Fwd: [DSP Events] 3rd UM Data Science Summer School - Registrations Open
JP Ebejer
From: Kenneth Scerri <kenneth...@um.edu.mt>
Date: Wed, 5 Jun 2024 at 11:44
Subject: [DSP Events] 3rd UM Data Science Summer School - Registrations Open
To: DSP Events Mailing List <event...@um.edu.mt>
Dear all,
The University of Malta is once again organising the Data Science Summer School, which will be held from the 15th to 26th July 2024 at the Msida campus. The school is targeted at undergrad and postgrad students as well as past graduates, practitioners, and professionals in STEM related areas and other domains who are interested in learning more about this exciting new field and possibly utilising data science techniques in their work.
Sessions will take place in the afternoons from 13:00 to 17:30 and consist of a two-hour lecture which will be followed by a two-hour practical tutorial. There will be a 30-minute refreshment & networking break in between.
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 remainder 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.
Upon successful completion of the school attendees should be able to:
Outline the challenge of working with big data using statistical methods
Integrate the insights from data analytics into knowledge generation and decision-making
Analyse how to acquire data, both structured and unstructured, process it, store it, and convert it into a format suitable for analysis
Apply the basics of statistical inference including probability and probability distributions
Explain classification methods and related methods for assessing model fit and cross-validating predictive models
Identify, and illustrate, the main concepts of machine learning algorithms and their application
Outline the difference between supervised and unsupervised learning approaches
Summarise the quantitative methods of text analysis, including mining social media and other online resources
Compare, and contrast, the various data interpretation and visualisation tools
Use Python as the main programming language to perform data science, and
Summarise the various privacy and ethics issues in Data Science.
To register and for more information about the event, please visit: https://www.um.edu.mt/events/datascience2024/ or contact me on kenneth...@um.edu.mt .
Regards,
Kenneth
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