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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.

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. Students will reflect upon the different themes covered on the module and their learning and development through a personal portfolio.

The 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;- the impact of data science and ethical innovations including critical data science, fairness, accountability, transparency, ethics and social justice (FATES),- cross-cutting themes such as sustainability (and the Sustainable Development Goals [SDGs]), decolonisation, and intersectionality.

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 in other first year modules, including (but not limited to) handling data in different file formats (e.g., CSV), structures (e.g., table, JSON), and implementation and transformation of different data structures, testing concepts to identify defects/errors while writing code. The module will develop skills such as:

handling and understanding different data structures; 

data wrangling and cleansing; 

data transformation and analysis; and

testing and debugging

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. It will explore the core legislation, standards and codes of conduct related to data. Students will also learn about 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. Finally, beyond theoretical understanding, students will also explore case studies to gain insights into how data science principles are applied in various organisational settings and across different industry domains.