COMP1961
Essentials of Data Science

This module aims to develop advanced mathematical and statistical skills essential for data science, enabling students to adopt a problem-solving approach to formulate, analyse, and interpret quantitative challenges.

It provides a rigorous understanding of probability theory, linear algebra, and calculus for addressing applied optimisation problems and industry-oriented data science use cases.

Students will also gain practical skills in programming techniques for handling large datasets and implementing core data science methods such as regression, time series analysis, and algorithmic design.

By the end of the module, students will be able to apply these concepts and techniques to develop robust, efficient models and algorithms for data-driven decision-making.

1. Demonstrate the understanding of core mathematical and statistical concepts and justify methodological choices in the analytical contexts
2. Formulate and employ the key principles and techniques of calculus and linear algebra in optimisation and other data science use cases.
3. Evaluate and apply appropriate statistical methods and probability distributions to extract meaningful insights, quantify uncertainty, and build predictive models to inform decision making.
4. Critically evaluate and justify with evidence-based reasoning the efficiency and effectiveness of programming solutions for complex data science applications.
5. Design and implement solutions to computational problems by selecting, applying, and evaluating appropriate algorithms and industry-standard tools, justifying adaptability in dynamic environments.
6. Collaborate effectively with peers to plan, develop, and critically evaluate approaches to analytical challenges, demonstrating inclusive learning practices, clear communication, and adaptability.

Probability – joint, union, conditional, Bayes’ Theorem, probability distributions; Linear algebra – vectors, matrices, operations and transformations, eigenvalues and eigenvectors; Calculus – derivatives, integrals, optimisation, multivariable calculus; Dealing with large datasets – concat, merge, join, aggregate; Logistic regression, time series analysis, graph theory; Algorithms – recursion, search, sort.

Portfolio: 50% weighting, 50% pass mark.
Learning Outcomes: 1, 2, 3 & 6
Word Length: 2750
Outline Details: Solutions to practical problems using mathematics, statistics techniques with programming/software tools. Students will submit portfolio tasks bi-weekly.

Project: 50% weighting, 50% pass mark.
Learning Outcomes: 4 & 5
Word Length: 2750
Outline Details: Coding-based practical work requiring advanced computational task with written report.