Module: Analytics & Business Intelligence

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Analytics & Business Intelligence
Module Code	MT5181 (ITS) / BAA1062 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Mathieu Mercadier
Module Teachers	-
NFQ level	9	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

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: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
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