BM9719 - Forecasting and Predictive Analytics

What will I learn on this module?

This module aims at educating you in the field of forecasting and predictive analytics to respond to the job market needs using a variety of methodologies. Your journey shall be a quest to distinguish the "true" signal from a universe of "noise" through the lenses of forecasting and predictive analytics. To be more specific, this module covers the typical methodological steps of a prediction exercise, statistical modelling, and artificial intelligence methodologies for prediction via applications in different business settings. This modules teaches you fundamental techniques used for predictive analytics: regression, classification, clustering, Bayesian and other machine learning approaches and models. You will learn how to perform forecasting using time-based data to predict future values from a model. You will get practice with classification and use various techniques for clustering and linear regression to solve common business problems; as well as learn techniques for assessing the effectiveness of your solutions.

How will I learn on this module?

You will be supported by a teaching and learning plan (TLP) which outlines the formal sessions (lectures and IT workshops), together with tutor-directed study and independent study/reading. You will be provided each week by a one-hour lecture which covers various theoretical concepts relating to forcasting and predictive analytics supported through reference and demonstration of appropriate IT applications. These lectures will be followed by a supporting 2-hour IT workshop where you will gain practical, hands-on experience of using contemporary statistical analysis tool, R. Directed learning will centre upon a range of activities including your pre-reading, your preparation for interactive and workshop based activities and use of the discussion board on the e-learning platform. Independent learning will centre upon you identifying and pursuing areas of interest in relation to forcasting and predictive analytics, by providing deeper/broader knowledge and understanding of the subject through a range of learning activities that will include extended reading, reflection, research etc. Critical reflection on knowledge, experience and practice underpins the learning and teaching philosophy along with the explicit development of competence. Moreover, this module emphasises enquiry-based learning and will involve you being an active participant in your learning to develop a research question based on the case studies in the various business situations. The group work arrangement is centred on the students at the heart of the activity by guiding you to ask well researched questions and share your findings with peers. Furthermore, research tutored approach will enhance your research rich learning experience through the module delivery by the module tutor and various research subject experts.

How will I be supported academically on this module?

The university is well-placed to support you in learning and research with excellent library and teaching facilities, as well as access to relevant and up-to date statistical analysis software. You will have access to industrial standard statistical analysis software supported by additional Blackboard materials and access to Northumbria University's library and databases.

Academic content and delivery will be enhanced through opportunity for guest practitioner expert input.

In addition, you will be supported at a programme level by an induction programme to introduce you to the University and the Masters' programme. Each of you will also be assigned a personal tutor to provide pastoral support and guidance throughout the programme. Further support is provided to you through the personal tutor sessions embedded into the Leadership and Management Development module, and a module teaching and learning plan detailing the delivery structure and University requirements.

You will be supported by an academic teaching team led by a designated module tutor. The module will have e-learning portal including all the materials related to this course supported by BB Ultra in addition to an electronic reading list.

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:
(Reading List service online guide for academic staff this containing contact details for the Reading List team –

What will I be expected to achieve?

Knowledge & Understanding:
• Develop a critical understanding on the business problem and the predictive analytics goals; describe the key steps, identify and apply the proper techniques in the predictive modelling process to solve the business problem [MLO1]

• Critically evaluate and interpret the results of the predictive models and how they can help in solving the business problem [MLO2]

Intellectual / Professional skills & abilities:
• Apply state-of-art predictive modelling in the statistical analysis language, R; sovle predictive modelling case to support decision making [MLO3]

• Develop academic report writing and presentation skills [MLO4]

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
• Reflect on your own ethical values [MLO5]

How will I be assessed?

Formative assessment will take place through group work, assignment discussion and reflection, discussion board activity on the e-learning platform, workshop-based activity, and theory/practice related discussions. Criteria will be provided to enable you to understand what is expected and how you will be assessed on your performance. You are required to demonstrate self-reflection and reflective practice where appropriate. Formative feedback will be provided throughout the module, particularly in relation to workshop tasks. You should, however be aware that formative feedback can, and will, occur in any communication with an academic tutor.

The summative assessment of this module will comprise two components; a 3,000 word report (weighted 60% and covered MLO1, MLO2, MLO3, MLO4 and MLO5) and a group presentation and demonstratin of work result (weighted 40% and covered MLO2, MLO3 and MLO4).





Module abstract

The primary objective of this module is to introduce you to various techniques available to extract useful insights from the large volumes of business data. This module will teach you fundamental techniques used for predictive analytics: regression, classification, clustering, Bayesian and other machine learning approaches and models. Beginning with basic models for revealing and establishing relationships, you will learn to apply increasingly sophisticated modeling techniques for practical data analysis, as well as commonly encountered problems so you can determine the fit and usefulness for prediction of your models, and apply them to typical business problems. As you develop your understanding of applied predictive analytics, you'll learn how to perform basic forecasting using time-based data to predict future values from a model. At the end of the module, you will not only see the substantial opportunities that exist in the business analytics realm, but also learn techniques that allow you to exploit these opportunities. Through undertaking this module you will not only develop a depth of knowledge of forecasting and predictive analytics and substantial practical skill of using R statistical analysis tool to support business decision making, but you will also enhance your abilities and competence across a range of graduate employability skills, including; project planning and management, time management, communication and negotiation.

Course info

Credits 20

Level of Study Postgraduate

Mode of Study 1 year Full Time
1 other options available

Department Newcastle Business School

Location City Campus, Northumbria University

City Newcastle

Start September 2024

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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