ANALYTICS TOOLS FOR BUSINESS - 2024/5
Module code: MAN2189
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, Finance, Marketing, and Management.
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 Facebook and Twitter. An understanding of business analytics and data mining concepts and techniques can offer a valuable advantage in the competition for jobs and placements.
Surrey Business School
TOLOO Mehdi (SBS)
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: 96
Lecture Hours: 11
Laboratory Hours: 22
Guided Learning: 10
Captured Content: 11
Prerequisites / Co-requisites
MAN2188 - FUNDAMENTALS OF BUSINESS ANALYTICS
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
- Introduction to IBM SPSS Modeler software
- 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 type||Unit of assessment||Weighting|
|Coursework||Analytics Tools Group coursework||60|
|Examination Online||Analytics Tools examination 120 min||40|
Alternative Assessment for ‘Analytics Tools Group coursework’ is an Individual coursework
The assessment strategy is designed to provide students with the opportunity to demonstrate:
Knowledge of the entire data analytics workflow process
Appreciation of the objectives for robust data analytics workflow processes
Ability to apply the theories, conceptual frameworks and methodologies that underpin data analytics and Machin learning algorithms in real applications
Thus, the summative assessment for this module consists of:
Analytics Tools Group coursework (60%)
For the project coursework, in small groups, to apply and deepen their knowledge with the criteria related to learning outcomes (1) to (5). The project will include the analysis of a set of real-world data using data analytics techniques within the taught software tools and visualising the results appropriately to produce a written report. Evidence of background reading must be provided. The project provides an opportunity for the student to experience the whole process of data analytics using machine learning by using the taught software tools using the approaches introduced in the workshops / labs, to undertake all necessary steps using the standard data mining process. The student will present the project findings and conclusions in a written report, give an oral presentation and provide code / software used, all of which will be marked. Students will be informed about the coursework topic and group at the beginning of the semester.
Analytics Tools Computer lab examination (40%)
The 2-hour examination will be a standard open book computer based - examination, with material coming directly from the module content. The broader topics for the questions will be covered during the taught and laboratory sessions. The problems will be similar to those discussed in the taught component, computer laboratory or indicated in the essential reading material. The answers will mainly be discursive and will be in alignment with the criteria related to learning outcomes.
Online assessments tied to lab sessions
Group feedback on lab session results – common errors, examples of good practice.
Individual feedback after online assessment covering key concepts.
- 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 software, as well as Python and R, using examples in many areas such as Business, Finance, Marketing, and Management.
|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 packages such as IBM-Modeler and coding in Python and R for analytics.||KPT|
|004||Construct text mining models for analysing complex data structures.||CPT|
|005||Recognise and tools needed to reveal patterns and valuable information hidden in large data sets.||KCPT|
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 a critical understanding of the importance of a robust data workflow that translate diverse, often messy data sources into useful cleaned / insights for decision-making.
cultivate an understanding of the main issues and challenges; provide a coherent conceptual framework; develop a critical awareness of the various approaches of machine learning algorithms.
introduce students the concept of a robust data workflow and to teach the basics of the various tools that enable operational delivery of data insights in business. The intention is to teach the objectives of the data workflow process (e.g. clean data, simple to understand insights) and how that relates to the decisions to be made off the data, rather than an exhaustive list of prospective software.
The course will be practical, encouraging students to get hands-on with example data analytics software tools, such as IBM SPSS Modeler and programming in Phyton and R that allow them to progress through all stages of the data workflow process, giving them an appreciation of the operational processes and software required to deliver effective operational data processes in business.
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.
Upon accessing the reading list, please search for the module using the module code: MAN2189
Analytics tools for business are key to instruments used in business. In this module students develop and deepen their digital capabilities in the context of business analytics in order to be able to make more sustainable and resourceful decisions as managers and leaders of the future. The module is essential for students to be employable.
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.