Data Science BSc

This module introduces students to computer programming and computational thinking (e.g. decomposition, pattern recognition, abstraction and algorithms). It covers the major paradigms used by data scientists (e.g. functional, object-oriented and event-driven) and explores the issues which arise by the choices programmers make (e.g. assumptions which are biased). The module will focus on programming with Python, one of the most widely used languages in data science. The module will also teach students how to use packages to extend base Python functionality and the use of online resources for reference and training. Students will engage in problem-based learning throughout with more open, inquiry-based learning towards the end of the semester.

This module will equip students with the knowledge and skills needed to acquire, manage and use data effectively and ethically. This includes data wrangling - transforming and mapping data from one form into another -  and how to interact with and interrogate databases from within programming languages (e.g., Python). It introduces data in its various forms, helps students to develop skills to think critically about data, how it can be collected or captured for various purposes, and how it can be effectively stored and organised. Commonly, Data Scientists must interact with various sources of data and storage technologies, such as databases. This requires being able to 'wrangle' data, query and manipulate data using Structured Query Language (SQL), and being able to model data conceptually so that effective, efficient and ethical databases can be designed, built and integrated into data science projects. This module will reinforce teaching on other Level 1 modules by reminding students of the importance of acknowledging data origins and the contexts of application when considering data techniques, ensuring legal compliance, and awareness of the Sustainable Development Goals (SDGs).

The vast amounts of information in a variety of types provide both opportunities and challenges to organisations daily. A primary aspect of data science is to make this information accessible to different groups of audiences, in different forms and mechanisms. Visualising data is an essential skill in communicating data effectively and is therefore a key process in decision making within organisations. 

This module will focus on theories and methods for visualising and presenting data and insights to different audiences. The module will discuss the building blocks of data visualisation, such as visual elements, and cover how to create and critique different visualisations to display data. The module will also cover design considerations and good practices in data visualisation and presentation.

Many organisations are making use of data science and new technologies (e.g., Artificial Intelligence (AI), cloud computing, IoT and Big Data) to drive digital transformation and become more 'data-driven'. Data science (and increasingly AI methods) can be applied in many ways within organisations and used for activities including business intelligence, advanced analytics, predictive modelling and data mining. This module will help students to understand the organisational and business contexts in which data science may operate, including people, cultures, processes and technologies. Students will learn about data strategy within the context of an organisation to understand how it guides and drives the organisations to use and manage data to support its specific business goals.

The module content is organised into three broad areas:

An introduction to organisations and being data-driven;

Building the capability of a data-driven organisation;

Data and analytics transformation and growth.

Students will be exposed to common use cases and applications from across sectors, highlighting the potential opportunities, as well as socio-technical challenges, for adoption and use. This will include talks from external speakers working in organisations that utilise data science and AI. The module will help students examine data-driven organisations, how they use data science across a range of organisational settings, and learn how organisations can make the most of using data and analytics to achieve digital transformation.

Students will learn some of the key principles involved, such as: 

The typical roles and responsibilities of employees who contribute to providing data science and analytical capabilities;

Data strategy that guides the use of data for organisation purposes;

The technologies needed to support a data-driven culture and way of working;

How data science projects and innovations are planned and managed;

The typical transformations that are needed to mature the use of data science and analytics and build an effective data-driven organisation. 

Students will also learn about common organisational challenges and barriers to adoption and becoming data-driven. Throughout the module the role of data or analytics 'translator' will be discussed to help mediate between organisational stakeholders and data specialists

This module equips students with a comprehensive overview of the fundamental aspects of quantitative research methods and statistics. Students undertaking the module will gain experience in dealing with data and ways to analyse and report them. Using data from a range of applications and sources, students will learn practical statistical techniques and fundamental principles, as well as using IBM SPSS software to analyse data to make inferences and predictions.

In the initial part of the module students will learn research question development, study design, data cycle, sampling and confounding, types of data, graphical and tabular representation of data and results, summarising numeric and categorical data. Students will then move on to learn about data distributions, hypothesis testing, confidence intervals and probability theory to build the knowledge-base required to undertake inferential statistics to make deductions about populations.

Inferential statistics techniques covered include parametric (e.g. t-tests, ANOVA, correlations) and non-parametric tests (e.g. Mann-Whitney, Kruskal-Wallis), bootstrapping and regression analysis. The module will also actively link with the learning undertaken in other Level 1 modules on the programme. Students will put into practice their newly acquired knowledge of statistical tools.

This foundational module underpins our approach to teaching future data scientists. It develops students' essential skills and awareness of the ethics and applicability of real-world data science contexts, whether that is big business, academic research, cause-related charities or public sector and policy.

Fundamentally, this module addresses two questions: firstly, 'What makes data science a science?', through material on the origins and orders of data science; and secondly, 'How does thinking about data science as a social and information science help us imagine and realise more ethical and sustainable futures?', through contexts of data.

Core content includes:

- the importance of useful data science, with critical understanding of how data science is used - in context - for good and bad;

- foundational professional skills and literacies (data, information, ethical and academic);

- how data work in different contexts: in the workplace, personal data and different geographies, domains and industries;

- how contextual data can improve understanding and how data is acquired, deployed, monitored and evaluated;

- the different origins and orders of data science including its history, perspectives and disciplines;

- the various concepts and applications used within data science such as the data-information-knowledge-wisdom (DIKW) pyramid and data lifecycles;

- the impact of data science and ethical innovations including critical data science and Sustainable Development Goals (SDGs), ethical data practices, ethical Artificial Intelligence (AI), data and AI futures, data politics and activism and using data for good causes;

- the benefits, challenges and threats of AI and data-driven approaches to decision-making, as well as human computer interaction across multi-cultural contexts;

- the core legislation, standards and codes of conduct related to data;

- concepts such as fairness, accountability, transparency, ethics and social justice (FATES) which will underpin students' future studies and actions in the workplace;

- cross-cutting themes such as sustainability, decolonisation and intersectionality.

Data stewardship is the collection of practices that ensure an organisation's data is accessible, usable, safe, trusted and fairly used. This module teaches students how good data stewardship can assist an enterprise in responsibly leveraging its domain data assets to full capacity. This requires a set of competencies to ensure data are properly managed, shared and preserved, throughout the data lifecycle, from curation to long-term preservation. To enable this to happen, students are taught about what it means to be an effective manager and team member, and about data management, data governance and the legal frameworks in which data stewards operate.

Focusing on one popular programming language, this module will teach students how to effectively use programming and computational tools for data processing and analysis. It will cover or extend the topics covered at Level 1, including (but not limited to) handling data in different file formats (e.g., CSV), structures (e.g., table, JSON), and transformation of data structures. The module will develop skills such as:

collaborative coding;

testing and debugging;

data wrangling and cleansing; and

data handling and analysis.

This module will introduce advanced data analysis methods that can be used to gain new insights from data by identifying important patterns and trends, and summarising the findings to inform decision making in such a way as to not lose sight of data origins, context and social impacts. The module will examine fundamental Machine Learning algorithms for data exploration, including clustering and classification, as well as for making predictions on future data using machine learning (e.g., SVM, decision trees, k-means). Topics such as feature selection and evaluation issues (e.g., measures and standardised benchmarks) will also be introduced. Case studies will be used throughout the module to demonstrate the use of advanced analytics and data mining methods for tackling real-world problems. Examples will also highlight how imprudent use of such methods has led to biased, unethical, or unfair outcomes. Students will gain practical hands-on experience through the use of widely-used software tools. This module also asks the core question, 'How does the data analysis approach influence outcomes?'

This module builds on the level one module Data Modelling and Storage to introduce students to more advanced and contemporary data storage and manipulation solutions and problems, and to further improve their data wrangling skills. This includes more sophisticated relational database queries using SQL, as well as topics including: non-relational databases (e.g. MongoDB), data warehouses (e.g. Amazon Redshift) and data lakes (e.g. Snowflake). Students will be taught common advanced SQL queries used by data analysts for ranking data, calculating delta values, calculating running totals, creating reports based on multiple conditions, and calculating summary statistics. When designing, implementing and querying data storage solutions, students are encouraged to consider bias, data-related legislation (e.g. GDPR), FATES - in particular, data security, but also appropriateness of data storage - and the environmental costs of data storage.

In this module, we investigate a broad range of data usage purposes including organisational management, policy-making and service-oriented decisions. It promotes an awareness of power dynamics in data practices and addresses questions like, 'How might we create a positive culture around data use, and influence effective 'data-driven' decision-making?'; 'In what ways are data used to influence decisions, and what are the effects?' It covers methods to improve the transparency of data use, algorithmic fairness, disinformation on the Web, criticality aspects of data literacy and wider societal impact of data science and bias.

In this module, students work through a guided inquiry of a data science problem, developing research, reflection, teamwork and project management skills. Students will work in agile project teams to plan, design, develop, test, deploy and evaluate their solution iteratively in phases. The module helps students to deepen their knowledge and skills in project management, by working through the phases using agile methods.

This module allows teams of students to investigate a challenge and build a data-enabled solution to address it. Unlike the Level 2 module Responsible Data Science Lab 1, students are responsible for directing the entire project, with staff having a more advisory role. It is also the first substantial project in which students have the opportunity to work with external partners on projects that can make a real difference in the world and have a positive impact.

Students will pitch ideas to form teams, and will be able to work on problems posed by external partners. Teams will develop a working prototype of their responsible innovation, and improve on it until its completion through effective project management. Teams will also consider what happens after the project through evaluation and sharing of their results. Students will work collaboratively in their teams to prototype, test, iterate, evaluate and share their project with the world. Teams have access to tutors to identify and address any knowledge and skills gaps throughout the process.

Teams will be assessed on the quality of their working prototype, as well as on how each member of the team has taken a growth-mindset to contribute in their own capacity. Teams will also communicate how the project makes a positive impact in a video or visualisation. This module ultimately tests how students can bring all of the skills, knowledge and resources of the programme together as independent and creative responsible data scientists.

This module consolidates and develops the knowledge gained throughout the BSc Data Science degree. It provides opportunities for students to investigate data science topics which interest them, be inspired by experts in the field and produce a portfolio of evidence of their achievements. The latter might be of interest to an employer.

A common outcome of data science activities is a product, service or application. These outcomes are designed to address specific problems and are based on inquiries within an organisation or a broader community.

The development of these outcomes usually starts with identifying the problem that needs to be addressed, broader constraints and scope of the problem, studying the domain and stakeholders, proposing a data science solution and finally evaluating the success of the activity.

In this module, students will learn how to make use of various methods and technologies - such as AI and machine learning, web programming, APIs, open source libraries and frameworks - to develop 'intelligent' data-driven applications, such as chatbots, recommendation, search, image recognition and sentiment analysis systems. Using existing cloud-based services such as Microsoft Azure, open source tools such as Tableau, easy to use libraries such as Keras and development frameworks such as ionic, react and cordova, students will learn how to design, build and test intelligent applications. These applications will be end-to-end solutions, based on inquiries that students identify to help solve real-world problems posed to them by industry representatives.

In this module, students will evaluate the theoretical perspectives on social media ecology. After constructing an ethical stance for collecting social media data, students will select and apply appropriate traditional and digital research methods for social media data collection and analysis. Finally, students will critically evaluate the research methods employed.