Business Analytics for Managers and Entrepreneurs
Essex Business School
Postgraduate: Level 7
Sunday 17 January 2021
Friday 26 March 2021
05 June 2020
Requisites for this module
MSC N11112 Business Analytics,
MSC N111JS Business Analytics,
MSC N21612 International Logistics and Supply Chain Management
Data analytics represents a massive opportunity for business leaders, managers and entrepreneurs alike in modern globalised economies. In today's Information and Communication Technology (ICT) led business world, the nature of data has moved from static spreadsheets to being highly interactive dynamic representations from multiple sources, which provide rich insights on markets, firms, competitors, consumers and networks.
This module shows how data analytics is a crucial skill to have in today's business world, and will illustrate this with real world examples where businesses have harnessed the power to solve critical problems. It then aims to equip students with a wide variety of data mining, visual and analytical techniques which can be applied to data generated from different markets, businesses, and business functions.
The module aims to provide the students with the following:
1. An appreciation of how business analytics can equip managers and entrepreneurs with critical tools and technologies, which would enable them to use freely available information for their own benefit
2. An awareness of what tools and technologies exist which can be freely used to harness the power of analytics on data sets of varying sizes and content
3. Essential analytical skills of handling, analysing and manipulating data in order to generate insights about businesses, markets, consumers and competitors
4. To learn simple but powerful visualisation techniques which can reduce the complexity in large data sets
5. Using simulations to explore scenarios and optimise decision making in different business functions and markets
On successful completion of this module students should be able to:
1. Obtain a critical understanding of principal theoretical approaches to analysing large data sets available in the modern business world
2. Develop key analytical skills of analysing these datasets using modern computational tools and techniques from a practitioners’ point of view
3. Gain overall perspective on the importance of data analysis and other quantitative techniques in both strategic and tactical decision making faced by managers and entrepreneurs in the modern business world
4. Evaluate typical data related questions faced by managers and entrepreneurs, and be able to devise analytical strategies to tackle these problems
5. Critically differentiate between the questions which can be tackled using quantitative methods and large data sets and which cannot be answered using the same, but which requires a mixed approach
No additional information available.
The following learning and teaching methods will inform the pedagogic process of the course: Lectures; Seminars
The lectures will be developed around the key theoretical concepts of data analysis, alternative methods and multiple toolkits. They will also address the issue of multiplicity of analytical requirements when it concerns actual practical applications, and hence will provide a rounded view of how to choose between different methodologies, based on the nature of the question posed as well as nature and source of available data.
The seminars will focus on practical aspects of using the material taught in lectures for solving real life problems. They will use freely available datasets in addition to established software toolkits, both advanced and basic, in order to develop the ability to query the datasets using alternative approaches, understanding the results of such queries and evaluating them in the context of the business problem.
This module does not appear to have a published bibliography.
Assessment items, weightings and deadlines
|Coursework / exam
Module supervisor and teaching staff
Dr Niraj Kumar, email: email@example.com.
Dr Niraj Kumar
Dr Ping Zheng
Canterbury Christ Church University
Available via Moodle
Of 21 hours, 21 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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