Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Data Analytics & Visualisation
Module Code	MT5000
School	DCUBS
Online Module Resources
Module Co-ordinator	Semester 1: terry obrien Semester 2: terry obrien Autumn: terry obrien
Module Teacher	terry obrien
NFQ level	9	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The information age is characterised by enormous amounts of data generated as part of an ever-widening range of our day-to-day activities. This data can lead to better decision-making, insight, and advantage. The module aims to equip learners 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 and in class demonstration.

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. Use the CRISP-DM Methodology of Data Mining.
4. Understand advanced analytics, statistical modelling techniques and contrast them for different types of problems.
5. Allocate appropriate tools to analyse a complex business-related issue

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	22	The lecturer will present the essential ideas and core concepts pointing students towards resources where they can get further information
Independent learning	65	Preparation for, and reading after lectures
Assignment	40	Assignments listed in Coursework above
Total Workload: 127

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

Introduction to Data Analytics and Visualisation.
- What is Data Analytics? Growth of Big Data. Data Analytics usage in organisations. Barriers to using Big Data..

Data Visualisation.
- Data Quality/Data Capture, Functions of Visualisations, Graphic Integrity, Data-Ink Ratio, Tables & Graphs, Multiple Datasets, Interactive Graphs.

CRISP-DM Methodology.
- Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation.

Tools.
- Statistical Software, Data Programming, Databases & languages, Business Intelligence Tools.

Advanced Analytics and Statistical Modelling.
- Basic and Advanced Statistical Tests, Linear and Logistic Regression, Clustering Techniques, Decision Trees, Time Series Analysis, Text Analysis, Survival Analysis.

More Advanced Tools & Techniques.
-EDA (Exploratory Data Analysis), Neural Networks, Machine Learning, Support Vector Machines (SVMs), Principal Component Analysis (PCA), K-Means Clustering, NoSQL, Apache Hadoop, Map Reduce,.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Assignment	Report and Presentation on a Tool or Statistical Technique	50%	Week 28
Assignment	Application of CRISP-DM Methodology to a Business Case	50%	Sem 2 End

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
This module is category 1

Indicative Reading List

Rajaraman, A., & Ullman, J. D: 2012, Mining of massive datasets, Cambridge University Press, Cambridge,
Rao, C. R., Wegman, E. J., & Solka, J. L.: 2005, Handbook of Statistics, Volume 24 Data Mining and Data Visualization., Elsevier., Burlington,
Tufte, E. R: 1983, The visual display of quantitative information, Graphics Press, Cheshire, Conn,,
Few, S.: 2012, Show me the numbers: designing tables and graphs to enlighten., Analytics Press., Burlingame, Calif,,

Other Resources

7145, Journal: Business Intelligence Journal., 0,

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