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This module introduces students to skills in computer programming and computational thinking needed for practical data science (e.g. decomposition, pattern recognition, abstraction and algorithms). Students will learn about the major programming paradigms used by data scientists (e.g. functional, object-oriented and event-driven) and explore the issues arising from the choices programmers make (e.g. potentially biasing assumptions about data or computation). 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 and productivity tools to support practical programming and extend base Python functionality. Students will also learn how to effectively use online resources for reference and training. Students will engage in problem-based learning throughout and practise key principles of effective group work in practical data science.

This module uses an inquiry-based approach to introduce students to the various technologies used to model and store data. Primarily, it focuses on the competencies needed to design and implement a well-formed, relational database, populating, manipulating and querying it using Structured Query Language (SQL) executed from within a computer program. SQL is also introduced as an industry standard method to 'wrangle' data. That is, to transform and map data from one form to one to another, to meet some data science goal. The entire data modelling and implementation process is undertaken with reference to other Level 1 modules and framed around responsible data science. This includes the importance of acknowledging data origins and the contexts of application when considering data modelling techniques, ensuring legal compliance, and the awareness of the Sustainable Development Goals (SDGs).

Many organisations are making use of data, analytics, and new technologies (e.g., Artificial Intelligence, cloud computing, Internet of Things, 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, data mining, predictive modelling and automation. A key use of data and analytics is to improve the outcomes (speed, accuracy, and relevance) for all types of decisions, from operational to strategic. The use of data science and more advanced techniques allows organisations to respond rapidly to changing requirements and contexts. In particular, the combination of predictive and prescriptive methods allows organisations to tackle complex problems, such as forecasting and simulating outcomes, that may assist with more informed and evidence-based decision making. 

This module will help students to understand the organisational and business contexts in which data and data science can be used to support digital transformation. This includes the people, cultures, processes, and technologies that are needed to become an effective data-driven organisation. As well as considering the opportunities and benefits of using data and analytics, this module will also consider some of the common barriers faced by organisations in adopting such approaches. Students will also learn about the importance of data leadership to drive concrete actions and the need for a clear data strategy to guide and drive organisations to use and manage data effectively and achieve their specific business goals.

The content in this module will be organised around three main themes:  - Organisations, the business context and the desire (and increasingly need) to be data-driven  - Building the capabilities of a data-driven organisation (i.e., what a data-driven organisation looks like) - The adoption of data and analytics, and developing maturity (i.e., how to create and grow as a data-driven organisation). 

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 and in information dissemination to the public.

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.

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 practicalities of real-world data science contexts. These contexts will include big business, academic research, cause-related charities as well as policy and public sectors.

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

Core content includes:

- the importance of useful data science, with critical understanding of how data science is used - in different contexts - 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 as well as ways that data are acquired, deployed, monitored and evaluated;

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

- the impact of data science and ethical innovations including critical data science, fairness, accountability, transparency, ethics and social justice (FATES), ethical data and Artificial Intelligence (AI), data and AI futures, data politics and activism, and using data for good causes;

- cross-cutting themes such as sustainability (and the Sustainable Development Goals [SDGs]), decolonisation, and intersectionality;

- 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.

Data influence decisions that change the shape of the world. Working with data, therefore, is to work within relations of ethics, governance, and power. This module will equip students with the skills to confidently articulate and navigate data power relations ethically by introducing them to advanced critical theories in data leadership, teamwork, and management. This module will explore how ethical data practices can foster inclusivity in organisational contexts, enabling data science to contribute toward progressive global goals. A crucial part of this effort requires an understanding of how power impacts diverse individuals and demographics in different ways. As such, this module will develop students' critical contemporary vocabularies in this area, allowing them to communicate social, cultural and political data concerns (such as those relating to intersectional inequalities) to future stakeholders in a variety of contexts. This module's focus on interpersonal skills, leadership and communication requires an assured understanding of the self, and we will explore ways in which data can be used to enhance individual and collective professional development to enable this in response. Overall, this module will provide students with the necessary skills to flourish in data work contexts where ethical practice, inclusive communication, and an awareness of wider global issues are increasingly important.

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.   Students will learn about approaches such as:  Support Vector Machines, decision trees, k-means, and deep learning 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 builds on the level one module Data Modelling and Storage. It introduces students to the different types of data and the consequential, contemporary data storage solutions including non-relational databases, data warehouses and data lakes. It also extends their data wrangling and SQL (Structured Query Language) skills to include the use of subqueries, matching, 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, the environmental costs of data storage and FATES (Fairness, Accountability, Transparency, Ethics and Security).

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 in data science, developing research, reflection, teamwork and project management skills. Students will work in project teams to plan, design, develop, evaluate and report on their project. The module helps students to deepen their knowledge and skills in project management through use of industry standard 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.