monday to Thursday.
3 credits Engage fully with class activities
4 credits Complete a complex post-class assignment
Rebeka O. Szabó is a sociologist-network scientist.
She is a junior researcher at the Laboratory for Networks, Innovation, and Technology at Corvinus University of Budapest.
Rebeka earned her PhD in network science at Central European University and her Master's degree in sociology at Universiteit Van Amsterdam. She was a visiting research fellow at Kellogg School of Management and The Northwestern Institute on Complex Systems of Northwestern University in 2020.
Her main scientific interests are teams, group dynamics, social networks, applied social psychology, and organisations intertwined with topics of cooperation and social inequality.
There is around three hours' preparatory work for Day 1. This includes:
Introduction to Python and Jupyter Notebook
Learn how to operate Jupyter Notebooks, through Google Colab. We also cover different data types in Python, loops, and conditional statements.
Homework: Set of programming games
Data collection I – Web scraping
Python is a popular language to extract data from the internet. Learn how to extract data from semi-structured websites and save the results into .xlsx and .csv files.
Homework: Scraper for a pre-defined website
Data analysis I – Intro to data cleaning, analysis and nested data structures
Data cleaning is one of the most challenging parts of the work of a data scientist. Learn how to extract relevant information from messy data and create data structures that are efficient to use.
Homework: Write functions – combine loops and conditions
Data analysis II – Data analysis with Pandas and data visualisation
A picture is worth a thousand words. Besides introducing Python's most popular data analysis toolkits (Pandas, Matpotlib, Seaborn), you will also learn how to convey the findings of your analysis effectively by creating appealing and scientifically valid visualisations. You will work in groups to analyse a pre-defined database, then present your findings to the class.
Homework: Exploratory data analysis with visualisations on a pre-defined data set
Data analysis II – Statistical modelling
How to conduct statistical modelling in Python. We will focus on the two most popular libraries:
We'll also learn about PCA and freely available data sets you might choose for your post-class assignment.
Introductory pre-recorded videos and required readings (mainly documentation of the libraries) will help you prepare for classes. The first half of each class will focus on introducing new materials, then you will code, either alone or in groups, with live support from the Instructor and Teaching Assistant.
Homework assignments on Days 1–4 will deepen your knowledge of each topic. Your homework will be checked by the Instructor and TA, and you can book one-to-one meetings with them to discuss it.
Basic statistical knowledge, no programming experience needed.
There are around three hours of preparation for Day 1. This includes:
