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Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

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Module Title
Module Code (ITS)
Faculty School
Module Co-ordinatorSemester 1: Mathieu Mercadier
Semester 2: Gerry Conyngham
Autumn: Mathieu Mercadier
Module TeachersGerry Conyngham
Nuala Lonergan
Mathieu Mercadier
NFQ level 9 Credit Rating
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.



Workload Full-time hours per semester
Type Hours Description
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% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Report(s)Report on The Role of Business Analytics is used in the students chosen Industry / Organisation70%n/a
Group project Business project involving feature engineering, ML & BI using Python and POWER BI. Presentation of the project.30%n/a
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

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