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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”
Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Monday 24 – Friday 28 July 2023
Minimum 2 hours of live teaching per day
09:30 – 11:45 CEST
This course offers an interactive online learning environment using advanced pedagogical tools, and is specifically designed for advanced students, researchers, and professional analysts. The course is limited to a maximum of 16 participants, ensuring that the teaching team can address the unique 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 for social scientists who want to learn how to conduct data collection and complex data analysis with Python.
The course will be highly interactive, with hands-on exercises and practical tips to help you start your journey in the world of Python. By the end of the course, you will have gained a strong foundation in Python programming and be able to apply your new skills to your own research projects.
To reinforce your learning, you will have after-class assignments from Monday to Thursday, where you will apply what you learned in class to real-world problems. These assignments will give you the opportunity to practice and improve your programming skills and receive feedback from the course instructors.
4 credits - Engage fully in class activities and complete a post-class assignment
Orsolya Vasarhelyi is an assistant professor at the Center for Collective Learning, and at the Institute of Data Analytics and Information Systems at Corvinus University in Budapest, Hungary.
Her research focuses on the gender differences in career development in project-based environments.
She is a Python enthusiast!
Learn how to operate Jupyter Notebooks, through Google Collab. You will cover different data types in Python, loops, and conditional statements.
Homework: Set of programming games.
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 cleaning is one of the most challenging parts of a data scientist's work. 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.
A picture is worth a thousand words. Besides introducing Python's most popular data analysis toolkits (Pandas, Matplotlib, Seaborn), you will 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.
How to conduct statistical modelling in Python. The focus will be on the two most popular libraries:
You'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 will help you prepare for classes. The course is structured into five live Zoom sessions, each lasting at least 2 hours. The live sessions will focus on introducing new materials, followed by coding work, either alone or in groups, with support from the Instructor and Teaching Assistant.
Homework assignments on Days 1–4 will deepen your knowledge of each topic. The Instructor and TA will check your homework, and you can book one-to-one meetings to discuss.
Basic statistical knowledge is required. No programming experience needed.
There are around three hours of preparation for Day 1. This includes: