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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Analytics Tools and Programming
Module Code SB5001
School DCUBS
Module Co-ordinatorSemester 1: Theo Lynn
Semester 2: Theo Lynn
Autumn: Theo Lynn
Module TeachersTheo Lynn
Martin Perry
NFQ level 9 Credit Rating 10
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Coursework Only
Resubmit exercises.

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.

Workload Full-time hours per semester
Type Hours Description
Lecture25Class or online lectures
Assignment Completion75Individual assignment research and completion
Group work50Working on group assignments
Independent Study50Preparation for class and tutorials.
Tutorial50Online 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 Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Group assignmentStudents will be allocated to a group and will be required to undertake a data analytics project for a client.30%n/a
AssignmentThe 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 PaperStudents will be required to complete an individual analytics project including a detailed project plan.30%n/a
In Class TestStudents 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;
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

  • 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,
Programme or List of Programmes
GDMBAGraduate Diploma in Business Analytics

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