Modules

Core Module Information
Module title: Predictive Analytics
SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SOE11167
Module leader: Sujoy Bhattacharya
School The Business School
Subject area group: Management
Prerequisites

There are no pre-requisites for this module to be added
Description of module content:

Have you ever wondered how streaming companies know what movies and TV shows you will like? Or how do retailers know which products you will want to buy? It's all thanks to Predictive Analytics. Predictive Analytics is a way of using data to predict future events. Businesses and organizations of all types use Predictive Analytics to make better decisions. Here's a simple example:Imagine you're running a lemonade stand. You want to know how much lemonade to make on a given day. You could just guess, but wouldn't it be better to know with more confidence how many people will want lemonade? You could use predictive modelling to figure this out. You could use data like past sales numbers, the day of the week, and the weather forecast to predict how many people will want lemonade on a given day.Predictive Analytics can be used to predict all sorts of things, like:- How many people will attend a concert?- How many products a company will sell?- How much traffic there will be on a given road?- How likely a customer is to churn?- How likely a student is to succeed at university? etc.In this module, you will learn about different predictive analytics techniques and how to use them to help your organisation make more accurate decisions about future events.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Demonstrate knowledge and understanding of the capabilities as well as limitations of predictive analytics techniques and have insight into the different fields in which we can usefully apply each.

LO2: Choose the most appropriate predictive analytics technique using various types of information criteria.

LO3: Model and solve business decision problems using the appropriate predictive analytics techniques and software.

LO4: Interpret the results of predictive modelling analysis, including explaining margins of error and business implications.
Full Details of Teaching and Assessment
2025/6, Trimester 2, IN PERSON,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: IN PERSON
Location of delivery: CRAIGLOCKHAR
Partner:
Member of staff responsible for delivering module: Sujoy Bhattacharya
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 Lectures will introduce new topics and provide an overview of key concepts. In-class debate and discussion: we will encourage in-class debate and discussion to help you develop your critical thinking skills and to learn from each other.
Face To Face Practical classes and workshops 20 Computer labs will give you hands-on experience in modelling with the relevant software tools. Interactive case studies based, as far as possible, on real organisations and real datasets: to help you apply what they have learned to real-world situations.
Online Guided independent study 160 Directed reading: we assign directed readings to help you learn more about specific topics in depth. Additional independent learning: we expect you to conduct further additional independent learning to address any additional weaknesses and gaps you identify to achieve the required depth of learning. Use audio, video and online materials: to help you learn at your own pace and explore topics in more depth.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Project - Practical 40 1~2~3 Week 8 HOURS= One file. We will provide you with a personalized dataset. You will use your understanding of the business problem and the characteristics of your dataset to choose the best predictive techniques from a suite of diverse options and use them with appropriate software tools to predict future values for selected variables.You will submit your final computer model in the appropriate file format for the software application: during the assessment, we will open and run the file to evaluate your model.
Project - Written 60 1~2~3~4 Week 13 , WORDS= 2500 words We will provide you with a personalized dataset. You will use your understanding of the business problem and the characteristics of your dataset to choose the best predictive techniques from a suite of diverse options and use them with appropriate software tools to predict future values for selected variables.You will write a report, with two parts:1. A technical part to document and justify your modelling process, selection of models, and their technical specifications. (1500 words)2. A non-technical part to explain to non-technical business people how you used predictive modelling to address the business problem, the results of your model, the margins of error, and the business implications. (1000 words)
Component 1 subtotal: 40
Component 2 subtotal: 60
Module subtotal: 100

Indicative References and Reading List - URL:
Contact your module leader
 

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