Undergraduate course

Data Science BSc (Hons)

In a world of big data and fake news, learn to cut through the noise with the creative computing skills of a Data Scientist.

Our Data Science degree will open up the hidden world of data to you and give you the skills needed to turn raw data into powerful and profound patterns and stories. Every day vast amounts of data are created and collected as we go about our lives. Smart phones, the internet of things, online forms, internet shopping – they’re all tracking and recording our behaviour and creating masses of information.

  • UCAS Code – G130
  • Location – York campus
  • Duration – 3 years full-time | 6 years part-time
  • Start date – September 2020
  • School – Psychological & Social Sciences

Minimum Entry Requirements

    96 points to include a C in Maths A-Level

    3 GCSEs Graded C/4 or above (or equivalent) including English Language and Maths

Tuition Fees

    UK and EU 2019-20 £9,250 per year

    International 2019-20 £12,750 per year

The York St John Experience

Course overview

The potential benefits of this information are enormous - but only if we have the ability to sort through it, analyse it and identify the processes underpinning it. Data Science is the set of methods, tools and knowledge used to unpick data and find the story behind it in reliable, robust and reproducible ways.

This is an exciting and emerging multi-disciplinary field with applications across almost every industry or aspect of life. Whether it is forecasting the spread of disease, teaching self-driving cars, predicting pedestrian flow at gigs and festivals, designing user-friendly websites, or preventing insurance fraud - data science has the answers.

 

Our course has a strong focus on probability and statistics, and their application to modelling and the analysis of data. You will explore content ‘hands-on’ through creative problem-solving, often exploiting powerful computational tools to perform statistical analysis, to find and visualise patterns and to assess uncertainties and unknowns.

Mathematics and computer know-how are important for Data Scientists but so is the ability to communicate, think creatively, visualise and solve problems. You will have specific training in computing, creative problem solving, communication, data analysis and entrepreneurship, equipping you for a broad range of future careers.

In the world of big data and fake news the skills of the data scientists are increasingly sought after. This course will enable you to interpret data sets, use powerful technology to answer important questions and, most importantly, to communicate your insights, and help people and policy makers to cut through the noise.

Course structure

Level 1

Level 1 develops your understanding of the core data science areas of probability, statistics & data analysis as well as computer programming and communication.

Modules in algebra, analysis and statistics will bridge the gap between school and university mathematics and underpin later advanced topics in data analysis, statistics and artificial intelligence.

You will gain valuable skills in creative problem solving and science communication in an interdiscplinary setting, learning and applying knowledge in groups alongside computer scientists, mathematicians and biologists.This interdisciplinary exposure will teach you to see problems from different disciplinary perspectives and to communicate your insights to specialists outside your field. This provides essential preparation for your future position as an interface connecting multiple disciplines.

Modules include:

  • Linear Algebra (20 credits)

In Maths and Physics we can use vectors to describe the locations of points in real space. In Data Science we can use these same vectors to describe the locations of data points in other, more abstract spaces. By picturing data in this way – imagining it existing as a series of locations, shapes or geometric objects – we can uncover a special way of understanding the data.

In this module you will find out about modelling space, how to describe geometric objects and their transformations (ways of moving them - through rotation, reflection or other movements). Through this study you will gain the ability to model abstract space, learning important techniques for visualising and manipulating data.

  • Programming 1 (20 credits)

Programming will provide an introduction to harnessing the power of computers to solve problems, analyse and then present data. The vast quantities of information in Big Data make it necessary to automate as much of the analysis as possible - it might take you a bit too long to do manually!

  • Practice in Interdisciplinary Problem Solving (20 credits)

This module will bring you together in a multidisciplinary context to contribute your unique knowledge and skills to solving problems collaboratively. You will learn the basics of scientific writing, and how to applying your computing knowledge to modelling and creating simulations of topics from a wide range of different areas. This versatility is part of the beauty of data science!

  • Analysis & Optimisation (20 credits)

Analysis describes the continuous functions underpinning all mathematical modelling. Some of these topics – such as differentiation and integration - might be familiar from school. Analysis can also help us improve processes and organisations by optimisation: what is the maximal benefit, the minimal cost, the best fit model, or the optimal way of performing a process? Analysis forms the basis of operational research, and helps us understand complex systems or organisations.

  • Probability, Statistics & Data Analysis (20 credits)

Probability theory describes our mathematical assumptions about the processes we are trying to understand. Statistics describes the quantitative methods used to analyse data on the basis of these assumptions. Data analysis uses powerful computational techniques to automate analysis so that it is reproducible and robust. This module will also touch on qualitative methods and the ethics of data.

  • Communication (20 credits)

We focus on both the communication of science and the science of communication. You will learn about cognitive pathways and the social and psychological aspects of communication. You will learn about storytelling for general audiences and how to keep listeners and readers engaged. You will produce talks, posters and group presentations and summarise your knowledge and research through storytelling.

Level 2

Level 2 builds upon the foundations in computer science, mathematics and multidisciplinary communication laid at level 1.

You will develop your ability to mathematically model and simulate diverse phenomena in the world around you, both analytically and via computer simulations. Your computational skills will be extensively developed towards advanced programming and data handling techniques.

You will get the opportunity to integrate different topics, tools and techniques in the context of an enterprise and research group project: diverse teams apply the knowledge they have gained in their individual courses of study to address a common question in research or entrepreneurship.

Modules include:

- Modelling & Numerical Analysis (20 credits)
- Graphs, Networks & Systems (20 credits)
- Databases (20 credits)
- Geometry & Groups (20 credits)
- Programming 2 (Data Science) (20 credits)
- Enterprise & Research Group Project (20 credits)

 

Level 3

Level 3 gets you to an advanced level of mathematical and computational sophistication and specialisation, with a portfolio of skills tailored to your personal interests.

There will be advanced modules that allow you to pursue a unique route exploring aspects of statistics, algebra or computer science and preparing you for the data science career path that you envisage focusing on.

A substantial individual research project also allows you to accentuate your programme of study and develop your interests in different directions. This dissertation is an extended piece of research and writing that shows your individuality, independence, creativity and communication skills, and as such is very highly rated by employers.

Modules

Modules may include:
- Dissertation (40 credits)
- Advanced Data Applications (20 credits)
- Number Theory, Information Theory & Cryptography (20 credits)
- Human Computer Interaction (20 credits)
- Internet of Things (20 credits)
- Advanced Web Development (20 credits)
- Artificial Intelligence (20 credits)
- Data Visualisation (20 credits)

Teaching & Assessment

Not surprisingly, on a Data Science Degree how we teach will change based on the data we collect about your learning experience. We will conduct mid and end of module evaluations and use this information to change what we do. We practice what we preach and listen to the voice of the student and use evidence based teaching methods.

Teaching methods include lectures, seminars, tutorials, Labs and practicals. Care is taken to ensure that students are taught using materials that they will encounter in the world of work. Data from industry is used to improve the employability of students and to give them ‘real world’ experience.

Students studying for a Degree in data science are expected to do around 20 hours of independent study a week. This self-study helps to develop self-motivation, discipline and good time management skills. Self-study can take a number of forms including i) preparation and revision for assessments; ii) solving problem sets; iii) reading set texts and iv) working on case studies.

Academic support is provided by both subject specialists and experts from across the University. This can range from guidance on how to present data using an info-graphic to how to manage demanding workloads

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