About

4th University of Malta Data Science Summer School

The event is targeted at students (undergrad, MSc, and PhD), postdocs, academics, members of public institutions, ICT practitioners and professionals. The event will be held at the Msida campus (BM-402) and will take place during the last two weeks of July (from 14 to 25 July 2025).

This year the summer school is split in two sections (candidates may choose to register for both sections or the second part only)

Part 1: Introduction to Python for Data Science (3 days) - This first part is meant for individuals with little programming or python experience. It will delve into the basics of programming methodologies for Data Science as required in the remaining of the course.

Part 2: Data Science in Python (7 days) - The second part of the school focuses on the use of Python for data science covering the basics of statistical approaches, regression, classification, clustering, machine learning, neural networks and deep learning among others.

Learning and Skills Outcomes

Upon successful completion of the school attendees should be able to:

1. Outline the challenge of working with big data using statistical methods
2.  Integrate the insights from data analytics into knowledge generation and decision-making
3.  Use Python as the main programming language to perform data science.
4. Analyse how to acquire data, both structured and unstructured, process it, store it, and convert it into a format suitable for analysis.
5. Apply the basics of statistical inference including probability and probability distributions.
6.  Compare, and contrast, the various data interpretation and visualization tools.
7. Explain classification methods and related methods for assessing model fit and cross-validating predictive models.
8. Identify, and illustrate, the main concepts of machine learning algorithms and their application
9. Outline the difference between supervised and unsupervised learning approaches.
10. Apply statistical models and machine learning approaches to time series data.
11. Introduce deep learning through artificial neural networks with many layers.
    
    

Admission Requirements

UM Data Science Summer School applicants should have a good background, and proficiency, in computing and programming, but need not necessarily be enrolled or have completed an ICT degree. Professional experience may also be sufficient to make up for the lack of a first degree. 

For applicants with limited programming or Python knowledge, it is recommended to follow both Part 1 and Part 2 of the school. Note that, good knowledge of, and proficiency in, the Python programming language is expected for applicants wishing to follow Part 2 only.

Teaching methods

• Lectures
• Practical Sessions and Tutorials
• Work in Groups
  
  

Schedule

Weekdays 13:00 to 17:30 on campus. A two-hour lecture followed by a two-hour practical with a 30-minute networking and tea & coffee break in between.

Attendees are expected to bring their own laptop. The first and second days will cover the installation and configuration of Anaconda and Python.

Language of Instruction

English

Assessment & Certification

Attendees will not be evaluated and graded. A certificate of attendance will be awarded to attendees who attend all the sessions.

Registration

Attendees can register and pay for the summer school online. Payment is required at the time of registration. Seats are allocated on a first-come first-served basis.

For group registrations please contact us as listed below.

Contact us

For more information and questions regarding the Summer School please do not hesitate to email