This module aims to introduce key concepts in programming using suitable programming languages, such as Python and/or R, focusing on data description, summary and visualisation.

This module introduces students to the emerging field of data science and equips them with the fundamental knowledge of using data to gain insights and support decision-making. The module demonstrates and provides hands-on experience with cleaning, integrating, exploring, transforming and summarising data sets. It teaches students to form questions and hypotheses from data; to utilise and apply a variety of statistical methods to effectively analyse data in a way that answers those questions or hypotheses; and to create suitable visualisations to communicate their analyses. By the end of the module students will in analysing and presenting data using R and RStudio.

The aims of this module are to:

• Understand the theoretical concepts of strategy and strategic management in relation to organisations using data and data analysis to build and implement their strategies
• Explore and evaluate methods used by organisations to determine strategic options
• Develop an understanding of the uses of data and data analysis as drivers for strategic change
• Develop skills in business environmental analysis and strategic planning
• Develop an understanding of the importance of risk management and corporate social responsibility when formulating a new data driven strategy.

The aim of this module is for the students to:

• Develop knowledge and understanding of a variety of storytelling techniques applied within decision making contexts.
• Explore and apply tools for the creation of digital stories to articulate organisational values, practices, and processes.
• Acquire a range of creative and analytical skills to engage in critical discussion and communicate meaningful data-driven stories that effect behavioural change, engage business partners and inform decision-making.

The aims of this module are to:

• Develop students' ability to reason about data
• Engage in the full lifecycle of a database including abstract modelling, concrete realisation, CRUD interaction, and privilege allocation.

Data Mining is a process of extracting information and patterns from the data, and provide insight and understanding to inform decision making. This module introduces key concepts of data mining process and techniques including data pre-processing, feature engineering, clustering and classification.
Students will gain practical experience in the overall data mining process, and in using different techniques to identify patterns and extract information from publicly available data. They will also learn to present their findings effectively in the form of a report.

The aims of this module are to:

• Introduce the facets of research from broad research philosophy to detailed data collection.
• Develop critical analysis skills across a range of different sources.
• Introduce ethical thinking into the development of an appropriate research methodology.

This module aims to introduce students to big data analytics and data visualisation tools and techniques that are widely used for business intelligence and other real-world applications. The module will enable students to solve a variety of complex data centred problems using computer software visualisation tools such as Tableau, Google Data Studio, and Microsoft Power BI.

Students will be equipped with the knowledge and experience needed to communicate complex concepts to a non-technical audience using interactive graphs and charts in the form of dashboards and worksheets to gain data insights.

Students will also learn about the importance of appropriate and responsible data use in government, healthcare and other sectors.

This module aims to provide students with knowledge and experience in modern machine learning techniques suitable for solving various AI challenges using real-world datasets. With an understanding of conventional machine learning and deep learning concepts and techniques, students will gain the ability to pre-prepare data, design machine learning models, and evaluate these models using suitable evaluation measures.

This module introduces the use of statistical models for data summary and prediction using the R or Python programming language and R packages.

The aim of this module is to allow students to demonstrate their knowledge of Data Science skills acquired in previous modules of their MSc Data Science course. Students will be given the opportunity to identify, design and conduct research on a topic relevant to their academic development and skills.

Students can select one of the following two options for conducting the research for their dissertation:

• A desk-based research project on a topic agreed with their supervisor that involves secondary data analysis or the collection and analysis of primary data.
• A project based on research conducted in collaboration with a Loughborough University partner organisation, which may involve the collection and analysis of primary data from this organisation, laboratory experiments and/or fieldwork.
  

Students will achieve a high level of understanding in the subject area and produce a written thesis which will discuss their research in depth and with academic rigour. The outcome of the research will be a written dissertation