DipHE Data Science
Full-time
Part-time
Two year
Four year
September 2026
In a nutshell
Data Science is a rapidly expanding interdisciplinary field that brings together statistics, machine learning, artificial intelligence (AI), and data analysis to uncover insights and solve complex problems. Data Scientists apply these techniques to a wide range of real-world challenges – from building recommendation systems that suggest your next favourite song or film on platforms like Spotify and Netflix, to developing cutting-edge AI tools that support medical diagnosis and improve patient outcomes.
Working with large and often complex datasets, Data Scientists train and apply machine learning models to extract patterns, generate predictions, and inform decision-making. Equally important, they translate their findings into clear and actionable insights through data storytelling, dashboards, and reports, ensuring that stakeholders across the organisation can make data-driven decisions.
What is a 'DipHE'?
A Diploma of Higher Education (DipHE) at Level 5 is awarded after two years of full-time study at university. Our DipHE programmes are hands-on and practical, with flexible learning options available so you can choose to study full or part time.
You can take a DipHE straight from college when you have completed qualifications like BTECs or A-Levels. Some people choose to take a gap year first or spend some time working before they start a DipHE.
Diplomas of Higher Education are perfect for people who want a university experience, but want the flexibility to qualify after two years rather than the full three years of a typical undergraduate degree. You get the support and teaching quality of a degree but don’t have to commit to three years of study before you begin. A DipHE can lead directly to a career as you will have gained valued skills and experience. However, you can also choose to stay on and gain a bachelor's degree by studying our one-year BSc (Hons) Data Science Top Up programme once you have completed your DipHE. This study route therefore provides you with greater choice and flexibility than a traditional degree.
This programme has been successfully approved for a HTQ higher quality mark. The HTQ quality mark means the course is an approved Higher Technical Qualification, a level 4 or 5 qualification quality marked by IfATE to indicate their alignment to employer-led occupational standards. Visit the Skills England website for more information on HTQ's.
You will:
- Be studying at a university with longstanding links in Data Science, giving you maximum employment and placeholder opportunities
- Broaden your understanding of data science, machine learning and AI, some of the most 'in demand' skills in the modern economy
- Gain experience using programming languages such as Python and SQL
- Learn through hands-on exercises, group activities and live industry briefs, and work with messy, real-world datasets in realistic, problem-based scenarios
- Build the practical skills you need for a broad range of career paths within data science and data analysis
- Develop your skills using a wide-range of industry-standard tools for data visualisation, statistical analysis and Big Data analytics
Course accreditations
This is for you if...
you are interested in finding out more about Artificial Intelligence, machine learning and Big Data
you have an enquiring mind, with a practical and analytical approach to problem solving
you enjoy working with data to spot patterns and trends or to solve problems
All about the course
Course delivery
This course has been designed alongside our industry partners to ensure that it meets the needs of industry. Modules that make up the programme have been developed as new learning experiences bespoke to the course, which will give you a concentrated two-year experience to prepare you for employment or top-up study.
Find out more about the modules below. The structure reflects a two-year full-time route:
Introduction to Data Analysis with Python
In this module, you will be introduced to the Python programming language and will build your skills working with a range of Python libraries for analysing, manipulating and visualising data, including NumPy, Pandas, SciPy, Matplotlib and Seaborn. Based around practical exercises and challenges, this module will develop your problem-solving skills and proficiency in Python. This module will also focus on developing your computational thinking, challenging you to think like a computer scientist when tackling problems.
Databases
This module will introduce you to relational databases and the fundamentals of Structured Query Language (SQL). In the first half of the module, practical workshops will introduce you to using SQL to query, extract and analyse data. In the second half, you will learn how to design a database and take into consideration issues such as data security, recovery and integrity.
Probability and Statistics
R is a widely used programming language in statistics, used to assess the governing company's financial performance, as well as other performance indicators, for example, patient outcomes for the NHS. You will undertake practical assessments from companies, evaluating statistical performance.
Data Visualisation
In this module, you will consider the importance of good data visualisation for communicating with data. You will learn the principles and theory behind data visualisation, best practices when visualising data and how to avoid misleading visualisations. Practical workshops will introduce you to industry standard tools for building data dashboards and reports and, across the module, you will create your own portfolio of dashboards and data stories.
Applied Machine Learning
This module will introduce you to the core concepts of supervised and unsupervised machine learning, and how we can use machine learning to discover patterns in data and make predictions.
The emphasis of the module is on practical application and you will use the Python programming language, and libraries such as Scikit Learn, to implement machine learning algorithms and build predictive models.
Introduction to Business Intelligence
This module sheds light on the use of business intelligence (BI) systems in organisational scenarios. The module will provide you with a broad set of skills applicable to the origins and evolution of BI systems, as well as distinctions between characters, data, information and knowledge.
Students will also learn about BI Systems, Data and Information, Problems with data, Data Warehousing, OLAP and Data Mining.
Artificial Intelligence & Deep Learning
Deep learning is at the heart of many of today's advances in Artificial Intelligence (AI). You will learn the theory behind neural networks and you will discover how deep learning is used within fields such as computer vision. Practical workshops will challenge you to build and train your own neural networks, working with a range of datasets of increasing complexity.
Professional Practices
The Professional Practices module will equip you with the research and professional skills required within industry. You will undertake team working used in the workplace, on real-world mathematics problems for which you will be required to develop a solution. You will also learn about the professional body, reflect on your skills and future direction with continuing professional development, and also take library-led courses on CV writing and soft skills development.
Big Data Analytics
In this module, you will learn the challenges and opportunities which characterise Big Data and will gain practical skills working with the tools and techniques used to process and analyse Big Data.
Text Mining and Natural Language Processing
In this module, you will learn the text mining techniques and methods used to analyse unstructured text and the latest deep learning approaches to Natural Language Processing (NLP). You will consider a wide range of applications, including text classification, document clustering, sentiment analysis and chatbots.
Data Science Project
In this module, you will use your data science and analysis skills to work with a complex dataset and solve a real-world business problem. You will synthesise the knowledge you have gained throughout the course to develop and justify your own solution. You will also develop your written and oral communication skills and learn how to communicate your results to both technical and non-technical stakeholders.
Digital Leadership & Management
This module sheds light on the role of business leadership and management in digital business scenarios. The module will provide you with a broad set of skills applicable to leadership in contemporary digital business, including skills development in management styles in digital business scenarios, digital business strategy, digital innovation and digital analytics and customer insights.
We take a flexible approach to our course delivery that promotes diversity and inclusivity and provides a blended learning experience, which will vary to meet specific programme requirements. This learning time includes formal lectures and interactive activities such as seminars, tutorials, practical sessions, laboratory and studio learning. Smaller classes may be used to support collaborative activities such as project and group work and presentations. A range of different assessments and feedback is offered to meet the needs of both our diverse student body and specific subject needs.
Our undergraduate courses are normally made up of 20 credit modules which are equal to 200 hours of learning time. A three-year degree qualification typically comprises a total of 360 credits (120 credits per year).
Frequently Asked Questions
What is a DipHE qualification?
DipHE stands for Diploma in Higher Education. This is a level 5 qualification (equivalent to the first two years studying a bachelor’s degree) undertaken for two years full-time or four years part-time.
Is a diploma in data science worth completing?
The vast field of data science is proving to be an exceptionally fast-growing sector to grow a career, no matter what focus area you choose.
Greater Manchester Institute of Technology
Located across England, Institutes of Technology (IoTs) are a national network of partnerships between local colleges, universities, and leading employers.
We are a proud partner in the Greater Manchester Institute of Technology. This means as a student on this course you will benefit from being part of the University of Salford community, with access to our facilities and support, and taught by our tutors. You will also be part of the GMIoT network, with access to additional events and activities.
What about after uni?
Employment
Demand for data scientists outstrips supply and there is continued demand for qualified professionals across many global industries. Recent government commissioned research shows that almost 50% of businesses are recruiting for roles that require hard data skills. On completing this course, you could apply for junior roles in data analysis or data science or continue to further studies on our BSc Data Science Top Up programme.
What you need to know
Applicant profile
As an applicant for this course, you will be interested in working with data and curious about the fields of Artificial Intelligence and Big Data. You will have gained some aptitude for mathematics at GCE A-Level (or equivalent) and have an interest in applying your knowledge to work with real-world datasets.
English language requirements
All of our courses are taught and assessed in English. If English is not your first language, you must meet our minimum English language entry requirements. An IELTS score of 6.0 (no element below 5.5) is proof of this, and we also accept a range of equivalent qualifications.
Read more about our English language requirements, including information about pathways that can help you gain entry on to our degree courses.
GCSE
Maths at grade C/level 4 or above (or equivalent) is required.
English Language at grade C/4 or above (or equivalent) is preferred but not essential.
You must fulfil our GCSE entry requirements as well as one of the requirements listed below.
UCAS tariff points
72 UCAS
A level
72 UCAS Points from Two full A-level equivalent. including grade C in Computer Science or Maths.
BTEC
MMP from BTEC in Computing or IT-related subject. Engineering or science subjects. Applied Science is accepted.
Access to HE Diploma
72 UCAS points from a QAA-approved course in Computing or Mathematics.
Scottish Highers
Two at Higher Level. Grade C in Advanced Higher Level Computer Studies or Mathematics.
Irish Leaving Certificate
Two at Higher Level. Grade C in Advanced Higher Level Computer Studies or Mathematics.
International Baccalaureate
30 points overall including Grade 5 in Higher Level Computer Studies or Mathematics.
Must have passed the full International Baccalaureate to be considered.
Salford Alternative Entry Scheme (SAES)
We positively welcome applications from students who may not meet the stated entry criteria but who can demonstrate their ability to pursue the course successfully. Once we receive your application, we'll assess it and recommend it for SAES if you are an eligible candidate.
There are two different routes through the Salford Alternative Entry Scheme and applicants will be directed to the one appropriate for their course. Assessment will either be through a review of prior learning or through a formal test.
For further information, please contact: enquiries@salford.ac.uk.
HOW MUCH?
| Type of study | Year | Fees |
|---|---|---|
| Full-time home | 2026/27 | £9,535 per year |
| Part-time | 2026/27 | part time fees will be calculated on a pro rata basis |
Additional costs
You should consider further costs which may include books, stationery, printing, binding and general sustenance on trips and visits.
All set? Let's apply?
UCAS Code: GG10
Start Date(s): September
Duration:
Two years full-time
Four years part-time
Enrolment dates
Student information
Terms and conditionsUCAS information
Course ID GG10
Institution S03