## Module and Programme Catalogue

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This module is inactive in the selected year. The information shown below is for the academic year that the module was last running in, prior to the year selected.

### 20 creditsClass Size: 30

Email: A.Choicharoon@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2020/21

### This module is mutually exclusive with

This module is approved as a discovery module

### Module summary

This module provides an introduction to analytical techniques that are used commercially to support business decisions.

### Objectives

This modules aims to further extend the knowledge and experience of students in the application of analytical techniques used in business analytics.

Learning outcomes
Learning Outcomes – Knowledge/Application
Upon completion of this module students will be able to:

- Describe and explain analytical techniques (Knowledge)
- Accurately apply these techniques to business problems (Application)

Learning Outcomes – Skills
Upon completion of this module students will be able to:

Subject specific
1. Apply appropriate analytical techniques to analyse business data to support management decision making

Transferable
1. Analytical skills – mathematical/numerical/statistical
2. Creative problem solving
3. Critical thinking – reviewing evidence; interpreting results
4. Research skills
5. Use of knowledge

Skills outcomes
Upon completion of this module students will be able to apply appropriate analytical techniques to analyse business data in support of management decision making.

### Syllabus

Indicative content:
1. Introduction to R
2. Loops, Function and Data Pre-processing
3. Data visualisation
4. Dealing with Missing Data
5. Descriptive Statistics and Linear Regression
6. Time Series Forecasting (Time Series Decomposition, Moving Averages. Exponential Smoothing, AR and Arima models)
7. Dimensionality Reduction (PCA and Factor Analysis)
8. Cluster Analysis
9. Decision Trees and Support Vector Machines
10. Discrete Choice Models
11. Survival Analysis
12. Neural Networks
13. Text Analysis
14. Optimisation (Linear and Evolutionary Algorithms)

### Teaching methods

 Delivery type Number Length hours Student hours e-Lecture 22 1.00 22.00 Tutorial 21 1.00 21.00 Private study hours 157.00 Total Contact hours 43.00 Total hours (100hr per 10 credits) 200.00

### Private study

This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.

### Opportunities for Formative Feedback

Your teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.

### Methods of assessment

Exams
 Exam type Exam duration % of formal assessment Standard exam (closed essays, MCQs etc) 3 hr 100.00 Total percentage (Assessment Exams) 100.00

The resit for this module will be 100% by 3 hour examination.