Generating Insights Through Deeper Analytics
This project-based module aims to introduce you to a multiplicity of tools and methods to ask questions of data and test hypotheses within organizations. It will provide theoretical insights and hands-on practice into the possibilities presented by data analysis techniques and tools.
External Engagement: the module is co-delivered by SAP, one of the corporate partners of the one planet MBA
Employability: the module will offer an opportunity to acquire knowledge and key analytical skills for those pursuing careers in functions such as innovation management, strategy, sustainability, sales and marketing
Ethics and Corporate Responsibility: the module will consider ethical aspects of analytics
Full module specification
|Module title:||Generating Insights Through Deeper Analytics|
MBAM913 Generating Insights Through Analytics
MBAM900 Foundation Programme
|Duration of module:||
Duration (weeks) - term 2: |
4 days (plus 6 weeks study)
The module aims to introduce you to a variety of tools, analytical methods and data visualisation techniques to assess the relevance and quality of available data and help you test hypotheses within organizations to inform organisational strategy. While many of those tools and techniques are used to understand business drivers, profile customer groups and review organizational performance, they can also be used powerfully to drive organizationally supportive, typical (sales and marketing) and creative activities.
This project-based course will provide theoretical insights and hands-on practice into the possibilities presented by data analysis techniques and tools. Just as is typical in an organization, you will be expected to work as part of a team, working with an existing, available dataset in order to gain and present an actionable insight, using the techniques presented throughout the course.
ILO: Module-specific skills
- 1. demonstrate awareness of the key elements of the Analytics stack and their role from data storage, modelling, analysis, visualization and presentation
- 2. demonstrate understanding by using existing, typical and creative insights and concerns to set up a question/hypothesis within a real data context
- 3. understand and identify typical use cases for data analysis
- 4. critically evaluate the typical steps of data analysis project and how each step needs to be carried out
ILO: Discipline-specific skills
- 5. demonstrate understanding of the key features of typical data analysis software, including with hands-on practice.
- 6. demonstrate ability to combine structured and unstructured data insights into valid and valuable statistical inferences.
- 7. explain and evaluate more complex data analysis projects with specialist data scientists, involving deeper skillsets (Hadoop, R-programming, NoSQL etc)
ILO: Personal and key skills
- 8. demonstrate understanding by using a publically available dataset, or an organizational dataset to which you have access and permission to use, set up a question or hypothesis, which would provide significant insight, evaluate and present actionable results, using modern visualization techniques
- 9. demonstrate cognitive skills of critical and reflective thinking
- 10. demonstrate effective independent study and research skills
Introduction to the module.
The stack - The data analysis stack and process.
Working with datasets
Advanced Data visualization
Trends in analytics
Indicative learning resources - Basic reading
“UK Department of Health: Prescription for Disaster” (on ELE)
Aadhaar: India’s ‘Unique Identification’ System
Biesdorf S.; Court D.; Willmott P. (2013): “Big Data: What’s your plan?” McKinsey Quarterly http://www.mckinsey.com/insights/business_technology/big_data_whats_your_plan
McAfee A.; Brynjolfsson E. (2012): “Big Data: The Management Revolution” Harvard Business Review Vol 90 Issue 10 p60-68
Barton D.; Court D. (2012): “Making Advanced Analytics Work For You” Harvard Business Review Vol 90 Issue 10 p78-83
Laartz J.; Monnoyer E.; Scherdin A. (2003): “Designing IT for Business” McKinsey Quarterly Number 2 p77-84
Last revision date