BM9616 - Predictive Analytics

What will I learn on this module?

In this module, you will develop the knowledge and skills in applying a variety of quantitative data analysis techniques to analyse data sets, identify the patterns in the data, and predict future trends to support informed decision making in business contexts.
You will be introduced to predictive analytics using appropriate analytical tools, and your learning will cover a range of techniques to perform predictive analytics. To be more specific, this module involves the following fundamental topics and techniques:
• Relationship analysis – applying correlation, linear and logistic regression analysis (and other techniques) to characterise relationships among business variables, identify patterns in data and predict future trends.
• Forecasting models on time series data – analysing trend and other components on time-based historical data, developing an understanding of various approaches in business forecasting and exploring their use on data with the language R and other software tools such as SPSS;
• Classification and clustering – developing skills to process and analyse complex data sets by using machine learning techniques such as classification trees, clustering, etc.

Working with these analysis tools, you will learn to develop confidence in dealing with a wide range of data sets. You will become familiar with the role of predictive analytics techniques to predict what might happen in the future which would help set realistic goals, perform effective planning, and avoid risks in business settings.

How will I learn on this module?

The one-hour weekly lectures will provide you with a theoretical underpinning for your learning, supported by two-hour weekly IT workshops which will give you an opportunity to practice the various analytical techniques. The lectures cover various theoretical concepts relating to predictive analytics supported through reference and demonstration of appropriate IT applications. The workshops allow you to build up a proficiency in the use of contemporary statistical analysis tools, such as R, SPSS, etc., and the necessary skills of interpretation and communication of findings. You will be able to follow up on these lectures and IT workshops through a one-hour weekly webinar with the members of the teaching team and fellow students to reinforce both the practical and theoretical learning, receive formative feedback and engage in question-and-answer sessions on the module materials and assessment brief.

The module has a supporting reading list that provides you with an opportunity to see how the various analytical techniques are applied to further managerial and research-based problems, as well as reference to a core text that will support your learning with further reading and practical examples. There will be additional resources located on the module's e-learning portal that will permit you to undertake further independent learning.

Independent learning time is set aside for learning activities, self-identified by you, to gain a deeper and broader knowledge of the subject. You may complete the review exercises, work with the electronic support tools (such as recordings of IT applications) or undertake further reading. The group work arrangement places the students at the heart of the activity by collaboratively working on a data set coordinating each individual’s contributions and engaging in the group presentation.

How will I be supported academically on this module?

Support will be provided to you by the module tutor and members of academic staff teaching in the module and providing the lecture input. This team of academic staff are allocated IT workshop groups of about 20 students, which provides closer, more personal academic support. These IT workshop groups are typically based on study programme cohorts, so you will be taught here alongside fellow members from your specific programme. The final aspect of the direct contact support is a 1-hour weekly webinar, where students can link with the module tutor and other members of the teaching team to reinforce both the practical and theoretical learning, receive formative feedback and engage in question-and-answer sessions on the module materials and assessment brief.

Furthermore, 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.

Your module is supported by an e-learning portal, which hosts lecture materials, IT workshops exercises and data files, alongside assessment details and various support facilities such as recordings of certain lectures and IT applications, alongside other electronic support facilities such as the module reading list.

You will have a wide-ranging electronic reading list that comprises of various textbooks whose contexts will reinforce the lecture and IT workshop inputs, alongside academic reports, conference papers and journal articles that showcase the application of various predictive analytics techniques presented in the module.

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 by using the statistical tools (R, SPSS, etc.); solve 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):
• Gain awareness on the ethical considerations on data and analysis [MLO5]

How will I be assessed?

Formative assessment will take place through group work, individual data analysis and interpretation. Criteria will be provided to enable you to understand what is expected and how you will be assessed on your performance. Formative feedback will be provided throughout the module, particularly in relation to workshop tasks. The weekly webinar sessions are a further channel for formative feedback on both the theoretical and practical aspects of the module and on the tasks that underpin the summative assessment.

You should, however, be aware that formative feedback can, and will, occur in any communication with an academic tutor.

The assessment of this module will comprise two components; a group presentation and demonstration of analysis and findings (weighted 30% and covered MLO2, MLO3 and MLO4).and a 2,500 word summative report (weighted 70% and covered MLO1, MLO2, MLO3, MLO4 and MLO5).





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: correlation analysis, linear and logistic regression, classification, clustering, and appropriate time series analysis and forecasting methods. Beginning with basic models for revealing and establishing relationships, you will learn to apply increasingly sophisticated modelling 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 will 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 statistical analysis tools (e.g. R and SPSS) to support business decision making, but you will also enhance your abilities and competence across a range of employability skills, including; team management and coordination, time management and communication.

Course info

UCAS Code N650

Credits 20

Level of Study Undergraduate

Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad

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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