MSc Data Science with Artificial Intelligence

Module 1: Applied Statistics and Data Visualisation (30 credits)

• Descriptive and inferential statistics and probability distributions
• Hypothesis testing, ANOVA, regression and time series analysis
• Solving statistical problems using R
• Theory and practice of data visualisation
• Dashboards, reports and data stories in Power BI

Module 2: Machine Learning and Data Mining (30 credits)

• The full data mining lifecycle, from data preprocessing to exploratory analysis
• Supervised and unsupervised algorithms: association rules, clustering and classification
• Neural networks, ensemble methods and text mining
• Practical activities in Python to extract and interpret insights from data
• Ethical and societal implications of machine learning practice

Module 3: Advanced Databases (30 credits)

• Relational database design and normalisation
• Advanced SQL querying and data types
• Indexing, recovery, security and transaction management
• Distributed databases, parallel systems and large-scale analytics
• How current research shapes the practical deployment of big data systems

Module 4: Big Data Tools and Techniques (30 credits)

• Defining characteristics, challenges and opportunities of Big Data
• Cloud computing, distributed processing frameworks and NoSQL databases
• Ingesting, processing, analysing and storing massive datasets
• Hands-on Apache Spark, PySpark and MongoDB on real-world text, image and IoT sensor data
• Machine learning and recommender systems at scale

Module 5: Project for MSc (Dissertation) (60 credits)

• Independent, research-informed project on a substantive problem in your field
• Robust research proposal and critical review of current practice and the state of the art
• Ethical considerations and appropriate methodologies and technologies
• A 15,000-word dissertation with results, reflections and recommendations
• Supported throughout by a dedicated supervisor