BM9616 - Predictive Analytics

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.

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.

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)

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]

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).

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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.