Module:

Module Specifications..

Current Academic Year 2024 - 2025

Please note that this information is subject to change.

Module Title
Module Code
School
Module Co-ordinator	Semester 1: Mathieu Mercadier Semester 2: Gerry Conyngham Autumn: Mathieu Mercadier
Module Teachers	Gerry Conyngham Mathieu Mercadier
NFQ level	8	Credit Rating
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

None

Description

This module aims to make students proficient in the fundamental technical skills required to work as a Business Analytics professional in a modern data driven organisation and in parallel to ensure students can apply their analytic skills to real life analytic projects providing real value and insights to these organisations. The module will start by providing students a definition of Business Intelligence and explain the role and benefit of using BI in modern data driven organisations The module has three key applied elements; - developing the some key programming and software skills (including Python, SQL, Power BI) - an applied project where students will work with on a real world project to apply the analytics skills they have developed - an e-portfolio , which allows students to showcase and evidence their business analytics skills. Students will develop their advanced Python skills using online teaching tools and this learning will be further developed in two full day programming workshops, where students will work as teams on Data Science challenges using Python. They will be supported in these workshops by experienced Python programmers. Another key feature of the module is student choice. Students will work independently on their analytical programming and software skills, using Digital Learning Tools. Within a suite of options they will have the option to specialise in analytics skills aligned in one area of business analytics, which will ideally be linked to their future career vision. Finally, the module will also include guest speakers from industry partners covering aspects of the Business Analytics Function, applications of analytics and communicating analytics findings.

Learning Outcomes

1. -Will be proficient in the use of the Python Programming Language, POWER BI (M/DAX) for Visualisation and SQL
2. Will develop the key technical skills to enable them function effectively in their own chosen analytics role.
3. The ability to support a business function identify the correct questions to “ask” their data to help achieve a strategic goal or goals.
4. Will be able to effectively communicate and present the output from a wide variety of analytical tools
5. Will have the insight and ability to align their own analytical skills to a future role as a Business Analyst in a modern data driven organisation.
6. Will be able to effectively communicate the key elements of Business Intelligence and the advantages of an effective BI strategy to a modern data driven organisation.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Fieldwork	40	Creating E-Portfolio of Analytics Skills
Seminars	5	Guest Speakers on Industry: Role of and Applications of Analytics in Business
Workshop	15	2 Learning Sprints - Workshops on Python were students collaborate to solve analytics programming challenges to develop their Python skills
Online activity	50	Enhancing their industry specific analytics skills independently using online training tools
Professional Practice	70	Applied Industry Based Projects
Online activity	50	Using online resource to learn Python, Power BI and SQL
Lecture	20	No Description
Total Workload: 250

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 and Advanced Python
What is Python, Why use Python, Jupyter Network Storing and Exploring Data, Google Colab, Basic Calculations in Python Functions in Python, Loops and Conditional Functions Importing data to Python from MS EXCEL, other formats Storing and Transforming Data, Numpy arrays, Panda Dataframes, Data Unions and Joins, Aggregating Data Python Modules and Packages Data Visualisation using Python (Matplotlib) Data Scraping using Python, HTML, JSON Files, Linking to APIs Object Oriented Programming

Applications of Business Analytics
Applied Client Based Analytics Project, Guest lectures from Industry on the role of analytics in business

-Specialised analytics Skills
Choice from suite of specialised analytics skills e.g. Big Data Management Tools and Hadoop Ecosystem, Marketing Analytics Tools and Techniques, Cloud Based Storage and Amazon Web Services, Predictive Analytics and Operations Research, Network Analytics and applications,………..)

Communicating analytics findings
Effective Presentation of Analytics Findings

SQL Training
Understanding SQL Databases, Selecting and Filtering Data with SQL, Joining Data with SQl, Agrrgating Data with SQL, Data Mainuplation with SQL

Power BI Training
Introduction to BI, Building your First Dashboard, Advanced Visualisations (Tremaps, Matrices, Waterfalls Charts), Query Editor, Data Analysis Expressions (DAX) in Power BI

Assessment Breakdown
Continuous Assessment	%	Examination Weight	%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Professional Portfolio	Eportfolio of Business Analytics Using DCUs E-Portfolio Platform students will need to create an E-Portfolio providing a summary of the Business Analytical skills they have developed over on their programmes and linking these skills to their own Professional Development Plan and analytic skills required in Business, The key elements of portfolio will be - Overview of applied project(s), including project scope, deliverables and final report - reflection on their analytics learning path - a summary of the role of analytics and tools used in modern data driven organisations - evidence in a variety of forms (professional certification, awards, sample work,	50%	Sem 2 End
Participation	Participation in activities and exercises throughout module	10%	Every Week
Digital Project	Python Programming Exercise	20%	n/a
Digital Project	Power BI Group Assignment	10%	n/a
Digital Project	SQL Exercise	10%	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

Indicative Reading List

Eric Siegel Edward L. Glaeser Cassie Kozyrkov Thomas H. Davenport: 2020, Strategic Analytics: The Insights You Need, Harvard Business Review, Boston, Massachussets, USA,
Skyrius, Rimvydas: 2021, Business Intelligence: A Comprehensive Approach to Information Needs, Technologies and Culture, 1st, Springer International Publishing,
Jaggia, Sanjiv: 2021, Business analytics: communicating with numbers, McGraw-Hill, New York,
2014: Business Intelligence Guidebook: from Data Integration to Analytics., Morgan Kaufmann., 286442
2015: Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance., Wiley & Sons., 286443
2013: Learning Python., O'Reilly, 286444
2015: Python Business Intelligence Cookbook., Packt publishing., 286445
2017: Microsoft Power BI Cookbook., Packt publishing., 286446
2020: (2020). Learning SQL., O'Reilly,

Other Resources

59429, Online Training, Kubicle, 0, Online Training in MS EXCEL, POWER BI, SQL, PYTHON, Machine Learning and AI, https://kubicle.com/,

This is a 10 Credit Module which is part of the 20 Credit Specialism in Business Analytics. All three moduels are corequisites

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Module Specifications..

Current Academic Year 2024 - 2025