BM9719 – Forecasting and Predictive Analytics


Newcastle Business School



Module Code

Forecasting and Predictive Analytics

Assessment Instructions 

Task Description 

Data sets vary from one domain to another. In this coursework, you will select a dataset related to a real-world  problem that best suits your area of interest. There are a wide variety of websites that provide publicly available  datasets. A categorised list of datasets from GitHub can be found at https://github.com/caesar0301/awesome public-datasets. The UCI Data Repository at https://archive.ics.uci.edu/ml/index.php is another long-standing  source of benchmark datasets for data analysis research. Kaggle https://www.kaggle.com/datasets has  interesting real-world problems and datasets. 

You can either select a dataset from the above sources, or another dataset of your preference that is available  online. The dataset should be publicly available. Your assignments can include the forecasting and predictive  analytics using R and/or any other statistical software you are comfortable with (SPSS, SAS, Minitab  etc.). You have to complete the following stages in this assignment: 

  1. Select a real life data set as the business case you will work on. 
  2. Set the forecasting and predictive analytics goals for the selected case study.

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Assessment Brief – Newcastle Business School

  1. Data exploration and preparation: The nature of the dataset may dictate some data exploration and  preparation that can help inform the decision. 
  2. Identify and apply the proper techniques in the predictive modelling process to solve the business  problem. 
  3. Use appropriate visualisations for the results. 
  4. Critically evaluate and interpret the results of the predictive models and how they can support business  decision making. 
  5. Reflect on professional, ethical and legal issues in relation to the business case study.  Component 1 Deliverable – Contributes 40% to the Module Mark  Component 1 will assess learning outcomes LO 2, 3, and 4 

Deadline: Electronic copy of your presentation needs to be submitted on Blackboard by 12:00 noon time on Friday 28th April 2023; group presentations will be made in the seminar sessions in the 12th teaching week (w/c 1st May 2023). 

What to Hand In  

  • Online – Each member in the group will be required to submit an electronic copy of their  presentation that includes screenshots from their use of techniques appropriately labelled and  commented 

You need to present your group work, demonstrate your forecasting and predictive modelling process  and results during the seminar sessions on the 12th teaching week (w/c 1st May 2023).  

Component 2 – Contributes 60% to the Module Mark 

Component 2 will assess learning outcomes LO 1, 2, 3, 4 and 5 

Deadline: Monday 22/05/2023 before 12:00 noon 

What to Hand In  

  • A case study individual report maximum of 3000 words that must deliver findings and  recommendations based on their research which will include the forecasting and predictive analytics.  The report documents the process of the entire case study, including data set, problem, data preparation  and exploration, selected forecasting and prediction methods, critical evaluation and justification of  the methods and findings.  
  • Online – file in a pdf format via Turnitin on Blackboard  

Please note that for Component 2 Individual Report, you need to choose a data set which is different than the data set you used for Component 1 Group Presentation.  

The submission will be done electronically via Blackboard, all deliverables shall be labelled with project name,  your student name and university number. 

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Assessment Brief – Newcastle Business School

The report will be assessed on: 

understanding of business problem 

review of relevant literature 

predictive modelling development methodology 

justification of business decisions driven by the modelling results 

consideration of professional, ethical and legal issues 

The report could broadly include the following sections: 

  • Abstract 
  • Introduction (introducing the business case and the objectives of forecasting and predictive  analytics) 
  • Literature review of related work 
  • Data exploration and preparation 
  • Forecasting and predictive modelling process 
  • Results 
  • Discussion, Conclusions and Future Work 
  • References 

These are generic section titles, which you may adapt appropriately to the case study that is investigated. You  may include sections describing modifications of methods or developments that are novel and specific to your  work.  

Late submission of work  

Where coursework is submitted without approval, after the published hand-in deadline, the following  penalties will apply. 

For coursework submitted up to 1 working day (24 hours) after the published hand-in deadline without  approval, 10% of the total marks available for the assessment (i.e.100%) shall be deducted from the  assessment mark. 

For clarity: a late piece of work that would have scored 65%, 55% or 45% had it been handed in on time  will be awarded 55%, 45% or 35% respectively as 10% of the total available marks will have been deducted. 

The Penalty does not apply to Pass/Fail Modules, i.e. there will be no penalty for late submission if  assessments on Pass/Fail are submitted up to 1 working day (24 hours) after the published hand-in deadline. 

Coursework submitted more than 1 working day (24 hours) after the published hand-in deadline without  approval will be regarded as not having been completed. A mark of zero will be awarded for the  assessment and the module will be failed, irrespective of the overall module mark. 

For clarity: if the original hand-in time on working day A is 12noon the 24 hour late submission allowance  will end at 12noon on working day B. 

These provisions apply to all assessments, including those assessed on a Pass/Fail basis. 

Word limits and penalties  

If the assignment is within +10% of the stated word limit no penalty will apply.

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Assessment Brief – Newcastle Business School

The word count is to be declared on the front page of your assignment and the assignment cover sheet. The  word count does not include: 

  • Title and  

Contents page Reference list AppendicesAppropriate tables,  

  • Quotes from  

figures and  

  • Glossary Bibliography illustrations 

interviews and  

focus groups. 

Please note, in text citations [e.g. (Smith, 2011)] and direct secondary quotations [e.g. “dib-dab nonsense  analysis” (Smith, 2011 p.123)] are INCLUDED in the word count. 

If this word count is falsified, students are reminded that under ARTA this will be regarded as academic  misconduct. 

If the word limit of the full assignment exceeds the +10% limit, 10% of the mark provisionally awarded to the  assignment will be deducted. For example: if the assignment is worth 70 marks but is above the word limit by  more than 10%, a penalty of 7 marks will be imposed, giving a final mark of 63. 

Academic Misconduct 

The Assessment Regulations for Taught Awards (ARTA) contain the Regulations and procedures applying  to cheating, plagiarism and other forms of academic misconduct

The full policy is available at: http://www.northumbria.ac.uk/sd/central/ar/qualitysupport/asspolicies/ 

You are reminded that plagiarism, collusion and other forms of academic misconduct as referred to in the  Academic Misconduct procedure of the assessment regulations are taken very seriously by Newcastle  Business School. Assignments in which evidence of plagiarism or other forms of academic misconduct is  found may receive a mark of zero. 

Mapping to Programme Goals and Objectives 

This assessment will contribute directly to the following programme goals and objectives. 

Knowledge & Understanding:  

  • Acquire, interpret and apply specialist functional knowledge in relation to their programme of study  [PLO 7.4.3] 

Intellectual / Professional skills & abilities

  • Demonstrate competence in contemporary analytical and ICT applications [PLO 7.1.3] Analyse and communicate complex issues effectively [PLO 7.3.1] 

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA): Reflect on their own ethical values [PLO 7.2.2]