KL5005 - Statistical Modelling and Data Visualisation

What will I learn on this module?

This module will provide you with the fundamental tools to identify appropriate exploratory analysis techniques to uncover hidden patterns and unknown correlations in large data sets. You will be able to assess the strength of statistical evidence of the revealed patterns/correlations. You will also develop appropriate technique to visualise data/outputs, implement suitable analytical methods for big data and critically assess the suitability of the chosen analytical technique.
You will have the opportunity to analyse and visualise data for tackling real-life problems. You will work individually and in group and have the opportunity to critically appraise both your own work and the work of others.

The module will include topics such as:
? Exploratory analysis of big data;
? Data visualisation;
? Data manipulation (e.g. dealing with missing values, detecting outliers values, data transformation);
? Univariate statistical methods (e.g. simple linear regression, residual analysis);
? Techniques for predictive data mining (e.g. methods for binary/logistic classification);
? A suite of appropriate computer packages (including R) will be used.

How will I learn on this module?

You will learn through a series of formal sessions taking place in computer labs. Main concepts with suitable applications/examples are introduced during the teaching sessions. Computer labs allow you to gain experience in applying concepts introduced in lectures through working on practical questions. All the computer labs will be scheduled in our modern computer laboratories, enabling you to apply the techniques and 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 the classroom.

Formative feedback is available weekly in the classes as you get to grips with new techniques and solve problems. 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, perform and critique techniques to solve practical problems is assessed in via a formal examination.

General feedback on student progression will be given in class and formal individual feedback will be written on scripts. 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 formal sessions will involve participation in both general class discussions as well as one to one discussions during the hands-on part of the formal sessions. 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 and supplementary material (such as relevant journal articles and news) 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. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

What will I be expected to achieve?

Knowledge & Understanding:

1. Implement suitable techniques for data analysis and visualisation;
2. Utilise information revealed from data for tackling real-life problems.

Intellectual / Professional skills & abilities:

3. Perform appropriate exploratory analysis to uncover hidden patterns and unknown correlations and evaluate the strength of statistical evidence of the revealed patterns/correlations;
4. Construct and interpret appropriate graphics to visualise data/outputs;

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

5. Understand and apply appropriate statistical methodologies to analyse problems in the real world.

How will I be assessed?

Lab-based examination: 50% (Semester 1)
Learning outcomes: 1,4
Group work (report) 30% + presentation 20% (Semester 2)
Learning outcomes: 2,3,5
Lab-based examination at the end of semester 1, followed by a group work in the form of a project to be carried out throughout semester 2. The group will produce a collective report and a presentation to which each group member contributes individually.

Formative assessment will be available on a weekly basis in the formal sessions 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.





Module abstract

The module will provide an introduction to Data Science and is designed to equip students with the knowledge, skills and confidence to examine and analyse big data arising from the real world in a variety of application areas. The module will give students an introduction to up-to-date techniques in data visualisation and data cleaning through a hands-on experience using a leading software language used in Data Science. The first part of the module will have a primary focus on effective data visualisation, providing the skills to summarise large data sets, diagnose problems with the data quality, and deal with such issues. The material will build upon key statistical methodology learnt in previous years. The second part of the module will consist of work in groups under supervision. The groups will work on real-life data sets, giving you the opportunity to work in a team and apply the knowledge gained in the first part of the module. The module will be delivered using a combination of lectures and computational-based seminars. For assessment, a coursework is chosen in order to examine the student’s capability to apply the analytical techniques to real world situations. A wide range of real-life problems will be used to motivate the subject matter.

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 2024 or September 2025

Fee Information

Module Information

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.


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