Business Analytics module (BU52018)
Learn the basic statistical methods and quantitative skills required to use data to inform business decisions.
This module introduces students to various statistical methods for decision-making and making informed business decisions.
The module will introduce Probability Theory, Estimation, and Statistical Inference and Regression Analysis.
Upon completing the module, students should be able to apply simple statistical analysis methods, build and estimate the parameters.
They should be able to interpret the results of stochastic models and discuss these models’ more general applicability.
What you will learn
The module will cover the following topics:
- Probability: Definition, theory, and examples
- Random variables and distributions
- Statistical inference
- Hypothesis testing
- Linear regression
By the end of this module, you will be able to:
- understand statistical concepts and principles relevant to business decision making
- apply the verbal, graphical, and mathematical representations of statistical and other quantitative techniques
- apply standard quantitative methods to business topics in a logical manner
- specify and quantify particular business relationships using appropriate techniques
- employ key concepts to structure problems in a variety of decision-making contexts
- use simple estimation techniques and conduct formal hypothesis testing to inform business decisions
- conceptualise business problems in a tractable form
- solve both abstract and practical problems using a variety of methods
Assignments / assessment
The assignments comprise the following:
- Class test: 40%
- Exam: 60%
Teaching methods / timetable
This module is taught using a mix of traditional lectures and applied tutorials. The material will be covered in lectures and reinforced with examples/applications in the tutorials.
The lectures and tutorials are carried out in the first five weeks of the semester (four-hour lectures and two-hour tutorials per week):
This module is available on following courses: