Registry

Module Specifications

Archived Version 2020 - 2021

Module Title
Module Code
School
Online Module Resources
NFQ level	9	Credit Rating	10
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The purpose of this module is to help develop the student’s capacity to make better data-driven decisions through leveraging insights gained from data analysis and to motivate students’ appreciation of current needs for marketing metrics and to develop a comprehensive understanding of strategy-based performance measurement frameworks. The module aims to equip students with a variety of data visualisation techniques and the knowledge of a variety of tools and statistical techniques to make sense of the emergence and exponential growth of big data. The content of this module is delivered mainly through lecturers, case studies and in class demonstrations.

Learning Outcomes

1. Explain Data Analytics, the emergence of big data and how organisations can make use of them.
2. Understand different Data Visualisation Techniques and explain the benefits and limitations of different techniques.
3. Understand the Big Data Lifecycle and how organisations can implement the Big Data Lifecycle as a consulting approach.
4. Understand advanced analytics, statistical modelling techniques and contrast them for different types of problems.
5. Evaluate and allocate appropriate tools, techniques and frameworks to analyse a complex business-related issues.
6. Develop an appreciation of performance management frameworks.
7. Use key marketing metrics and design marketing performance measurement systems.
8. Assess marketing metrics practices in organisations.
9. Demonstrate research, work management, presentation, and collaboration skills.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	30	Class or online lectures.
Assignment Completion	120	Individual assignment research and completion.
Group work	24	Working on group assignments.
Seminars	6	Attendance at guest lectures or seminars on relevant topics.
Tutorial	10	Attendance at tutorials on different analytics techniques and tools.
Independent Study	60	Preparation for class and tutorials.
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

Data Types and Data Structure
What are the different types of data types? What is the difference between structured and unstructured data? Which types of data sources organisations from different industries are currently using?

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

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

Introduction to Databases and SQL
Definition and types of databases and basic SQL commands for data retrieval.

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

Social Media Analytics
Basic text mining and network analytics using R and Gephi.

Marketing Metrics
Basic and advanced metrics to measure marketing performance.

Case Studies
Exemplar use cases of data analytics and metrics in marketing.

Assessment Breakdown
Continuous Assessment	%	Examination Weight	%

Type	Description	% of total	Assessment Date
Course Work Breakdown

Reassessment Requirement
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
Unavailable

Indicative Reading List

Paul Farris, Neil Bendle, Phillip Pfeifer, David Reibstein: 2015, Marketing Metrics: The Manager's Guide to Measuring Marketing Performance, 3, Pearson, 978013408596
Runkler, Thomas A.: 2016, Data Analytics, Springer, 9783834825
Daniel G. Murray: 2013, Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software, John Wiley & Sons, Inc., 9781118612
Paul Teetor: 2011, R Cookbook, O'Reilly, 9780596809157
Harvard Business Review: 2018, HBR Guide to Data Analytics Basics for Managers, Harvard Business Press, 9781633694293

Other Resources

32544, Online Tutorial, Kubicle, 0, Tableau for Data Visualization, https://www.kubicle.com/products/tableau, 32545, Online Tutorial, Kubicle, 0, Excel for Business Analytics, https://www.kubicle.com/products/excel, 32546, Online Tutorial, Datacamp, 0, Introduction to R, https://www.datacamp.com/courses/free-introduction-to-r,

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