Data Science BSc (Hons)

This module provides a strong foundation in the key areas of computing, i.e., mathematics, modern computing systems, cybersecurity, and professional skills. It equips students with the essential knowledge needed to tackle real-world computing problems and prepare them for advanced computing studies.

The mathematical aspect covers a range of topics that include:

• Set theory
• Geometric and trigonometric problem solving
• Vectors
• Linear, quadratic, fractional, and simultaneous equations
• Matrices
• Logarithmic functions
• Simple differential equations
• Introduction to complex numbers

The computing systems considers topics that include:

• Computer architecture - Hardware, software, and peripherals
• Basic computing mathematics - Number systems, binary operations, probability
• Logic gates and Boolean Algebra
• Machine Code and Assembly Language
• Compilers and Linkers
• Data Formats
• Operating Systems and File Management

The cybersecurity component addresses areas including:

• Cybersecurity Concepts
• The Human Weakness in Cybersecurity
• Cybersecurity Vulnerabilities
• Cybersecurity Exploitation
• Cryptography and Information Security
• Cybersecurity Auditing Methods
• Cybersecurity Defensive Strategies
• Programming for Cybersecurity with Python
• Ethical Hacking and Penetration Testing Lifecycle

The professional skills aspect encompasses subjects including:

• Employment & Employability
• Curriculum Vitae
• Role of Professional Bodies
• Security Obligations, Ethics and Law
• Copyright and IPR Concepts
• Report Writing and Referencing
• Proposal and Business Plans
• Social Networks and Collaboration
• Data Collection Methods
• Reflection

This module introduces fundamental concepts in database systems, emphasising their importance in modern computing. Students will gain theoretical and practical skills in designing, implementing, and querying relational databases, while also exploring alternative database management systems (DBMS), such as NoSQL and distributed databases. Through hands-on experience with tools like SQL and exposure to NoSQL systems, students will learn to choose the right database solution for different use cases.

The aims of this module are:

• Understand the role and importance of databases in computer systems and applications.
• Explain core database concepts, including schemas, tables, keys, and relationships.
• Design normalised database schemas using Entity-Relationship (ER) modelling.
• Implement relational databases in a DBMS.
• Write SQL queries for data definition, manipulation, and retrieval.
• Compare and contrast relational databases with alternative database models such as NoSQL and distributed systems.
• Explore use cases for NoSQL databases, including document stores, key-value stores, and graph databases.
• Understand the challenges and benefits of distributed databases, including replication and scalability.
• Awareness of basic techniques for database optimisation and indexing.
• Discuss data security, privacy, and ethical considerations in database design and use.

This module explores the design and development of computer software (applications) from two perspectives:

1. The implementation of software using programming code, in a high level statically typed, object-oriented programming language, such as Java.
2. The user-centred design approach to software design, considering human factors, user experience, usability, and prototyping.

The programming aspect covers a range of topics including

• The syntax and semantics of a programming language
• Different types of data, including collections
• Control flow - conditional, iterative and recursive
• Data input and output (CLI and file based)
• Object-orientation, including class design and inheritance
• Unit testing
• Basic refactoring

The human aspect considers topics that include

• User-centred design
• Human factors
• Usability
• Accessibility
• User experience
• Low and high-fidelity prototyping
• Evaluation

The module focuses on the application of computer programming, and related technologies, in solving everyday problems. In this process, problems and challenges will be analysed, leading to the selection and deployment of tools and techniques in response.

As a problem-based learning strategy is employed, the syllabus of the module is fluid from occurrence-to-occurrence. However, central themes and foundational concepts that will be covered are:

• Computational thinking (decomposition, abstraction, algorithm design, pattern recognition)
• Collaborative software development
• Solution evaluation
• Project portfolios

This module enables students with A-Level German or equivalent to further develop their grammar, vocabulary and expression and apply these to real world situations.You will work with written and recorded texts on a range of cultural, personal and social topics and will develop your oral and written communication skills at Post-A level.

This module is designed for students that have completed A-Level or equivalent in French. You will further develop your grammar, vocabulary and expression and apply these to real world situations. You will work with written and recorded texts on a range of cultural, personal and social topics and will develop your oral and written communication skills at Post-A level.

This module is designed for students that have completed a A-Level or equivalent in Spanish. You will further develop your grammar, vocabulary and expression and apply these to real world situations. You will work with written and recorded texts on a range of cultural, personal and social topics and will develop oral and written communication skills at Post-A level.

This module is designed for students who have completed GCSE in Chinese or equivalent. You will further develop your grammar, vocabulary and learning conventions for spelling and pronunciation. You will work with written and recorded texts on a range of cultural, personal and social topics and will develop oral and written communication skills at an intermediate level.

This module is designed for students who have completed GCSE or equivalent in French. You will further develop your grammar, vocabulary and learning conventions for spelling and pronunciation. You will work with written and recorded texts on a range of cultural, personal and social topics and will develop oral and written communication skills at an intermediate level.

This module is designed for students that have completed GCSE or equivalent in Spanish. You will further develop your grammar, vocabulary and learning conventions for spelling and pronunciation. You will work with written and recorded texts on a range of cultural, personal and social topics and will develop oral and written communication skills at an intermediate level.

This module provides the opportunity to study a new language from scratch and introduces you to basic grammar, vocabulary and cultural contexts. You will apply the language to practical situations using both oral and written skills. 

Many optimisation problems in business and industry can be expressed in the form of a linear programming problem and this is even more apparent with increasing reliance on Artificial Intelligence and Machine Learning.  Businesses and industry use linear programming to determine what to make in order to maximise their profits, Amazon use it to schedule your parcels for delivery and it is also used widely in Game Theory: you can use it to beat your friends at rock-paper-scissors and other games!

In this module, we will study the theoretical background behind the linear programming methods, learn how to express real-world questions as linear programming problems and solve them by hand and using computer programs.  We will also explore some other optimisation methods used in AI and Machine Learning. Topics may include:

• Canonical forms of linear programming problems.
• Theoretical considerations: relevant results from set theory and geometry.
• Integer linear programming.
• Solutions of linear programming models: Simplex and dual simplex methods, Pivot algorithm, and computer-based techniques.
• Degeneracy, cycling, duality.
• Use of a mathematical computer software package, for example, Python, Matlab, Excel etc.
• Application to logistics in transportation and assignment problems, VAM and the Hungarian algorithm.
• Game theory: zero-sum matrix games, multi-phase games.
• Non-linear optimisation techniques in Machine Learning

This module explores the fundamental ethics and principles of artificial intelligence and understanding how it impacts on society, specifically examining the ethical, social and technical challenges posed by AI systems. As part of this module, students will develop their understanding of key ethical principles, the societal impact of AI and will evaluate how human factors can influence system design/model development and potentially perpetuate human biases. This module will also explore different governance approaches and legislative requirements for AI and will examine the diverse strategies for mitigating discrimination and bias in AI based systems.

Stochastic processes serve as essential mathematical models for systems and phenomena exhibiting apparent randomness. Examples encompass diverse scenarios, such as the growth of a bacterial population, fluctuations in electrical current due to thermal noise, or the motion of gas molecules. The applications of stochastic processes span various disciplines, including biology, chemistry, ecology, neuroscience, physics, image processing, signal processing, and financial markets. In order to understand such random behaviour, we introduce and study Markov chains, random walks, Brownian motion and stochastic differential equations. Through these topics, students will not only establish a robust foundation in the principles of stochastic processes but will also gain valuable insights into their diverse applications across numerous domains. The module's goal is to equip students with analytical tools essential for comprehending and modelling complex uncertainties, thereby enhancing their capacity to address real-world challenges in mathematics, statistics, and related fields.

Topics covered include:

• Brief review of Probability Theory via a Measure Theory approach.
• Martingales: Basic definitions, filtrations, stopping times.
• Doob's Martingale inequalities and Convergence Theorem.
• Markov Chains.
• Moment generating functions.
• Characteristic functions.
• Probability generating functions.

This module provides a comprehensive introduction to data analytics, focusing on foundational concepts and practical applications. Students will develop essential skills to analyze data, solve real-world problems, and explore the rapidly growing field of big data analytics. The key perspectives are:

• Introduction to  the data analytics process, including data collection, cleaning, analysis, visualisation, and interpretation.
• Hands-on experience with data analytics tools and techniques.
• Exploring big data analytics concepts, including scalable data processing and analysis.
• Promoting ethical decision-making and effective communication in data-driven contexts.

The data analytics process covers :

• Identifying business problems and defining objectives.
• Collecting and cleaning raw data for analysis.
• Performing exploratory data analysis (EDA) to uncover patterns.
• Visualising and interpreting results to derive actionable insights.

The statistical and analytical thinking aspect includes: 

• Measures of central tendency and dispersion.
• Regression analysis and correlation.
• Data modelling and simulation.

The tools and techniques component covers:

• cleaning and preprocessing using Python or R.
• SQL for querying and managing databases.
• Data visualisation using tools like Tableau, Power BI, Matplotlib and Apache Superset
• Introduction to big data ecosystem.

The big data analytics aspect addresses:

• Scalable data storage and processing.
• Analysing large datasets with distributed systems.
• Leveraging machine learning for big data insights.
• Real-time analytics and stream processing.

The ethical and professional skills aspect encompasses:

• Ethical issues in data privacy and security.
• Effective communication of data insights through storytelling.
• Collaboration and teamwork in data projects.
• Presentation and reporting of analytics outcomes.

This module provides a structured, university-level work placement for 4, 5 or 7 weeks (optional timeframe decided on a 1 2 1 basis) as one continuous block / period with a placement provider (i.e. industry apprioprate sector). It is designed to enhance your professional skills in a real-world job setting.

The placement can either be organised by you or with support from university staff.

All work placements within this module must be university-level; this means:

• Undertaking high-skilled work commensurate with level 5 study (e.g. report writing, attending meetings, delivering presentations, producing spreadsheets, writing content on webpages, social media, marketing services/products etc)
• Physically placed (albeit part of it can be hybrid) within an employer setting in one continuous block / period for 4, 5 or 7 weeks for a minimum of 140-147 hours over the course of the entire work placement

Where applicable, your existing part-time employer can be approached/used as the placement provider, if the high-skilled work.

• criterion above is fulfilled for the full duration of the placement.
• All quality assurances/agreements provided by the University are adhered to, by you and the employer.

The work placement context may not necessarily, reflect your degree discipline per se, but rather, it will give you an enriched experience to enhance your professional skills in a real-world job setting.

This is an experiential learning opportunity that incorporates 35 teaching contact hours/lectures to prepare for the 150 contract hours. L5 students can use all their skills learned to date on an actual real-world (external business) client-driven project, working in teams and producing an artefact. 

You are also expected to undertake around 215 hours of self-study.

This module not only gives them enhanced skills but the opportunity to work for a real client thus giving them a valuable CV and LInkedIn entry as work experience that can contribute to their employability portfolio. 

You will collaborate in teams and produce full client documentation alongside a reflection of their expereince and this all give some much needed contemplation of their skills to date and how to use them. 

The module will provide the opportunity to further develop your language skills, building on your previous learning at advanced level. The second half of the module includes a placement abroad or, alternatively, a project on a sustainability issue in a target language country. The first half of the module will prepare you for placements abroad where appropriate as well as a deeper understanding of sustainability in target language contexts. Students of more than one language may take one language in the first half of the module and spend their time abroad developing a different language. 

The module will provide the opportunity to further develop your language skills, building on your previous learning at intermediate level. The first half of the module includes intensive taught sessions in interactive workshop mode which will prepare you for placements abroad or self-directed language development. The second half of the module includes an placement abroad or, alternatively, a project on a business or tourism issue in a target language country. Students of more than one language may take one language in the first half of the module and spend their time abroad developing a different language. 

The module will provide the opportunity to further develop your language skills, building on your previous learning at beginner level. The first half of the module includes intensive taught sessions in interactive workshop mode which will prepare you for placements abroad or self-directed language development. The second half of the module includes a placement abroad or, alternatively, a project on a cultural issue in a target language country. Students of more than one language may take one language in the first half of the module and spend their time abroad developing a different language. 

• The multiple facets of global citizenship
• Ethical engagement and practice
• The United Nations Sustainable Development Goals
• Cross-cultural issues and sensitivity
• Intercultural communication
• Culture shock
• Cultural adjustment
• Self- assessment of needs: identification of the range of transferable skills, competencies and attitudes employees need and employers expect graduates to possess-with a strong focus on understanding the intercultural competencies (ICC) needed to live and work abroad.
• Critical analysis/evaluation of individual requirements in relation to culture/cultural adjustment/culture shock/visas/medical.
• Critical analysis/evaluation of skills already acquired in relation to key skills related to ICC.
• Devising strategies to improve one’s own prospects of working abroad in the future.
• Devising an action plan to address gaps in transferable skills based on organisational analysis and sector opportunities.

Part A:      

Preparation for Experiential Overseas Learning will take place at the University of Chester during level 5 and will include:  

• The multiple facets of Global citizenship
• Ethical engagement and practice
• Cross-cultural issues and sensitivity
• Intercultural communication

Theories, models and strategies of learning

• Theories and models Intercultural competence
• Theories and models of Integration and Multiculturalism
• Critical thinking skills and models of Reflection

• Experiential learning models
• Self-directed experiential learning

Personal and placement-related skills

• Enhanced independence
• Improved command of multicultural behaviour
• Increased knowledge and confidence in their individual facets of personal identity
• Effective time management and organisational skills
• Project management – working away from University and independent study
• Self-management and personal development
• Team building and team work

Part B:            Overseas

Students will engage in experiential learning activities overseas for at least 150 hours 

Year in Industry placement year for computing students. 

This is a placement module where students acquire a 1 year paid work placement related to their area of study.

Full attendance at the work placement and University-scheduled events is mandatory.

The placement hours stated for this module are only the minimum required hours for this module, it is likely most placements will last for 9 months - 12 months and exceed the minimum 1,180 placements hours.

The traditional academic programme structure is not applicable. The placement content is freely structured and determined by negotiation between the student, placement supervisor and host organisation. It is generally informed by the aims and learning outcomes and by the objective of optimising added value for both the host organisation and the student experience

Preparation for the year abroad will take place in Chester during level 5 and will include:

• Cross-cultural issues and sensitivity
• Host-country orientation, study methods– economic, political and social reality of the country
• Orientation specific to exchange – health, education, gender issues
• The United Nations Sustainable Development Goals
• Practical matters relating to living and studying in the wider world

Theories, models and strategies of learning

• Critical thinking skills, experiential learning and models of reflection

Personal and placement-related transversal skills

• Effective self-motivation and independent resourcefulness
• Effective time management and organisational skills
• Project management – working away from University and independent study
• Self-management and personal development

Whilst abroad:

You will undertake study at one of the University of Chester's partner universities or undertake and approved work placement or virtual placement. If you are a student,  it is expected that you will choose a series of modules at the university abroad which must be agreed by the host institution and the Module Leader. you must supply details of you modules on a learning agreement within 4 weeks of arrival at the host university.

Students will attend a combination of interactive lectures and practical workshops designed to provide theoretical knowledge and understanding with experiential learning by applying key concepts, tools and models in innovative ways. Lectures will predominantly explore key business enterprise and development concepts, models, theories and examples whereas workshops will enable students to practically apply these business insights to their specific area of study through individual and group work. As part of the learning experience, formative and summative assessments give students the opportunity to discuss their work with tutors and gain valuable feedback as they develop and apply their learning in ‘real-life’ entrepreneurial contexts. 

Quality Control is an important application of statistical theory and methods. Since the industrial revolution, factories have needed to find ways to manufacture high quality products while minimising cost to maintain a competitive edge. From monitoring an automotive production line to stop out-of-control processes, to sampling techniques in the food production industries designed to minimise food waste, quality control procedures form a key part of the modern manufacturing process.

In this module you will learn the techniques companies employ to achieve the above. Topics for this module may include:

• Introduction to quality control: summary of relevant probability theory and probability distributions.
• Methods and philosophy of statistical process control.
• Control charts for variables: mean, median and range charts, special charts.
• Control charts for attributes: p-charts, np-charts, u-charts, c-charts.
• Process capability studies.
• Acceptance control charts.
• Acceptance sampling: single, double, and sequential sampling plans, rectifying inspections.
• Modified control limits.
• Cumulative sum techniques: CUSUM Charts, V-Masks, Tabular CUSUM.

This module offers an in-depth exploration of artificial intelligence (AI) and its transformative role in the development of advanced software systems. It introduces key theoretical approaches and practical techniques for designing and deploying intelligent technologies, empowering you with the skills to build AI-driven solutions.

Key topics covered include:

• Introduction to Artificial Intelligence: Understanding the foundations of AI and its significance in modern software development.
• Theoretical Approaches to AI: Exploring algorithms and models that underpin intelligent systems, such as decision trees, neural networks, and reinforcement learning.
• Practical AI Implementation: Gaining hands-on experience with AI techniques, including machine learning, natural language processing, and computer vision, through coding exercises and projects.
• Designing and Deploying Intelligent Systems: Examining methods for building robust, scalable, and ethically sound AI technologies.
• AI in Various Domains: Critically evaluating how AI is applied across industries such as business, healthcare, education, law, government, and scientific research, along with the ethical and societal implications of these applications.

This module blends theory with practical application, equipping you to develop intelligent systems and critically assess their impact in a wide range of real-world contexts.

Recognition of the need to apply data science applications in organisations.

Establishment of correct selection and application of data science techniques (i.e. data shaping, model type selection, testing and application) in various organisational contexts. 

Application of common machine learning tools (e.g. logistic regression, non-linear model estimation, neural network) in a common development environment (e.g. R, Python, Scala) in preparation for the real world context.

Evaluation of the role of ethics in the application of data science techniques

Students will undertake a large self-directed software project in a specialist topic of their choice with guidance and support from a dedicated academic supervisor.

The project will begin with an appraisal of said topic, usually through a literature review and/or a commercial assessment of viability. This will be followed by planning and creation of a practical software artefact covering an implementation lifecycle, making use of project management techniques.

Ethical issues will be explored, leading to required approval for quantitative and/or qualitative testing, with results then analysed and used to inform futher development and to draw conclusions against a hypothesis.