Short Title:Data Analytics and Visualisation
Full Title:Data Analytics and Visualisation
Language of Instruction:English
Module Code:DAVS H3000
 
Credits: 5
Field of Study:Marketing and advertising
Module Delivered in 5 programme(s)
Reviewed By:GLENN MEHTA
Module Author:GARRY O REGAN
Module Description:The purpose of this module is to develop critical student appreciation of key data mining techniques available to marketing professionals, and to develop the skills needed to compile, analyse, and model data using modern data visualisation techniques and software tools.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Consider and apply a range of specialised data analysis techniques and tools in Marketing and Advertising contexts.
LO2 Identify and discuss the potential of data analytics tools for Marketing and Advertising.
LO3 Create and present high impact data visualisations using current data visualisation techniques and software tools.
LO4 Source, compile and evaluate data required for specific business-driven analytics.
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Continuous Assessment Weekly individual and group assignments: In addition to the formal test points, marks will be awarded for regular individual and group assignments undertaken during weekly lab sessions (some of which may require completion by a subsequent session); 1,2,3,4 10.00 Ongoing
Continuous Assessment Individual assignment to develop high impact visualisations for selected real world data. Assessment would involve a) Choose subject area and acquire data b) Design the visualisations c) Develop the visualisations d) Test and present the finished product. 3,4 25.00 Ongoing
Laboratory Practical work assessment: A formal in-class test in which the student will complete an assignment to test the achievement of learning outcomes for the practical work to date (analytics). 1,2 25.00 Week 12
Short Answer Questions An in-class theory assessment on all theory covered - questions presented through an online assessment medium (Moodle). This may be presented in two parts - mid and end semester. 1,2,3,4 40.00 Week 12
No End of Module Formal Examination

TU Dublin – Tallaght Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecturer/Lab Identify and develop the skills needed to (a) compile, analyse and effectively model data using modern data visualisation techniques and software tools and (b) identify, evaluate and apply a selection of key data mining tools and techniques using selected software tools. 3.00 Every Week 3.00
Independent Learning Reading and assignments. 6.00 Every Week 6.00
Total Weekly Learner Workload 9.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecturer/Lab Identify and develop the skills needed to (a) compile, analyse and effectively model data using modern data visualisation techniques and software tools and (b) identify, evaluate and apply a selection of key data mining tools and techniques using selected software tools. 2.00 Every Week 2.00
Independent Learning Reading and assignments. 7.00 Every Week 7.00
Total Weekly Learner Workload 9.00
Total Weekly Contact Hours 2.00
 

Module Resources

Recommended Book Resources
  • Eric Siegel. 2016, Predictive analytics, 2nd Ed., New York; Wiley [ISBN: 1119145678]
  • Daniel T. Larose 2014, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Ed., Wiley [ISBN: 0470908742]
  • Provost, Foster and Fawcett, Tom 2013, Data Science for Business: What you need to know about data mining and data-analytic thinking, 1 Ed., O'Reilly Media [ISBN: 978-144936132]
  • Scott Murray 2017, Interactive Data Visualization for the Web, 2nd.ed. Ed., O'Reilly Media [ISBN: 978-149192128]
  • Nathan Yau 2013, Visualize This, 2nd. ed. Ed., Wiley [ISBN: 978-19383770]
  • Stephen Few. 2013, Information dashboard design, 2nd ed. Ed., Analytics Press [ISBN: 1938377001]
  • William Lidwell, Kritina Holden, Jill Butler. 2015, The Pocket Universal Principles of Design, Gloucester, Mass; Rockport Publishers [ISBN: 1631590405]
Recommended Article/Paper Resources
  • Tableau Software Visual Analysis Best Practices
Other Resources
  • Website: Tableau SoftwareVisualisation Analytical Tool, Tableau Software Inc., Seattle, USA.
  • Website: Data Mining SoftwarePolyanalyst, Megaputer Intelligence Inc., Illinois, U.S.A.
  • Website: Power BI, Microsoft
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_BMKDM_D Bachelor of Arts in Digital Marketing 5 Mandatory
TA_BMADM_D Certificate in Applied Digital Marketing (60 credit Minor Award) 1 Mandatory
TA_BPDMK_C Certificate in Digital Marketing (30 credit Special Purpose Award) 1 Elective
TA_BPSMK_C Certificate in Social Media Marketing (30 credit Special Purpose Award) 1 Elective
TA_BIDMS_D International Digital Management & Sales 1 Mandatory