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5th University of Malta Data Science Summer School

The Data Science Summer School is aimed at undergraduate, MSc, and PhD students, as well as postdoctoral researchers, academics, members of public institutions, and 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 20 - 31 July 2026), excluding the weekend.

This year, the summer school is aimed at individuals with little or no experience in computer programming and data science. The sessions cover the fundamentals of programming methodologies for data science in Python, as well as, basic statistical and mathematical techniques, predictive modelling, and a capstone project in which participants apply the knowledge acquired in earlier sessions. The programme therefore provides a solid introduction and foundation in data science, enabling participants to progress to more advanced study.

Learning and Skills Outcomes

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

1. Use Python as the primary programming language for data science tasks.
2. Apply variables and basic data types to store, manipulate, and represent information in Python programs.
3. Design programs using control structures and data structures to implement decision-making, repetition, and organized data handling.
4. Develop modular solutions using functions and classes to structure code and promote reuse.
5. Analyse methods for acquiring, processing, storing, and transforming data into formats suitable for analysis.
6. Apply the fundamentals of statistical inference, including probability and probability distributions.
7. Compare and contrast various data interpretation and visualization tools.
8. Outline the challenges associated with working with data using statistical methods.
9. Integrate insights from data analytics into knowledge generation and decision-making.
10. Explain regression and classification tasks, as well as methods for assessing model fit and cross-validating predictive models.
11. Identify and illustrate key concepts of machine learning algorithms and their applications.
12. Select and apply basic data science methods to solve a given problem.

Admission Requirements

UM Data Science Summer School applicants are expected to be in possession of or are currently enrolled in a first degree. Professional experience may also be sufficient to make up for the lack of a first degree.

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. If necessary, students will be assisted in installing and configuring 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