BSc (Hons) Data Science with Finance

• Introduction to Data Science
  Learn how to integrate and use data-driven approaches in business to make effective operational decisions and create value from data. Gain insight into statistics and machine learning, including an overview of relevant methods and approaches. 

• Introduction to Academic Work
  The application of good scientific practice is an academic fundamental. Develop the skills required to write strong scientific papers and get an overview of different examination formats and requirements. 

• Introduction to Programming with Python
  Develop a foundational understanding of the Python programming language. Learn about Python’s role in data science-related programming and programming concepts like variables, data types, and statements.  

• Mathematics: Analysis
  Learn the fundamentals of differential and integral calculus and understand their related concepts. This module builds skills to formulate and solve various science and technology-related problems.   

• Collaborative Work
  Build key interdisciplinary competency to develop connections and navigate networking opportunities. Learn about:  

  • collaborative learning and working practices 

  • constructive cooperation with others 

  • creative thinking 

  • empathy 

  • emotional intelligence. 

• Statistics - Probability and Descriptive Statistics
  Learn the essentials of probability and descriptive statistics. Explore random variables, probability density distributions and key statistical concepts. Understand inequalities and limit theorems. 

• Object Oriented and Functional Programming with Python
  Build upon your knowledge of Python programming to understand advanced concepts, functions, and object-oriented programming notions like classes, objects, and design principles.  

• Mathematics: Linear Algebra
  Numerous scientific and engineering challenges can be solved with linear algebra. This module introduces the subject and basic notions like vectors and matrices. Learn about solutions for problems in analytical geometry and develop problem-solving skills. 

• Intercultural and Ethical Decision-Making
  Gain the skills to understand intercultural competencies, diversity, and ethics development. Plan and implement learning processes in these areas by creating a case study. 

• Statistics - Inferential Statistics
  An introduction to statistical analysis and Bayesian techniques. Learn to estimate and optimise parameters. Understand statistical and systematic uncertainties, statistical testing, and decision theory. 

• Introduction to Financial Services Sector
  This module gives an introduction to the financial services industry and explores the main components of the financial system.

• Introduction to Banking Law, Regulation and Ethics
  Get an overview of banking law, regulation and how firms operate according to ethical values.

• Database Modelling and Database Systems
  Develop knowledge of relational database systems, data schemas and modern DB systems (NoSQL) for storing and accessing data. Learn how to store data in relational data models and access stored data with SQL. 

• Project: Build a Data Mart in SQL
  In this module, apply database theoretical knowledge, methods, and approaches to solve a real-world scenario. In this case study, implement your design and architectural choices in a functioning database.

• Business Intelligence
  This module introduces the procedures and models for data provision, information generation and analysis. Build skills in data warehousing and develop techniques to optimise business activities. 

• Project: Business Intelligence
  In this module, develop your business intelligence (BI) knowledge. Use well-known BI techniques to design and prototype BI applications based on specific requirements. 

• Crypto and Blockchain
  This module considers the growth of crypto and blockchain, the evolution of money, from coins to fiat currencies, plastic, and crypto assets. You’ll develop an understanding of the current state of the market and its principles. 

• Fintech
  There has been a huge increase in the number of technological finance solutions in recent years. Uncover the main sectors targeted by Fintech companies. Learn about the current state of the market, technological solutions, and its future direction. 

• Machine Learning - Supervised Learning
  Develop knowledge of large margin classifier concepts and tree-structured models. Gain an understanding of machine learning, with a focus on supervised learning, like labelled data. 

• Machine Learning - Unsupervised Learning and Feature Engineering
  This module provides tools and techniques for unsupervised learning, like machine learning approaches, and feature engineering. Learn relevant methods to find robust and meaningful features. 

• Data Science Software Engineering
  This module gives a detailed overview of data science methods and paradigms to develop enterprise-grade models and bring them into production. This module explores traditional and agile project management techniques and software development paradigms, including pair, mob, and extreme programming. 

• Project: From Model to Production
  Gain hands-on experience with integrating predictive models into enterprise-grade applications or services. In this project-based module, consider aspects like data storage, processing, and service availability.  

• Agile Project Management
  Gain a practical introduction to agile project management and learn to distinguish it from plan-driven approach. Learn the values, activities, and roles of typical agile procedures and practice with an example project. 

• Big Data Technologies
  This module introduces the four ‘Vs’ of data – and data sources and types. Learn about the most common data storage formats and the challenges large amounts of data pose for underlying infrastructure. 

• Foreign Exchange Exposure and Management
  Examine the challenges multinational enterprises (MNEs) face. Learn about international corporate finance within foreign exchange markets and the management of foreign exchange exposures. 

• International Investment Appraisal
  In this module, you’ll consider international corporate finance within the context of managing multinational enterprises. Learn about controlling international operations, financing, transfer pricing and investments. 

• Data Quality and Data Wrangling
  Explore techniques for acquiring, formatting, and tidying data to make it suitable for subsequent analysis to unlock business value. Learn about data quality and key methods for data quality management.  

• Explorative Data Analysis and Visualisation
  This module introduces useful approaches, tools, and techniques to explore data sets. Examine detailed visualisation principles and techniques to develop skills and present analytical outcomes.  

• Cloud Computing
  An introduction to cloud computing, its enabling technologies, and analytics capabilities. Learn about cutting-edge advances like serverless computing, storage, and popular cloud offerings. 

• Seminar: Ethical Considerations in Data Science
  This module explores ethical issues in relation to data science methodologies and techniques which are an everyday part of contemporary life. Develop skills, knowledge and understand how to practice ethical data science. 

• Time Series Analysis
  This module examines time series analysis and data eg, the number of products sold at a retail outlet. Explore ARMA-based models (Box-Jenkins approach) and the alternative Holt-Winters formulas used for time-series analysis and forecasting.  

• Neural Nets and Deep Learning
  Learn how feed-forward networks are set up and trained, and how to avoid overtraining. This module also covers common network architectures and highlights design choices and data collection impacts. 

• Introduction to Data Protection and Cyber Security
  This module covers important IT security concepts. Learn terminology, typical application fields, IT security application areas, and standard procedures and techniques. 

• Model Engineering
  This module focuses on best practices data scientists use to build high-quality enterprise-grade models.  Explore techniques for model validation and model combination and machine learning approaches.  

• Undergraduate dissertation (with finance focus)
  Apply the subject-specific and methodological competencies learned throughout your course to present an academic dissertation with a finance focus. You’ll also learn how to tackle a practical-empirical or theoretical-scientific problem.  

Once enrolled, you will be expected to meet LIBF's policies and standards.

Achieving the second degree, also known as the dual award, from IU is subject to meeting IU's entry requirements and academic regulations.

Each module is led by either LIBF or IU. The academic regulations of the lead institution will apply.

The course handbook provides you with all the important information on your study programme.

Course handbook

Learn more about the objectives, learning outcomes and content of the study programme.

Programme specifications