The event is targeted at students (undergrad, MSc, and PhD), postdocs, academics, members of public institutions, ICT practitioners and professionals. The event will be on-campus (Msida) and will take place in the last two weeks of July (from 17 to 28 July 2023).
Data science has been described as the ‘sexiest job of the 21st Century’. It is expected to be one of the most sought-after skills in the next three years. Data science combines computer science, statistics, information science, AI, and statistics, together with their related methods, processes, algorithms, and systems for the purpose of extracting knowledge, patterns, trends, and insights from data. Over the past few years, data science has become increasingly popular and is now widely applied in business, industry, and academia.
The Data Science Platform (DSP) is organising the 2nd UM Data Science Summer School which will be held in the afternoons in the two-week period Monday 17 July to Friday 28 July. The school will be held at the University of Malta at the Msida campus. Sessions will be from 13:00 to 17:30 and consist of a two-hour lecture will be followed by a two-hour practical tutorial. There will be a 30-minute refreshment & networking break in between.
The school is targeted at undergrad and postgrad students in STEM related areas (such as ICT, Engineering and Science) as well as past graduates, practitioners, and professionals who are interested in learning more about this exciting new field. The DSP is committed to helping build a more diverse, and inclusive, data science community and also welcomes postgraduate students and professionals/practitioners in non-ICT areas.
Our lecturing team consists of academics and researchers in computer science, AI, computer and communications engineering, statistics, and information systems.
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. Analyse how to acquire data, both structured and unstructured, process it, store it, and convert it into a format suitable for analysis
4. Apply the basics of statistical inference including probability and probability distributions
5. Explain classification methods and related methods for assessing model fit and cross-validating predictive models
6. Identify, and illustrate, the main concepts of machine learning algorithms and their application
7. Outline the difference between supervised and unsupervised learning approaches
8. Summarize the quantitative methods of text analysis, including mining social media and other online resources
9. Compare, and contrast, the various data interpretation and visualization tools
10. Use Python as the main programming language to perform data science, and
11. Summarize the various privacy and ethics issues in Data Science.
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.
A good knowledge of, and proficiency in, the Python programming language is recommended.
• Lectures
• Practical Sessions and Tutorials
• Work in Groups
Weekdays 13:00 to 17.30 on campus. 2-hour lecture followed by 2-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 sessions will cover the installation, and configuration, of Anaconda and Python.
English
Attendees will not be evaluated and graded. A certificate of attendance will be awarded to attendees who attend all the sessions.
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.
For more information and questions regarding the Summer School please do not hesitate to email
