Registry
Module Specifications
Archived Version 2020 - 2021
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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. | |||||||||||||||||||||||||||||||||||||
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 |
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Indicative Content and
Learning Activities Data Types and Data StructureWhat 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 ExcelExcel Data Analysis Toolpak, Basic Statistics using Excel, Basic Modelling in Excel, Solving linear optimisation problems using Excel Solver.Data Preparation and VisualisationBest practices in data cleaning, anomalies detection, and normalisation. Basic and advanced visualisation techniques using Tableau.Introduction to Databases and SQLDefinition and types of databases and basic SQL commands for data retrieval.Data Analytics using RData structure, working with data frames, and introduction to R functions.Social Media AnalyticsBasic text mining and network analytics using R and Gephi.Marketing MetricsBasic and advanced metrics to measure marketing performance.Case StudiesExemplar use cases of data analytics and metrics in marketing. | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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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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