Applied Business Analytics and Decision Making

The details
Essex Business School
Southend Campus
Undergraduate: Level 6
Sunday 15 January 2023
Friday 24 March 2023
17 August 2022


Requisites for this module



Key module for

BSC N111 Business Administration and Supply Chain Management,
BSC N112 Business Administration and Supply Chain Management (Including Placement Year),
BSC N113 Business Administration and Supply Chain Management (Including Year Abroad),
BSC N114 Business Administration and Supply Chain Management (including Foundation Year),
BSC N114CO Business Administration and Supply Chain Management (including Foundation Year)

Module description

This module will provide an overview of applications of business analytics for real world business problems.

Business examples and case studies will be used to explain the variety of decision making issues that can be solved using analytics to gain business intelligence. Useful business analytics software (like R, Python or similar) will be discussed in this module.

Module aims

The aim of this module is to:
1. provide an overview of various applications of business analytics, data mining and machine learning for solving business problems;
2. help students understand how these data analytics methods have been employed to gain business intelligence;
3. equip students with knowledge on the choice of appropriate business analytics software for a given application.

Module learning outcomes

On successful completion, students will be able:
A. To critically understand the breadth and importance of real-world applications of business analytics.
B. To critically analyse how business analytics methods have been used to gain business intelligence with specific examples from practice.
C. To critically understand how to choose one or more business analytics tool and software for solving specific real-world problems.

Module information

Indicative syllabus:

Introduction to business analytics and decision making
Data mining and machine learning
Multi-criteria decision making approaches
Application of advanced data analytics
Supervised learning (e.g., Neural Networks)
Text mining (e.g., sentimental analysis)
Introduction of business analytics tools or software (like Python, R, or similar)
Applications of these tools or software in decision making
Challenges of applying business analytics tools in practice

Learning and teaching methods

The following learning and teaching methods will inform the pedagogic structure of the course: lectures, case studies, class exercises, group work, and signposting to other resources and support. Students will be encouraged and required to refer to a wide range of resources covering textbooks and academic peer reviewed journal articles, to build an understanding of theoretical concepts and refer to online platforms to follow current trends and practices concerning the application of business analytics and decision making tools in practice. The lectures will be developed around key concepts as mentioned in the indicative module content and will use a range of examples and cases from practice to demonstrate the application of approaches and tools. The lectures will follow a weekly format of 2 hours (1 hour lecture and 1 hour seminar) per week for 10 weeks.


The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Written Exam  Individual Essay  27/03/2023  100% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Peng Xu, email:
Mr Peng Xu



External examiner

No external examiner information available for this module.
Available via Moodle
Of 4 hours, 4 (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), module, or event type.


Further information
Essex Business School

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