Data Science MSci/BSc (Hons)

In this module you’ll gain a comprehensive understanding of modern artificial intelligence concepts and applications. You’ll explore the differing definitions of just what ‘artificial intelligence’ means, and the legal and ethical issues which arise surrounding decision-making computer systems. Ultimately, you’ll build a portfolio of solutions that address artificial intelligence challenges, as you navigate areas such as knowledge representation, reasoning, and how human factors impact the field of AI. 

Compulsory

This module continues linear algebra and differential equations from the first year with the overlap between the areas emphasised – in both cases, the general goal will be to explore ways in which one can find a ‘suitable basis’ for a given problem. This could include orthonormal bases, bases of eigenvectors or bases of generalised eigenvectors. It will conclude with Singular Value Decomposition which will bring together bits of linear algebra seen over the first two years. The module will also cover second order ODEs (linear, but with non-constant coefficients), systems of first order ODEs, series solutions and self-adjoint operators. 

Compulsory

Building on your programming and algorithms studies from first year, this module expands your insights into advanced programming techniques and complex data structures. You’ll learn what terms such as ‘graph’ and ‘tree’ mean in computing, and how to use them in your own software development. You’ll become familiar with strategies to address the computational complexity of the problems you’re trying to solve, empowering you to write more sophisticated, and more efficient, software solutions. 

Compulsory

Picking up where Working with Data left off, the Data Science module equips you with the skills and tools you need to explore the world of Big Data. Using state-of-the-art software, you’ll explore concepts such as predictive modelling, data wrangling, sampling, and analysis. You’ll also explore the complex subject of data visualisation, and how you can use visualisation techniques to make the results of your data analysis understandable to every audience. 

Compulsory

This module will introduce two of the most commonly used statistical techniques, multiple regression and analysis of variance (ANOVA). The statistical inference concepts of hypothesis testing, and estimation will be extended within the statistical modelling framework. Statistical computing environments and packages will be used throughout. The methods taught are used extensively in industry, commerce, government, research and development. This module is particularly relevant for those intending to go on placement year, for graduate jobs, and for final year statistics projects. The statistics modules will build on the concepts introduced in this module. 

Compulsory

You will gain experience in teamwork, cooperation, and project planning. You will also get a better understanding of topics covered earlier in the course which feeds into the creation of a real-world data science project. Throughout this module, you will develop tailored modern data science tools using “Raspberry pi” to explore and illustrate the fundamental concepts in data science, and practical skills in creating datasets for the data science approaches considered for solving the adopted real-world problems.  

Compulsory

This module² provides you with an opportunity to reflect upon and gain experience for an approved placement undertaken during your programme. A placement should usually be at least 26 weeks or equivalent; however, each placement will be considered on its own merits, having regard to the ability to achieve the learning outcomes. 

Optional

This module² provides you with an opportunity to reflect upon and gain experience for an approved international study/work placement undertaken during your programme. A work/study placement should usually be at least 26 weeks or equivalent; however, each placement will be considered on its own merits, having regard to the ability to achieve the learning outcomes. 

Optional

This module develops the theory and practice of data visualisation for exploration and presentation.  Principles of visual design, human perception and cognition, data storytelling, and data ethics will be applied through modern software tools to create effective visualisations of a variety of types of data. 

Compulsory

This module introduces you to more advanced statistical methods/models required to analysis and model more complex data. These include Generalised Linear Models, principal components analysis, cluster analysis and graphical Bayesian networks. The module will be set within a range of real-world contexts.  

Compulsory

Building on your existing knowledge of Artificial Intelligence, this module dives into the broad field of machine learning, one of the core building blocks of many AI systems and methods. You’ll learn the difference between supervised and unsupervised machine learning, what an artificial neural network is and when best to deploy one to solve a problem, and how to analyse the effectiveness of a wealth of machine learning algorithms when applied to actual data. 

Compulsory

Project Discovery is a module that enables you to identify and refine a topic relevant to your degree title. You will be able to choose between an individual project or a group project, carried out as a team with other Coventry University students in collaboration with students at international collaborators.

Whether working individually or as a member of a team, the identification of a topic will be achieved through a range of invited talks and the formation of topic study groups. Guided study will be enabled by the allocation of a project supervisor. 
Alongside the discovery component, there will be a lecture series addressing research methods, academic writing, results evaluation, and literature reviews. 

Compulsory

This is a practical module in which you will develop and deliver a primary research artefact and a dissertation. You may engage in individual development of the artefact and write an individual dissertation; or a group-developed artefact with a group-written dissertation and an individual critical evaluation and reflection. The group option will allow Coventry University students to collaborate with students at international participating institutions to develop a cross-site project. You will be supported by a supervisor (two if the group option is chosen) and the module is heavily focused on self-management. 

Compulsory

Choose one of the three optional modules:

Artificial Neural Networks - 20 Credits
This module introduces you to other important and popular machine learning approaches that are developed by mimicking how the human brain functions. Neural networks have been successfully used in a wide range of applications including image processing, speech and natural language processing, medical diagnosis, bioinformatics and computational biology, emotion recognition, robotics, control. This module looks into the introductory concepts used in neural networks and their application to solving real-world problems.  

Mobile Application Development - 20 Credits
Our everyday lives have never been more integrated with our mobile devices and applications. In this module, you’ll explore everything which goes into mobile application design, from the notion of RESTful APIs to continuous integration and analytics. You’ll demonstrate your understanding by using a development kit to build a portfolio of applications for mobile platforms.

Advanced Topics in Statistics - 20 credits
This module introduces you to several important topics in the areas of advanced statistics. This module is useful if you are interested in a Statistics postgraduate study or in careers involving Statistics or Data Science. 

Optional

Neural networks (NNs) represent an important and popular machine learning approach that attempt to model how the human brain works. NNs have been successfully used in a wide range of applications including visual image processing, speech and natural language processing, medical diagnosis and prognosis, emotion recognition, bioinformatics and computational biology, robotics, control, etc. This module introduces the concepts used in neural networks and their application to solve real-world problems. The main topics covered in this course will contain biological motivations of neural networks, different approaches including the main supervised and unsupervised neural network architectures, static and temporal learning approaches, data collection and preparation methods for neural network learning, applications of neural networks, current trends and future developments. 

Compulsory

This module provides you with an introduction to machine learning techniques, the associated concepts and applications. Machine learning is the process whereby systems learn by identifying structures and patterns within data. Machine learning has proved an important tool in various applications including data mining, games design, diagnosis and natural language processing. Machine learning covers a broad field of intelligent techniques and their associated algorithms, such as data preparation and pre-processing, reinforcement learning, supervised and unsupervised learning, classification and clustering, applications of machine learning and future developments. 

Compulsory

Information retrieval is among core knowledge driving some of the world’s successful and high-tech businesses including Google, Facebook, and Twitter. In this module, you will be exposed to a range of common information retrieval methods, from theory to practice. The modules main emphasis is text retrieval, covering only a brief outline of multimedia information retrieval. In addition, it emphasises data mining methods, especially for text classification and document clustering problems. 

Compulsory

This module introduces you to the current management and visualisation methods for Big Data. Cutting-edge techniques will be taught which will enable you to discover patterns, relationships and associations in big data sets. You will engage with the emerging critical issues within the context of traditional database management systems which make them unsuitable to process big data. Thus, the nature of big data, recognised by its volume, velocity and variety, which prevents analysis in the normal setting of a traditional database will be studied and advanced analytical techniques require to understand big data will be covered. 

Compulsory

This module provides you with a sound knowledge of the theoretical and practical underpinnings of data management systems in centralised and distributed environments. This module is motivated by the need to harness the potential of traditional systems and of modern schemes, which arose in response to the challenges posed by Big Data. The organisation and delivery of the module implements a two-pronged approach, through the investigation of data models and through the study and application of relational databases and NoSQL databases. The study of distributed frameworks such as Hadoop, and relevant distributed techniques such as sharding and replication, will provide further enhancement to the scope of the module. 

Compulsory

This module provides you with more advanced concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module is structured around recent developments on Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and topic modelling, and Probabilistic graphical models as a powerful framework for representing complex domains using probability distributions. In addition, this module will also provide you with the advanced concepts and techniques of evolutionary computation and fuzzy logic from an application-oriented viewpoint, where worked examples and case studies will be used to reinforce the taught material. Applications of fuzzy systems will be taught in combination with other machine learning techniques within a hybrid intelligent system. On completion of the module, you will also have a good understanding of evolutionary computation techniques and should be able to solve a wide range of real-world optimisation problems. 

Compulsory

This module prepares you to conduct your own research in the field of data science, under guidance from a supervisor. The exact details will depend on your preference and supervisor research interests, but this will generally include implementing or extending ideas from current research. In the preparatory module you will identify a suitable topic of study and a project supervisor. You will then exercise and extend your skills in gathering, understanding and critically evaluating literature; assessing and acting on relevant ethical and legal issues; and applying planning processes for the undertaking of a significant piece of work. 

Compulsory

In this practical module, you will build on the planning and literature survey from the preparatory module and conduct the proposed research.  You will implement the necessary tools, performing the research experiments, analyse the findings, and draw suitable conclusions. Finally, you will write up your work into a significant report. 

Compulsory