Applied Business Analytics and Decision Making

The details
Essex Business School
Southend Campus
Undergraduate: Level 6
Monday 13 January 2025
Friday 21 March 2025
25 June 2024


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),
BSC N130 Business and Analytics,
BSC N131 Business and Analytics (including Placement Year),
BSC N132 Business and Analytics (including Year Abroad),
BSC N133 Business and Analytics (including Foundation Year),
BSC N133CO Business and Analytics (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 aims of this module are:

  • To provide an overview of various applications of business analytics, data mining and machine learning for solving business problems.

  • To help students understand how these data analytics methods have been employed to gain business intelligence.

  • To equip students with knowledge on the choice of appropriate business analytics software for a given application.

Module learning outcomes

By the end of this module, students will be expected to be able to:

  1. Critically understand the breadth and importance of real-world applications of business analytics.

  2. Critically analyse how business analytics methods have been used to gain business intelligence with specific examples from practice.

  3. 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 advanced business analytics and data mining

  • Advanced data analytics tools: Python programming

  • Machine learning: Classification methods

  • Application of supervised machine learning

  • Machine learning: Clustering methods

  • Application of unsupervised machine learning

  • Advanced data analytics: Deep learning and neural networks

  • Advanced data analytics: Text mining and analytics

  • Application of advanced data analytics

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Learning and teaching methods

This module will be delivered via:

  • One 1-hour lecture per week;
  • One 1-hour seminar per week; together with
  • Case studies; class exercises; group work; signposting to additional 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 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
Coursework   Individual Essay    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:
Dr Peng Xu



External examiner

No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.


Further information
Essex Business School

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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