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Level
Level: IntroductorySoftware
Member rate £572.50
Non-Member rate £1,145.00
Early bird discount available until 14 December 2026.
Save £45.00 Loyalty discount applied automatically*
Save 5% on each additional course booked
* If you attended a qualifying previous Methods School in 2026, you qualify for £45.00 off your course fee.
Date: Thursday 18, Friday 19, Wednesday 24, Thursday 25 and Friday 26 February 2027
Time: 09:30 – 12:30 CET
This interactive course uses advanced pedagogical tools and is designed for master's-level students and above. It runs as three-hour live sessions on Zoom over five days, with course materials and pre-course readings available on our Learning Management System at least two weeks before the start. Office hours are also available for one-to-one consultations on your own project.
The course is limited to 18 participants, ensuring that the teaching team can address the individual needs of each participant.
By the end of the course, students will be able to:
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Giovanna Maria Dimitri is a tenure-track Assistant Professor at the University of Milan (Statale), Italy, and Guest Lecturer in Data Science at the University of Cambridge. She obtained her PhD from the Department of Computer Science at the University of Cambridge, supervised by Prof. Pietro Liò, with the dissertation ‘Multilayer network methodologies for brain data analysis and modeling’.
She has published more than 70 papers in top peer-reviewed conferences and journals on the development of novel deep learning applications, and has extensive experience as an associate editor and reviewer for top journals in computer science. She has also organised international conferences, most recently WIVACE 2026 at the University of Cambridge in September 2026 and ANNPR 2026 at the University of Milan in October 2026.
She has wide teaching experience, having taught courses in deep learning and machine learning, which she currently teaches at the University of Cambridge and the University of Milan.
The course is a 15-hour introductory programme for PhD students in social and political sciences, delivered over five three-hour sessions on 18, 19, 24, 25, and 26 February. It combines theoretical introductions with hands-on exercises to develop practical programming, data analysis, and AI/ML skills for social science research.
Programming fundamentals, data structures, and basic data manipulation using pandas.
Introduction to R, data manipulation, and visualization using tidyverse and ggplot2.
Exploratory data analysis, descriptive statistics, and visualisation using Python and R, applied to social and political datasets.
Core AI/ML concepts, supervised learning, model training and evaluation, and practical applications using scikit-learn.
Formulating research questions, identifying appropriate data and methods, designing AI/ML workflows, and developing a mini research proposal.
For the additional ECTS credit, you will complete a data science and machine learning project. You are expected to identify a social science research question on a topic of your choice (we suggest using a public repository such as Kaggle) and implement a data science pipeline.
You are expected to submit:
It would be very useful for you to be familiar with (or have some working experience with):
The course will still start from the basics of programming in Python, but some prior knowledge will help you get the most out of the course.
You don't need to install anything before the course. The first session includes a guided setup of the course tools.
To code, we will use the interactive environment of Google Colab notebooks. It is a free tool; the only requirement is a Gmail account to use for the exercises, so if you don't have one already, please create one before the course.
Pre-course readings:
The course includes three hours of live teaching each day for five days, alongside readings and other learning activities.
Once you have registered and paid, you will receive access to our Learning Management System (LMS) approximately two weeks before the course start date. Course materials, including pre-course readings, will be available here. Allow approximately 20 hours per week before the course to review the materials and complete readings and any pre-course tasks.
During the course week, you should allow approximately two to three hours per day for preparation and assignments, in addition to the live teaching sessions.
The course offers the opportunity to earn three ECTS credits. To earn a fourth credit, you will need to complete a post-course assignment involving approximately 25 hours of work.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach prerequisite items. If in doubt, please contact us before registering.
