Business Statistics with Python - BUSN9690

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Canterbury
Autumn Term 7 15 (7.5) Shaomin Wu checkmark-circle

Overview

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.

Details

Contact hours

Private Study: 116
Contact Hours: 34
Total: 150

Method of assessment

Main assessment methods:
VLE Test 1 (45 minutes): 20%
VLE Test 2 (45 minutes): 20%
Examination (2 hours): 60%

Reassessment methods
100% examination

Indicative reading

See the library reading list for this module (Canterbury)

Learning outcomes

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.

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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