This module is not currently running in 2024 to 2025.
The aim of this module is to enable students to apply basic statistical inference methods for tackling real-world business questions and equip them with basic knowledge of the Python statistical programming package.
The module covers two indicative areas:
1. Business Statistics: Students will learn about descriptive analysis of quantitative data, focusing mainly on how to effectively summarise data, and inferential analysis of quantitative data, which includes identifying key properties of a given dataset, deriving point and interval estimates, hypothesis testing, correlation analysis, and simple linear regression.
2. Python programming package: This will cover the Python programming language and introduce students to basic and more advanced concepts within Python, as well as how to use Python for performing statistical data analyses.
Private Study: 116
Contact Hours: 34
Total: 150
Main assessment methods:
VLE Test 1 (45 minutes): 20%
VLE Test 2 (45 minutes): 20%
Examination (2 hours): 60%
Reassessment methods
100% examination
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Display conceptual understanding of the nature of data analysis and probability modelling.
- Critically evaluate managerial problems that can be framed as data analysis problems.
- Perform advanced statistical analyses and communicate results in written reports.
- Demonstrate effective use of Python statistical packages.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Deal with complex issues both systematically and creatively, make sound judgements in the absence of complete data, and communicate conclusions clearly to specialist and non-specialist audiences.
- Demonstrate self-direction and originality in tackling and solving problems through research design, data collection, analysis, and reporting.
- Demonstrate effective use of statistical software.
University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.
