2023/24 Taught Postgraduate Module Catalogue

LUBS5996M Understanding Data for Decision Making

15 creditsClass Size: 400

Module manager: Sajid Siraj
Email: S.Siraj@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2023/24

This module is not approved as an Elective

Module summary

This module gives students a hands-on practise on acquiring data from disparate sources and gaining business insights from these data that can help in managerial decision making. Students will learn how to create spreadsheets and perform operations like filtering, sorting, drilling, dicing, and slicing (standard business terms used by data analysts). The students will learn how to gain insights from these data, both visually as well as statistically, and use them for critical evaluation and evidence-based decision making. This module has no pre-requisite for prior knowledge of data analytics, computer science, or statistics.

Objectives

To empower students with the digital skills related to obtaining data from different sources and understanding these data to support evidence-based decision making.

Learning outcomes
On completion of the module, students will be able to: 
1. understand and apply cross-industry standard processes for analysing data 
2. acquire and interpret various types of data from a range of data sources 
3. critically evaluate the visual characteristics of data and to demonstrate how to process operations in a spreadsheet. 
4. statistically infer insights from data, explaining relationships and/or identifying outliers. 
5. critically appraise their findings and use these evaluations and insights to make business decisions.

Syllabus

The indicative contents are the following:
- Introducing the IBM’s Cross Industry Standard Process for data mining (CRISP-DM).
- Understanding various data sources (files, databases, web services, feeds) and the issues and challenges related to different types of data.
- Performing data operations (e.g. filtering, sorting, searching) and visual inspection of data to highlight any trends and/or anomalies.
- Combining data from different sources (e.g. groceries transactions with loyalty cards). Grouping data for summarising and joining data for investigation.
- Performing statistical descriptive analysis and using them for business and data understanding phases of CRISP-DM.
- Creating and validating prediction models (e.g. using Excel Solver) for decision making.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	1.00	10.00
Practical	10	2.00	20.00
Independent online learning hours			20.00
Private study hours			100.00
Total Contact hours			30.00
Total hours (100hr per 10 credits)			150.00

Private study

The students are expected to practice software skills taught during the lectures and demonstrated during practicals. Some of the tasks will involve going online and exploring open-access data sources, for example, from Kaggle and/or UCI repositories. There will be no text book.

Opportunities for Formative Feedback

Online weekly quizzes with individualised feedback.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	A project requiring data analysis, 3000 words.	100.00
Total percentage (Assessment Coursework)		100.00

Resit is 100% by 3,000 word report

Reading list

There is no reading list for this module

Last updated: 20/02/2024

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