MQM2IL06-Data Analysis

Module Provider: Leadership, Organisations and Behaviour
Number of credits: 0 [0 ECTS credits]
Terms in which taught: Autumn / Spring / Summer module
Non-modular pre-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Sinem Bulkan

Type of module:

Summary module description:

This module forms part of the Henley Executive Diploma in Managing Business Transformation (Improvement Leader Apprenticeship), and as such, sets out to provide the knowledge, skills and behaviours required by Improvement Leaders/Change Managers/Business Transformation managers in today’s world, in relation to Data Analysis. The module is designed to meet the learning outcomes of the Improvement Leader Apprenticeship Standard. 

The module covers key data analysis and statistical methods, statistical and graphical analysis, and statistical process control to build the organisation’s knowledge and skills and make recommendations for improvement that extend the capabilities of the organisation. It aims to develop the learner’s skills in the use of systematic decision-making tools (statistical process control) through the implementation of statistical-based techniques to monitor and control a process to advance the quality or uniformity of the output of a process.  

The module largely focusses on developing the skills of the learner in data analysis especially in terms of assessing the organisation’s approach to these data analyses, guiding others on the completion of these analyses, and making recommendations to drive sustained improvement strategy. Additionally, the module covers the use of Minitab for graphical and statistical analysis.  

Key behavioural skills – professionalism, and strategic thinking – are strongly outlined throughout the module. The learner therefore gains knowledge and understanding of how to plan and implement data and statistical analysis at unit/business/corporate level focusing not just on techniques and tools, but also on sustainable behavioural outcomes. 


The module aims to: 

  • Develop knowledge and understanding of graphical and statistical analyses, investigating and evaluating the measurement and analysis approaches. 

  • Develop knowledge and understanding of the implementation of multiple regression or binary regression analysis and guiding others on the completion of the regression analysis. 

  • Develop knowledge and understanding of data acquisition and data-driven decision making.  

  • Develop the skills to accurately assess and provide constructive feedback on graphical and statistical analysis conducted by others and make recommendations. 

  • Develop the skills to make recommendations on how an organisation can drive sustained improvement through the application of statistical process control. 

  • Develop knowledge and understanding of the use of Minitab for graphical and statistical analysis. 

  • Develop an awareness of key behavioural skills needed: professionalism, and strategic thinking. 

Assessable learning outcomes:

By the end of the module, it is expected that programme members may be able to demonstrate their ability in the following areas: 

Knowledge and Understanding: 

K8: Data Analysis – Statistical Methods 

  • Regression (multiple & binary logistic), forecasting and queuing theory 

  • To complete a multiple regression or binary logistic regression analysis study and draw accurate conclusions and recommendations. 

  • To guide others on the completion of regression analysis. 

  • To promote the principles and benefits of statistical modelling to the wider organisation.  


S15: Data Acquisition for Analysis  

  • To assess and provide constructive feedback on data acquisition conducted by others in terms of tool selection and application, conclusions and recommendations. 

  • To build the organisation’s knowledge and skills in terms of data-driven decision-making. 

S16: Statistics and Graphical Analysis 

  • To assess and provide constructive feedback on graphical and statistical analysis conducted by others in terms of tool selection and application, conclusions and recommendations. 

  • To communicate opportunities for robust application of basic data analysis methods and engage others to extend/embed the application of data-driven approaches. 

  • To investigate and evaluate measurement and analysis approaches that extend the capabilities of the organisation. 

  • To establish strategies for gathering and analysing life-cycle data in the context of a key product, process or service. 

S22: Data Analysis – Statistical Process Control 

  • To assess the organisation’s approach to ongoing process control and make recommendations for improvement with reference to the application of Statistical Process Control. 

  • To build the organisation’s knowledge and skills in terms of ongoing process control with reference to Statistical Process Control. 


B3: Demonstrate personal resilience. Challenge, influence & engage seniors. (Professionalism) 


B4: Drive future thinking for themselves and others.?Actively seek out new ideas, opportunities methods and tools. Build a knowledge and best practice sharing network. (Strategic Thinking) 

Additional outcomes:

Use of Minitab or Equivalent tool (Graphs and Quality Tools and the Stat Menu in Minitab). 

Virtual Tools 

Outline content:

The module covers the following topics: 

  • Regression Analysis (Multiple & Binary Logistic) 

  • Forecasting and Queueing Theory 

  • Principles and Benefits of Statistical Modelling 

  • Data Acquisition for Analysis 

  • Data-driven decision-making 

  • Statistical and Graphical Analysis 

  • Statistical Process Control (Objectives, Selection of variables, Rational Subgrouping, Control Chart Selection, Control Chart Analysis) 

  • Use of Minitab or Equivalent tool (Graphs and Quality Tools and the Stat Menu in Minitab) 

  • Virtual Tools 

-The topics are specifically linked to the behavioural skills (Professionalism, strategic thinking). 

-The content should be mapped against the OFSTED requirements (British values / Safeguarding / Prevent / Maths / English) and Professional Practice Module.  

-The content should acknowledge limitations and challenges to the approaches discussed within the module content. 

Global context:

The module is taught with reference to the global context of the learners’ organisations and their experiences. 

Brief description of teaching and learning methods:

Teaching and learning takes place through a blended learning approach. The teaching and learning methods comprise a combination of self-study via a range of online materials on the Canvas learning platform, face-to-face workshops with Academic Faculty and Learning Coaches, and interaction with a Learning Coach (face-to-face and online) who supports the cohort throughout the module.  Each person participates in a facilitated Action Learning sets either individually or in teams in week 4 o f the module. 

Materials on Canvas include content on-screen, videos, PowerPoint presentations, journal articles, book chapters, practical activities, e-portfolio, and reflection points.  

Contact hours:
  Autumn Spring Summer
Seminars 4
Practicals classes and workshops 7
Work-based learning 64
Guided independent study:      
    Wider reading (independent) 24
    Wider reading (directed) 23
    Advance preparation for classes 3
    Essay preparation 25
Total hours by term 150 0 0
Total hours for module 150

Summative Assessment Methods:
Method Percentage

Summative assessment- Examinations:


Summative assessment- Coursework and in-class tests:


Formative assessment methods:

One 1500-word Work-Based Project, for which formative feedback will be provided.  

Penalties for late submission:

There is no penalty for late submission. However, if learners are at risk of missing the deadline, they are asked to submit an ECF requesting a 14-day extension.  

Assessment requirements for a pass:

Evaluation of the work-based project leads to a decision of ‘Proceed’ or ‘Revise’.  In order to gain a ‘Proceed’ the learner must satisfactorily meet the assignment brief requirements. 

Any learning outcomes not achieved will be highlighted for the learner, so that it is clear that these learning outcomes should be addressed prior to reaching Gateway for the End Point Assessment (EPA).   

Learners may revise their project as many times as necessary, as they progress through the programme.  However, only one resubmission will be evaluated, and feedback provided by the Learning Coach (see reassessment arrangements, below).

Reassessment arrangements:

The revised work-based project should be resubmitted by the deadline of the notification of the ‘revise’ decision.  This resubmission will be evaluated by the Learning Coach, feedback will be provided and an indication of whether the revised project has met the ‘Proceed’ criteria.  No further resubmissions will be evaluated by the Learning Coach.  The student will need to address any remaining gaps regarding achievement of learning outcomes prior to reaching Gateway for the L6 Improvement Leader End Point Assessment.  Students should discuss this with their Learning Coach at their planned review meetings. 

Additional Costs (specified where applicable):

Travel, accommodation, and subsistence  - Expenses when attending workshops (in the case of a workshop taking place at Greenlands or offsite.) 

Last updated: 22 September 2022


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