BSc (Hons) in Data Science - London Metropolitan University (UK)

Course overview

This undergraduate programme leads to a BSc (Hons) award from London Metropolitan University and provides a comprehensive introduction to data programming, statistical modelling, business intelligence, machine learning and data visualisation. The course balances theory with practical, tool-based practice.

What you will learn

• Apply mathematics and statistical practice to analyse real datasets and support decision-making.
• Develop coding skills and software practices to build scalable data products across the product life cycle.
• Design and implement data pipelines, visualisations and applied machine learning solutions for domain problems.
• Recognise business opportunities and exploit data science innovation to solve practical challenges.

Core digital tools and platforms

• Hands-on use of Python - Jupyter, RStudio and statistical packages for analysis and modelling.
• Experience with big-data technologies such as Spark, Kafka and Hadoop for scalable processing.
• Familiarity with database and tooling ecosystems - Oracle, SQL Server, Linux and Apache Airflow.
• Data visualisation and business-intelligence tools including Tableau and D3 technology.

Teaching and assessment

Teaching combines lectures, tutorials and workshops with formative and summative assessment. Assessment includes project artefacts, dataset analyses, program code and written reports to show applied competence and academic rigour.

Course modules - Year 1

• Data Analysis - descriptive and inferential statistics using statistical software.
• Financial Mathematics - mathematical techniques for finance and Excel-based computation.
• Fundamentals of Computing - principles of information processing and initial programming skills.
• Introduction to Information Systems - role of information management and basic database and web foundations.
• Logic and Mathematical Techniques - foundational mathematics for computing and data science.
• Programming - core programming constructs and software development fundamentals.

Course modules - Year 2

• Cloud Computing and the Internet of Things - cloud services and distributed data sources.
• Data Structures and Specialist Programming - efficient data representation and advanced coding practice.
• Databases - design, implementation and query of relational data stores.
• Professional and Ethical Issues - legal, ethical and professional responsibilities in computing.
• Smart Data Discovery - exploratory and automated techniques for insight discovery.
• Statistical Methods and Modelling Markets - applied statistics and modelling for market contexts.

Course modules - Year 3

• Artificial Intelligence and Machine Learning - supervised and unsupervised learning methods and applications.
• Career Development Learning - employability skills and professional preparation for the data sector.
• Data and Web Development - integrating data services with web technologies and APIs.
• Project / Project Analysis and Practice - independent project demonstrating applied data science capability.
• Big Data and Visualisation - large-scale data processing and advanced visual presentation methods.
• Cryptography and Number Theory - fundamental security principles relevant to data handling.

Where this degree can take you

• Prepared for roles such as Junior Data Scientist, Associate Data Analyst or Data Science Operational Officer.
• Applicable industries include technology, finance, media, public sector and consultancy firms that rely on data-driven decisions.
• Good preparation for further study or research in data science and related fields.

Send Inquiry

Your inquiry will be sent directly to the institute. A copy will also be emailed to you for your records.