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Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title Data Analytics & Metrics
Module Code STA1000 (ITS: MG5008)
Faculty DCU Business School School DCU Business School
NFQ level 9 Credit Rating 10
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 that besides having the aim of maximising value for the firm, they also take into consideration sustainable practices such as environmental constraints.
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.


WorkloadFull time hours per semester
TypeHoursDescription
Lecture30Class or online lectures.
Assignment Completion120Individual assignment research and completion.
Group work24Working on group assignments.
Seminars6Attendance at guest lectures or seminars on relevant topics.
Tutorial10Attendance at tutorials on different analytics techniques and tools.
Independent Study60Preparation for class and tutorials.
Total Workload: 250
Section Breakdown
CRN10090Part of TermSemester 1 & 2
Coursework100%Examination Weight0%
Grade Scale40PASSPass Both ElementsN
Resit CategoryRC1Best MarkN
Module Co-ordinatorAndreas RobotisModule Teacher
Assessment Breakdown
TypeDescription% of totalAssessment Date
AssignmentAt 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 presentationEach 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 TestStudents 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
AssignmentEach 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
AssignmentStudents 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;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.

* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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.

Indicative Reading List

Books:
  • 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


Articles:
  • Experian: 2013, Big Data: The Future of Information and Business, Harvard Business Review, https://hbr.org/resources/pdfs/comm/experian/hbr_serasa_experian_report.pdf, 57085
  • 2011: Big Data, Analytics and the Path From Insights to Value, MIT Sloan Management Review, http://www.ttivanguard.com/realtime/bigdata.pdf, 57086, 1
  • Beyond the hype: Big data concepts, methods, and analytics,: International Journal of Information Management, 35, 57087, 1, S. Fox and T. Do
  • International Journal of Managing Projects in Business: 6, 57088, 1, U. Sivarajah, M. Mustafa Kamal, Z. Irani, V. Weerakkody, 2017
  • 70: 57089, 1, Lynn, T., Healy, P., Kilroy, S., Hunt, G., van der Werff, L., Venkatagiri, S. and Morrison, J., 2015, Towards a general research framework for social media research using big data
  • 57090: 1, Chen, M., Mao, S. and Liu, Y, 2014, Big Data: A Survey, Mobile Networks and Applications, 19,
  • 1: Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C., 2018, Social media analytics–Challenges in topic discovery, data collection, and data preparation, International Journal of Information Management, 39,
  • Linda J. Popky: 2015, Identify the Marketing Metrics That Actually Matter, Harvard Business Review, https://hbr.org/2015/07/identify-the-marketing-metrics-that-actually-matter, 57093
  • 2014: Online Marketing Metrics You Need To Be Measuring, Forbes, https://www.forbes.com/sites/jaysondemers/2014/08/15/10-online-marketing-metrics-you-need-to-be-measuring/#11be9d9676c1, 57094, 1
  • Assessing marketing performance: reasons for metrics selection: Journal of Marketing Management, 20, 57095, 1, O'Sullivan, D., & Abela, A. V.
  • Journal of Marketing: 71, 57096, 1, Järvinen, J., Karjaluoto, H., 2015
  • 50: 57097, 1, Hanssens, D. M., Pauwels, K. H., 2016, Demonstrating the value of marketing
Other Resources

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

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