Data Science and Machine Learning: The Fundamentals

About

The most important aspect of computer science is problem solving, an essential skill for life.
Data Science is concerned with how to gain knowledge from the vast volumes of data generated daily in modern life, from social networks to scientific research and finance, and proposes sophisticated computing techniques for processing this deluge of information. In parallel, Machine Learning is concerned with the development of analytical models and algorithms to learn from data and make accurate predictions.

This course addresses fundamental aspects of Data Science and Machine Learning, e.g., analytical models to represent and understand the data, efficient algorithms to manipulate and extract relevant knowledge, and corresponding models to understand their overall performance and limitations.

In particular, students study the design, development and analysis of software and hardware used to solve problems in a variety of business, scientific and social contexts. During this course, students will study techniques for how to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making. Students would be expected to have some basic knowledge of linear algebra and calculus.

Course leader

Dr Florin Ciucu Alex Dixon

Target group

This course is open to students studying any discipline at University level provided they have basic knowledge of linear algebra and calculus. We welcome individuals from all backgrounds, including students who are currently studying another subject but who want to broaden their knowledge in another discipline. Students should also meet our standard entry requirements and must be aged 18 or over by the time the Summer School commences and have a good understanding of the English language.

Course aim

To understand the foundational skills in data analytics, including preparing and working with data; abstracting and modelling an analytic question; and using tools from statistics, learning and mining to address the question.

Fee info