Data Science BSc
Explore this course:
You are viewing this course for 2024-25 entry. 2023-24 entry is also available.
- A Levels AAB
Other entry requirements
- UCAS code I2L9
- 3 years / Full-time
- September start
- Find out the course fee
- Optional placement year
- Study abroad
Taught by active researchers and developed with industry experts, you'll learn the data, information and analytical skills to become a critical data professional.
You'll analyse and critically evaluate a wide range of different real-life problems from a data science perspective. Whether it be studying data from a sports team to improve performance, using real-world data as part of the solution to climate change, preparing for spikes in hospital admissions or analysing business expenditure - data science is an evolving field.
You won't just be learning how to read and analyse data - you will be learning how to use data to make ethical decisions. The course has sustainability, equality, diversity and ethical practice at its core. You will be prepared for a career where you can use data-driven solutions to have a positive impact on society.
On our course you'll:
- become a dynamic, forward-thinking problem solver with a responsible approach to data and information
- learn how to present data to different audiences and stakeholders, using visualisation and statistical methods
- develop the skills you need to collaborate effectively with others to solve data-related problems and create responsible data solutions.
Opportunities for study abroad, work-based placements and developing your personal portfolio will put you in a strong position for the future.
The course is extended to four years if you opt to do a placement year or year abroad.
A selection of modules are available each year - some examples are below. There may be changes before you start your course. From May of the year of entry, formal programme regulations will be available in our Programme Regulations Finder.
Choose a year to see modules for a level of study:
UCAS code: I2L9
In year one, you'll develop fundamental capabilities and understandings in data science, including data visualisation and data modelling. With a strong focus on sociological theory, you will explore the underpinning concepts of responsible data science and the ethical application of technical approaches. You will also be introduced to computer programming and computational thinking.
- Practical Programming for Data Science 1
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.20 credits
- Data Modelling and Storage
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).10 credits
- Communicating Data
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.20 credits
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.
- Data Driven Organisations
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.10 credits
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
- Statistics for Insight
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.20 credits
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.
- Data Science Foundations and Contexts
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.40 credits
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.
In year two, you'll build on these foundations and apply these to the data lifecycle and team-based projects. You’ll enhance your programming skills to develop software that processes and analyses data in complex data structures.
- Ethical Data Management and Governance
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.20 credits
- Practical Programming for Data Science 2
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:10 credits
testing and debugging;
data wrangling and cleansing; and
data handling and analysis.
- AI and Machine Learning for Advanced Analytics
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?'20 credits
- Databases and Beyond
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.10 credits
- Using Data for Responsible Decision Making
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.20 credits
- Responsible Data Science Lab 1
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.40 credits
In your final year (year three or four, depending on whether you choose to do a placement year), you'll have the opportunity to specialise and to prepare yourself for employment through your portfolio and your independent study.
- Responsible Data Science Lab 2
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.60 credits
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.
- Data Science Portfolio
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.20 credits
- Building AI Applications
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.20 credits
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.
- Researching Social Media
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.20 credits
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption. We are no longer offering unrestricted module choice. If your course included unrestricted modules, your department will provide a list of modules from their own and other subject areas that you can choose from.
Learning and assessment
You'll learn through a mix of laboratories and practical classes, group work, interactive lectures and seminars, inquiry-based and self-directed learning. A diverse range of learning and assessment activities will support you to develop the Sheffield Graduate Attributes. You'll learn a broad set of skills, including teamwork and project-based tasks so that you will be ready for graduate career opportunities.
On each module, you will be taught by subject specialists who are also active researchers in their field. This research-led approach means that our curriculum is current and relevant, and it is further supported by visiting lecturers and other industry-based experts.
Our staff backgrounds and research reflect influences from computing, health, critical data studies and different social sciences disciplines, as well as experience from professional practice in data roles.
Your lecturers are here to support your development, meaning that you’ll be given extensive feedback on your work. We use a range of assessment methods including, exams, online tests, group/individual presentations and coursework.
This tells you the aims and learning outcomes of this course and how these will be achieved and assessed.
With Access Sheffield, you could qualify for additional consideration or an alternative offer - find out if you're eligible.
The A Level entry requirements for this course are:
A Levels + additional qualifications ABB + A in a relevant EPQ
International Baccalaureate 34
BTEC Extended Diploma DDD in Engineering, Applied Science, IT or Computing
BTEC Diploma DD in Engineering, Applied Science, IT or Computing + A at A Level
Scottish Highers AAAAB
Welsh Baccalaureate + 2 A Levels B + AA
Access to HE Diploma Award of Access to HE Diploma in a relevant subject, with 45 credits at Level 3, including 36 at Distinction and 9 at Merit
GCSE Maths grade 6/B
The A Level entry requirements for this course are:
A Levels + additional qualifications ABB + A in a relevant EPQ
International Baccalaureate 33
BTEC Extended Diploma DDD in Engineering, Applied Science, IT or Computing
BTEC Diploma DD in Engineering, Applied Science, IT or Computing + B at A Level
Scottish Highers AAABB
Welsh Baccalaureate + 2 A Levels B + AB
Access to HE Diploma Award of Access to HE Diploma in a relevant subject, with 45 credits at Level 3, including 30 at Distinction and 15 at Merit
GCSE Maths grade 6/B
You must demonstrate that your English is good enough for you to successfully complete your course. For this course we require: GCSE English Language at grade 4/C; IELTS grade of 6.5 with a minimum of 6.0 in each component; or an alternative acceptable English language qualification
Equivalent English language qualifications
Visa and immigration requirements
Other qualifications | UK and EU/international
If you have any questions about entry requirements, please contact the department.
The University of Sheffield Information School is ranked number one in the world for Library and Information Management in the QS World University Rankings by Subject (2022 and 2021).
By studying with us, you'll develop solid foundations in ethics, sustainability, critical thinking, and how to influence outcomes of data science to positively impact society.
We offer an outstanding academic education through the principles of research-led teaching, so you're always challenged and up to date.
The school has been at the forefront of developments in the information and data field for more than fifty years. The subject is characterised by its distinctive, interdisciplinary focus on the interactions between people, information and digital technologies.
Our students are from around the world creating a multicultural, vibrant and invigorating environment where you can thrive in your learning. As part of our mission to provide world-quality university education in information, we aim to inspire and help you pursue your highest ambitions for your academic and professional careers.
Our staff are experts in their field and work with organisations in the UK and worldwide, bringing fresh perspectives to your studies. They'll give you the advice and support you need to excel in your subject. We also work closely with partners and experts from industry, ensuring that your learning is always linked to your future career.
You'll have access to a high-quality, specialised learning environment including cutting-edge computing suites and our iLab usability testing facilities.Information School
Why choose Sheffield?
The University of Sheffield
A top 100 university
QS World University Rankings 2023
92 per cent of our research is rated as world-leading or internationally excellent
Research Excellence Framework 2021
Top 50 in the most international universities rankings
Times Higher Education World University Rankings 2022
No 1 Students' Union in the UK
Whatuni Student Choice Awards 2022, 2020, 2019, 2018, 2017
A top 10 university targeted by employers
The Graduate Market in 2022, High Fliers report
QS World University Rankings by subject 2022
As an evolving discipline, data science skills and knowledge are in strong demand with employers across a number of sectors.
We've worked closely with employers and industry partners to develop our curriculum to provide you with the relevant skills and experience to develop your future career. Our course is designed to equip students with the capabilities to manage the complexities of data in organisations and to integrate the work of data scientists with those in more managerial or policy-making roles.
All students have the opportunity to take either a placement year or a year abroad in between Levels 2 and 3. Students can also opt for a work experience module in Level 3 to spend time developing real-world skills with a local partner organisation or business.
Our annual Data Science Industry Day gives you an opportunity to meet employers and to link your learning at university with real-life contexts and challenges.
Some examples of the areas you may choose to explore include:
- Sustainability and global development
- NGOs, charities and third sector organisations
- Media and social media
- Finance and business
- Retail and ecommerce
- Public sector, transport and health
- Sports analysis
- Academia and research
Placements and study abroad
Fees and funding
The annual fee for your course includes a number of items in addition to your tuition. If an item or activity is classed as a compulsory element for your course, it will normally be included in your tuition fee. There are also other costs which you may need to consider.
Funding your study
Depending on your circumstances, you may qualify for a bursary, scholarship or loan to help fund your study and enhance your learning experience.
Use our Student Funding Calculator to work out what you’re eligible for.
University open days
We host five open days each year, usually in June, July, September, October and November. You can talk to staff and students, tour the campus and see inside the accommodation.
If you’re considering your post-16 options, our interactive subject tasters are for you. There are a wide range of subjects to choose from and you can attend sessions online or on campus.
Our weekly guided tours show you what Sheffield has to offer - both on campus and beyond. You can extend your visit with tours of our city, accommodation or sport facilities.
Telephone: +44 114 222 2646
The awarding body for this course is the University of Sheffield.
Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.
Any supervisors and research areas listed are indicative and may change before the start of the course.