BSc (Hons) Data Science Bachelor's degree at the University of Sheffield

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

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 questi

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

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 modul

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, an

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

The first half of 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 anoth

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 contr

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