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Two students looking at a computer

Data Science BSc

Information School

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    You are viewing this course for 2023-24 entry. 2024-25 entry is also available.

    Key details

    Course description

    Students studying in the library

    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:

    Title: Data Science BSc
    UCAS code: I2L9
    Years: 2023
    Year one

    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. 

    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.

    20 credits
    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.

    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

    10 credits
    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.

    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.

    20 credits
    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.

    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.

    40 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.

    Programme specification

    This tells you the aims and learning outcomes of this course and how these will be achieved and assessed.

    Find programme specification for this course

    Entry requirements

    With Access Sheffield, you could qualify for additional consideration or an alternative offer - find out if you're eligible.

    Standard offer

    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 a relevant subject

    BTEC Diploma DD in a relevant subject + 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

    Other requirements
    • GCSE Maths grade 6/B

    Access Sheffield offer

    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 a relevant subject

    BTEC Diploma DD in a relevant subject + 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

    Other requirements
    • GCSE Maths grade 6/B

    English language requirements

    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.

    Information School

    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

    Information School

    Number 1 in the world for library and information management

    QS World University Rankings by subject 2023

    Graduate careers

    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


    You may have the opportunity to add an optional placement year as part of your course, converting the three year course to a four-year Degree with Placement Year. 

    A placement year will help you to:

    • gain an insight into possible careers
    • develop a range transferable skills 
    • build a professional network
    • get a feel for what you do and don’t like doing
    • add valuable work experience to your CV
    • gain experience of applying for jobs and interview practice
    • apply elements of academic learning in the workplace

    Study abroad

    Spending time abroad during your degree is a great way to explore different cultures, gain a new perspective and experience a life-changing opportunity that you will never forget. 

    You can apply to extend this course with a year abroad, usually between the second and third year. We have over 250 University partners worldwide. Popular destinations include Europe, the USA, Canada, Australia, Singapore and Hong Kong. 

    Find out more on the Global Opportunities website.

    Fees and funding


    Additional costs

    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.

    Examples of what’s included and excluded

    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.

    Visit us

    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.

    Open days: book your place

    Subject tasters

    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.

    Upcoming taster sessions

    Campus tours

    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.

    Campus tour: book your place

    Apply for this course

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    How to apply When you're ready to apply, see the UCAS website:

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    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.

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