Module: Analytics Tools and Programming

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Analytics Tools and Programming
Module Code	SB5001 (ITS) / BAA1088 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Mathieu Mercadier
Module Teachers	Guto Santos, Theodore Lynn
NFQ level	9	Credit Rating	10
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only
Resubmit exercises.

Description

The main objective of this module is develop an understanding of the management of analytics projects and to introduce the student to the fundamental tools and techniques of using data and communicating insights to inform management decisions. The module will introduce a variety of approaches for framing analytical problems and projects including CRISP-DM and the EMC Data Lifecycle, as well as project management and communication approaches and considerations. Students will develop their understanding and practical skills in preparing, visualising and analysing structured and unstructured data. Students will be exposed to the principles of data science and analytics and equipped with a variety of tools and techniques to prepare, visualise and analyse data. Common analytics problems will be explored using Excel (Solver), SQL, Tableau, R and other commonly used tools, programming languages and techniques.

Learning Outcomes

1. Understand the key concepts of data analytics, the characteristics of big data and how organisations can make use of them.
2. Understand different data preparation, visualisation and analysis techniques and explain the benefits and limitations of different techniques.
3. Evaluate, select and apply appropriate tools, techniques and frameworks to analyse a complex business-related issue.
4. Understand, identify, analyse and assess the legal, regulatory and ethical risks associated with analytics projects.
5. Evaluate, select and apply appropriate project management methodologies and techniques to manage a data analytics project.
6. Prepare and cost a detailed project plan for an analytics project including a data management plan.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	25	Class or online lectures
Assignment Completion	75	Individual assignment research and completion
Group work	50	Working on group assignments
Independent Study	50	Preparation for class and tutorials.
Tutorial	50	Online tutorials in project management and data analytics tools.
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 to Project Management
Key concepts in project management including starting and initiating a project, controlling a stage, managing product delivery, managing a stage boundary, directing and closing a project.

Managing data analytics projects
Deploying the a data analytics management framework (e.g. EMC Data Lifecycle), reframing a business challenge as an analytics challenge, selecting appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences.

Requirements Life Cycle Management
Introduce the process to manage and maintain requirements and design information from inception to retirement.

Strategy analysis
Align business analytics projects to the business need and strategic objectives of the organisation.

Solution Evaluation
Understand how to assess the performance of and value delivered by analytics solutions deployment within the organisation, and how to identify potential barriers or constraints that prevent the full realisation of the value.

Analytics and modelling using Excel
Excel Data Analysis Toolpak, Basic Statistics using Excel, Basic Modelling in Excel, Solving linear optimisation problems using Excel Solver

Preparing an Analytics Project Plan
Documenting an analytics project plan, resourcing and budgeting analytics projects, preparing a data management and data breach response plan.

Data Analytics using R
Data structure, working with data frames, and introduction to R functions.

Visual Analytics and Data Visualisation using Tableau
Tables, charts and dashboards using Tableau.

Data Preparation and Visualisation
Best practices in data cleaning, anomalies detection, and normalisation. Basic and advanced visualisation techniques.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Group assignment	Students will be allocated to a group and will be required to undertake a data analytics project for a client.	30%	n/a
Assignment	The course will have regular in-class exercises to enable students apply different techniques and familiarise themselves with them. Students will be graded on their participation in exercises including the submission of four pieces of in-class work.	20%	n/a
Research Paper	Students will be required to complete an individual analytics project including a detailed project plan.	30%	n/a
In Class Test	Students will be required to complete an in-class test on an agreed project management certification programme e.g. PRINCE2.	20%	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

IIBA: 2015, A Guide to the Business Analysis Body of Knowledge, 5, 6, 8, 10, 11, 978-1-927584
Runkler, Thomas A.: 2016, Data Analytics, Springer, 9783834825
Ohri, A: 2013, R for Business Analytics, Springer, 9781461443
Daniel G. Murray: 2013, Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software, John Wiley & Sons, Inc., 9781118612
Stanford InfoLab: 0, Mining Massive Datasets, http://www.mmds.org/,
Few, S.: 0, Show me the numbers: Designing tables and graphs to enlighten, Analytics Press,
Axelos: 2015, Prince2 Agile, Stationery Office Books (TSO), 9780113314676

Other Resources

58670, Online Tutorial, Tableau, 0, Free Training Videos, https://www.tableau.com/learn/training, 58671, Online Tutorial, DataCamp, 0, Introduction to R, https://www.datacamp.com/courses/free-introduction-to-r, 58672, Online Tutorial, Kubicle, 2020, Kubicle BI and Data Analytics Library, Kubicle,