ANALYTICS TOOLS FOR BUSINESS ECONOMICS - 2024/5
Module code: ECO2065
Module Overview
The aim of this module is to introduce many of the important new ideas in data mining and business analytics, explain them as statistical framework, and describe some of their applications in Business Economics and Finance.
Data mining is the process of mining large quantity of data to extract useful information. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well-known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including at firms such as Meta. An understanding of business analytics and data mining concepts and techniques can offer a valuable advantage in the competition for jobs and placements. This sector remains one of just a few areas showing consistent growth in terms of job opportunities and salaries even during recession.
Module provider
Economics
Module Leader
GOLSON Eric (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 51
Lecture Hours: 22
Seminar Hours: 11
Guided Learning: 33
Captured Content: 33
Module Availability
Semester 2
Prerequisites / Co-requisites
ECO2064
Module content
The module content will focus on a selected set of critical areas in data analytics and the software tools used. As an indication of the kind of concepts that will be covered, below is an indicative set of topics:
-An introduction to data mining process model for business and management
-Data pre-processing, visualisation and exploratory analysis used in large datasets
-Use of AI including neural network in data mining and its application in risk analysis
-Classification, decision trees and their applications.
-Association and rule mining and their applications to business and management
-Data mining predictive models and their applications
-Accessing and collecting data from the Web and introduction to text mining.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | COURSEWORK 1 | 40 |
| Coursework | GROUP COURSEWORK 2 | 60 |
Alternative Assessment
Coursework 2 is a group coursework which can be done individually in the resit period.
Assessment Strategy
The summative assessment for this module consists of:
- 40% python individual coursework
- 60% group data analytics project coursework - technical report based on open ended project.
Formative assessment and feedback
Students receive verbal feedback during lectures and seminars in which questions and real-world examples are discussed. In addition to this, they receive a number of problem sets designed to further their knowledge and prepare them for the summative assessments which are discussed in lab tutorials. Students receive guideline solutions against which they can compare their own results.
Module aims
- Introduce the fundamentals of data mining and its application to business analytics.
- Identify the various algorithms of data mining and how to effectively apply them to real-world problems.
- Explore leading data mining using examples from Business Economics.
Learning outcomes
| Attributes Developed | ||
| 001 | Identify the key steps of the data mining process. | KP |
| 002 | Apply key methods using analytics software for turning diverse data into useful insights. | KPT |
| 003 | Demonstrate the ability to use data mining and coding in Python. | KPT |
| 004 | Construct text mining models for analysing complex data structures. | CKPT |
| 005 | Recognise and tools needed to reveal patterns and valuable information hidden in large data sets. | CKT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to: encourage critical understanding of the role played by data analytics for business decision making. Learning will be directed, supported and reinforced through a combination of lectures, computer laps, and online discussion groups, plus directed and self-directed study. The module is research-led and offers a mix of theoretical insights and case study material that will be delivered in different formats where appropriate.
The learning and teaching methods include:
1 hour lecture per week X 9 weeks
2 hour lab tutorial per week X 11 weeks
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECO2065
Other information
Employability: The module is designed to give fundamental knowledge and skills in data analysis which is in high demand in business, government, and academia. Resourcefulness and resilience: Student will learn and practice data analysis within Python which is widely used for data analysis in business and public bodies. Global and Cultural Intelligence: Students will be able to collect and analyse data from various countries to observe differences and similarities between countries and regions. Sustainability: Doing the group coursework student will learn how to plan, coordinate, and complete projects. They will learn how to work in teams and evaluate teammate¿s contributions. Learned transferable skills can be applied to future employment or graduate study. Digital Capabilities: Student will learn how to use Python for large scale data analysis. They will obtain general understanding of how programming languages work.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.