MQM2BDA-Business Data Analytics

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Autumn term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Dr Markos Kyritsis


Type of module:

Summary module description:

Data-driven recommendations are becoming increasingly popular amongst organisations; quickly replacing qualitative assessments that were until recently based on experience and tacit knowledge. A good understanding of data analytics has, therefore, become a fundamental skill for anyone looking to work with organisations that plan to make strategic use of their data assets. In this module students will be introduced to key concepts related to descriptive and predictive analytics, and will become adept at managing, summarising, and analysing data. Furthermore, students will gain experience with building predictive models that can lead to data-driven solutions to commercial problems.



Ultimately, the aim of this course is to provide students with the skills to (a) explore univariate and multivariate data sets and describe data architecture and structures using modelling and visualisation techniques, (b) formulate analysis questions and hypotheses and use statistical methods to support or reject these hypotheses, (c) develop statistical models and machine learning algorithms that support data-driven business decisions, (d) critically evaluate the results of analyses and successfully summarise them graphically using appropriate visualisation techniques.


 To satisfy this general aim, students will acquire key knowledge and skills in:

  • Exploring and visualising complex data sets

  • Applying principles of data driven analysis using inferential statistics and statistical modelling

  • Developing and comparing predictive models

  • Forming data driven decisions to solve commercial problems

Assessable learning outcomes:

On completion of this module, the student should be able to:

  1. Formulate analysis questions and hypotheses, and apply appropriate statistical methods to either support or reject the hypotheses

  2. Argue in favour of a suitable methodology for a data-driven analysis given the specific nature of the data set and analysis question(s).

  3. Describe how key algorithms and models are applied in developing analytical solutions, as well as how th ese solutions can be beneficial to organisations

  4. Discuss when to use parametric and non-parametric tests in order to conduct high-quality complex investigations. 

  5. Discuss how machine learning algorithms and models are applied in developing analytical solutions to commercial problems

  6. Reduce complexity of large data sets by applying dimension reduction techniques, and present the results in a way that supports human understanding of complex data sets

  7. Summarise data using visualisation techniques, and discuss how this approach reduces complexity of information on which decisions can be based

  8. Partition data sets and use both regression modelling and machine learning to generate data-driven solutions to commercial problems. 

  9. Evaluate and compare fitted models and select the most appropriate model based on its performance

Additional outcomes:

The student should:

  • Become familiar with the industry standard data analytics and visualisation tools

  • Become familiar with software development tools and approaches surrounding data analytics

  • Become adept at developing scripts using one of the industry standard analytic tools (e.g., R, Python, SATA, etc.)

Outline content:

The key themes of the module are:

  1. Cleaning, summarising and visualising data sets

  2. Inferring results from a sample to the population using parametric tests

  3. Inferring results from a sample to the population using non-parametric tests

  4. Reducing dimensionality of data sets

  5. Building Regression models and evaluating their performance

  6. Training Decision Trees and Random Forests and evaluati ng their performance

  7. Documenting the results of the analysis and developing data-driven solutions

Brief description of teaching and learning methods:

This module will be taught in a blended learning approach, which mostly includes directed self-study, undirected self-study, and workshops. It assumes no prior knowledge or experience in statistics, therefore students are expected to do a fair amount of wider reading. Data sets related to business problems will be provided as ‘case studies’ to individual students, who will then have to apply everything they learned to form data-driven recommendations. The coding and analysis will be documented and submitted as part of a report that is worth 100% of their grade.


Contact hours:
  Autumn Spring Summer
Practicals classes and workshops 14
Work-based learning 90
Guided independent study:      
    Wider reading (independent) 20
    Wider reading (directed) 27
    Advance preparation for classes 5
    Completion of formative assessment tasks 7
    Essay preparation 32
    Reflection 5
Total hours by term 0 200 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Report 100

Summative assessment- Examinations:


Summative assessment- Coursework and in-class tests:

Number and length of assignments and in-class tests, and if available, the submission date for each assignment (expressed as a week of a specific Term):

Submission of an individual report of 3000 words comprising the analysis, model building, scripts, and recommendations for addressing the business question using a data-driven approach. The source code for the scripts is not part of the word count.

Formative assessment methods:

Formative Assessment Methods (Work which provides opportunities to improve performance (e.g. through feedback provided) but which does not necessarily always contribute towards the overall module mark):

Students will be given feedback on the progress of their individual project through tutorials and practical sessions. Online quizzes will be made available to help assess students’ understanding of the subject. These are for their own benefit and will be marked automatically. The grade of formative assessment will not contribute towards the overall module mark. 

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy “Penalties for late submission for Postgraduate Flexible programmes”, which can be found here:
The Support Centres will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

50% in coursework

Reassessment arrangements:

Resubmission of coursework report

Additional Costs (specified where applicable):

Cost Amount
Required text books: Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications. Cost: £55 for paperback version from publisher (UK page). 1


Last updated: 8 December 2020


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