-
Study
-
Undergraduate
- Search for a Course
- Undergraduate Open Day & Events
- Application Guides
- Northumbria University UCAS Exhibitions
- Foundation Years
- Undergraduate Fees & Funding
- School & College Outreach
- Continuing Professional Development
-
Postgraduate
- Postgraduate Study Degree
- Postgraduate Research Degrees
- Postgraduate Open Days and Events
- Postgraduate Fees & Funding
- Flexible Learning
- Thinking about a Masters?
- Continuing Professional Development
- Change Direction
-
Student Life
- The Hub - Student Blog
- Accommodation
- Life in Newcastle
- Support for Students
- Careers
- Information for Parents
- Students' Union
- Northumbria Sport
- Be Part of It
-
-
International
International
Northumbria’s global footprint touches every continent across the world, through our global partnerships across 17 institutions in 10 countries, to our 277,000 strong alumni community and 150 recruitment partners – we prepare our students for the challenges of tomorrow. Discover more about how to join Northumbria’s global family or our partnerships.
View our Global Footprint-
Applying to Northumbria
- European Union
- Our London Campus
- Northumbria Pathway
- International Events
- Entry Requirements and Country Representatives
- Regional Offices
-
Northumbria Language Centre
- Faculty Requirements
- Acceptable English Requirements
- Pre-sessional English Language and Study Skills
- Academic Language Skills Programmes (ALS)
-
International Fees, Funding & Scholarships
- International Undergraduate Fees
- International Undergraduate Funding
- International Masters Fees
- International Masters Funding
- International Postgraduate Research Fees
- International Postgraduate Research Funding
- International Money Matters
-
Life at Northumbria
- International student support
- Careers
-
International Mobility
- Current Northumbria Students
- Incoming Exchange Students
-
-
Business
Business
The world is changing faster than ever before. The future is there to be won by organisations who find ways to turn today's possibilities into tomorrows competitive edge. In a connected world, collaboration can be the key to success.
More on our Business Services -
Research
Research
Northumbria is a research-rich, business-focused, professional university with a global reputation for academic quality. We conduct ground-breaking research that is responsive to the science & technology, health & well being, economic and social and arts & cultural needs for the communities
Discover more about our Research -
About Us
-
About Northumbria
- Our Strategy
- Our Staff
- Place and Partnerships
- Student Profiles
- Alumni Profiles
- Leadership & Governance
- Academic Departments
- University Services
- History of Northumbria
- Contact us
- Online Shop
-
-
Alumni
Alumni
Northumbria University is renowned for the calibre of its business-ready graduates. Our alumni network has over 244,000 graduates based in 178 countries worldwide in a range of sectors, our alumni are making a real impact on the world.
Our Alumni - Work For Us
What will I learn on this module?
Data Science concerns extracting information from data – in other words giving a voice to the data. Different analysts may have different purposes when analysing data – the intention may be to describe the information in the data, explain the relationships between parts of the data or use a subset of the data to predict the outcome of a variable of interest. For example, that variable could be whether a customer with a particular profile may buy an item of interest. Most companies collect data on their customers and are interested in how this data can be used to improve customer experience as well as profits. Depending on the intention, the approach taken by the analyst will differ and this module will cover the main tools for classification, clustering, association mining and outlier detection allowing you to analyse data with confidence.
By the end of the module, you should have developed an awareness of different approaches to analysing various forms of data and should have an ability to appraise which analytical techniques are appropriate. You will be able to perform the analysis and interpret the results correctly.
Outline Syllabus
Classification techniques that may include decision trees, support vector machines, linear discriminant techniques and logistic regression.
Clustering techniques including k-means clustering, apriori association mining, naïve Bayes and dimensionality reduction.
How will I learn on this module?
You will learn through a series of lectorials which combine formal lectures and hands on experience using computer software. Classes will be scheduled in our modern computer laboratories enabling you to apply the techniques presented in the lecture part of the session and, in this way, deepen your understanding of the material and develop your practical skills. This, in turn, develops your confidence to explore the subject area further as an independent learner outside of the classroom.
Formative feedback is available weekly in the classes as you get to grips with new techniques and perform analyses. In addition, we operate an open door policy where you can meet with your module tutor to seek further advice or help if required. Your ability to select appropriate techniques and use the appropriate approach to analyse different types of data is assessed in lab based examinations at the end of both semesters.
General feedback on assessments will be given in class followed by individual feedback . An opportunity to discuss work further will be available on an individual basis when work is returned and also through the open door policy.
How will I be supported academically on this module?
Direct contact with the teaching team during the lectorials will involve participation in both general class discussions as well as one to one discussions during the hands-on part of the lectorial. This gives you a chance to get immediate feedback pertinent to your particular needs in this session. Further feedback and discussion with the teaching team are also available at any time through our open door policy. In addition, all teaching materials, selected R scripts, and supplementary material (such as interesting articles) are available through the e-learning portal.
What will I be expected to read on this module?
All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.
What will I be expected to achieve?
Knowledge & Understanding:
1. Consider which method of analysis is most appropriate for a given set of data and the information required
2. Appraise a data analysis for its accuracy, robustness and suitability
Intellectual / Professional skills & abilities:
3. Evaluate the associations between elements of a set of data and summarise these relationships
4. Consider different classification techniques and select the most appropriate for analysis
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Critically appraise the use of data analysis techniques in various scenarios and determine which approach is relevant for selected data and analyse and summarise results
How will I be assessed?
SUMMATIVE
1. Lab based examination (50%) – 1,3,5
2. Lab based examination (50%) – 2,4,5
Summative assessment will be tak place as formal examination at the end of each semester.
FORMATIVE
Formative assessment will be available on a weekly basis in the lectorials through normal lecturer-student interactions, allowing them to extend, consolidate and evaluate their knowledge.
Formative feedback will be provided on student work and errors in understanding will be addressed reactively using individual discussion. Solutions for laboratory tasks will be provided after the students have attempted the questions, allowing students to receive feedback on the correctness of their solutions and to seek help if matters are still not clear.
Pre-requisite(s)
None
Co-requisite(s)
None
Module abstract
‘Data Science’ concerns extracting information from data using statistical and computational techniques. You will learn about the four main areas of data science and learn how the approach to analysing data depends on both the type of data and the aim of the analysis.
You will learn to answer questions such as ‘Is this new customer likely to buy?’ or ‘Which type of customer is this new customer most like?’. The module is ‘hands-on’ and is taught through a combination of lectures and tutorials. Formative feedback is available weekly in the classes as you get to grips with new cryptologic techniques and solve problems. Assessment is via lab-based exams (where you will analyse brand new data sets) each worth 50% of the module mark.
The module has been designed to give you an education in how data science techniques have developed recently as well as an ability to assess which technique is most appropriate for the data available and question posed.
Course info
UCAS Code G100
Credits 20
Level of Study Undergraduate
Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad
Department Mathematics, Physics and Electrical Engineering
Location City Campus, Northumbria University
City Newcastle
Start September 2025
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
Useful Links
Find out about our distinctive approach at
www.northumbria.ac.uk/exp
Admissions Terms and Conditions
northumbria.ac.uk/terms
Fees and Funding
northumbria.ac.uk/fees
Admissions Policy
northumbria.ac.uk/adpolicy
Admissions Complaints Policy
northumbria.ac.uk/complaints