Module: Data Analytics & Metrics

Module Specifications.

Current Academic Year 2024 - 2025

All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).

As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.

Date posted: September 2024

Module Title	Data Analytics & Metrics
Module Code	MG5008 (ITS) / STA1000 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	-
Module Teachers	Andreas Robotis
NFQ level	9	Credit Rating	10
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Repeat the module

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	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	At least 8 case studies will be analysed and discussed in MG5008. Questions will be set for each case study. Students are required to read the assigned case studies and attempt to answer the allocated questions before each class. Students are not required to submit a one-page analysis for the case that their group is presenting. Students will be graded on their best 6 submissions.	30%	n/a
Group presentation	Each student will be allocated to a group and each group will present twice - on a case study or an analytics topic.	20%	n/a
In Class Test	Students are required to complete the Google Analytics Academy Digital Analytics Fundamentals Course at https://analyticsacademy.withgoogle.com/course. Students will be assessed through an in-class test with multiple choice, exercises and open questions.	10%	n/a
Assignment	Each student is required to submit a report demonstrating their mastery of the analytics techniques taught in MG5008. The teaching team will provide students with social media data sets. The results should be delivered in the form of a report with supporting appendices.	25%	Sem 1 End
Assignment	Students will be required to identify and interview marketing managers to develop an understanding of the current marketing performance practices in organisations. The findings should be delivered in the form of a presentation and a report with supporting appendices.	15%	Sem 2 End

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

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,