Data Science BSc (Hons)

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

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.

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

You now have the opportunity to pick an optional module to learn a new language or build on your existing language skills as part of your degree. You can choose:

• Subsidiary Language for Beginners (choice of German, Italian or Spanish)
• French: Intermediate Language Development
• Spanish: Intermediate Language Development
• Chinese: Intermediate Language Development
• German: Communication in Practice
• French: Communication in Practice
• Spanish: Communication in Practice

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.

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

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 analyse 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.

Choose one of the following:

1. Professional Placement (40 Credits) Optional
2. Term abroad (40 Credits) Optional
3. One of the following Language options

Advanced Language Development and Global Sustainability (40 Credits) Optional

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. 

Developing Intercultural Literacy and Cross-Cultural Skills (40 Credits) Optional

• 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.

Experiential Overseas Learning (40 Credits) Optional

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 

Post Beginner Language Development and Global Cultures (40 Credits) Optional

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. 

Upper Intermediate Language Development and Global Employability (40 Credits) Optional

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. 

Or you can choose ONE of the following:

• University Placement Year Optional
• Subject Placement Year Optional
• International University Placement Year Optional

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. 

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.