Module: Analytics and Business Intelligence

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Analytics and Business Intelligence
Module Code	MT5181
School	DCUBS
Module Co-ordinator	Semester 1: Mathieu Mercadier Semester 2: Gerry Conyngham Autumn: Mathieu Mercadier
Module Teachers	Gerry Conyngham Nuala Lonergan Mathieu Mercadier
NFQ level	9	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Coursework Only

Description

This module is designed to give students an insight into the tools and techniques used in Business Analytics and Business Intelligence to enable organisations use data to make better decisions.

Learning Outcomes

1. Will be able to effectively communicate the key elements of Business Intelligence and the advantages of an effective BI strategy in an Organisation
2. Will be able to effectively communicate and present the output from a wide variety of analytical tools
3. Will gain a knowledge of the applications of machine learning techniques in business
4. Will gain knowledge in the use of the Python Programming Language and POWER BI for Visualisation
5. Will have the ability to identify the correct questions to “ask” their data teams to help achieve a strategic goal or goals.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Total Workload: 0

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Business Analytics and Business Intelligence
What are Business Analytics and Business Intelligence, Lifecycle of Business Analytics Project, Applications in Industry, Growth of Data

Why Business Intelligence?
Advantages of a BI strategy for a modern data driven Organisation. Obstacles and Challenges in delivering an effective BI strategy

Key Business Intelligence Software
Data Visualisation and Analysis Tools (POWER BI), Programming Languages (Python), Cloud Based Tools

Analytical Techniques in Business Analytics
Descriptive Analytics, Prescriptive Analytics, Predictive Analytics, Data Mining, Text Analytics, Network Analytics, Web Analytics

Machine Learning and Artificial Intelligence
Overview of Machine Learning and AI. Applications in Business, Machine Learning Case Study

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Report(s)	Report on The Role of Business Analytics is used in the students chosen Industry / Organisation	70%	n/a
Group project	Business project involving feature engineering, ML & BI using Python and POWER BI. Presentation of the project.	30%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Jaggia, Sanjiv: 2021, Business analytics: communicating with numbers, McGraw-Hill New York,
Eric Siegel, Edward L. Glaeser, Cassie Kozyrkov, Thomas H. Davenport: 2020, Strategic Analytics: The Insights You Need, Harvard Business Review,
Skyrius, Rimvydas: 2021, Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture, Springer International Publishing,

Other Resources

None

Programme or List of Programmes

MBA	Masters in Business Administration

Archives: