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DATA8001 - Data Science and Analytics

Title:Data Science and Analytics
Long Title:Data Science and Analytics
Module Code:DATA8001
Credits: 5
NFQ Level:Advanced
Field of Study: Data Format
Valid From: Semester 1 - 2017/18 ( September 2017 )
Module Delivered in 2 programme(s)
Module Coordinator: David Goulding
Module Author: Aengus Daly
Module Description: This module will provide the learner with an overview of the important themes in the growing field of data science and analytics. The learner will study both the established methods and technologies used and also investigate new and emerging ones. Emphasis will be placed on the context and use of data analytics within organisations, within, for example, decision support systems, business performance management and knowledge management systems. Data analytics/mining software will be used, e.g. SAS, RapidMiner and R in both the lectures and labs.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Describe the field of data science and analytics, its concepts, technologies and its historical roots. Give a detailed overview of the main approaches to developing a data analytics/mining project lifecyle.
LO2 Perform exploratory data analysis using mining software packages.
LO3 Find patterns and solutions within a data set using data mining techniques.
LO4 Describe a number of data mining and business intelligence concepts and techniques.
LO5 Describe the area of big data, unstructured data and visualisation techniques and their relationship to data mining and analytics.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
13373 DATA8001 Data Science and Analytics
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
No Co Requisites listed

Module Content & Assessment

Indicative Content
Data Science and Analytics landscape, terminology, technologies and historical development.
Data Analytics Project Life Cycle
CRISP-DM etc., variety of actors, challenges. Investigate case studies in the field, looking at a variety of approaches, technologies with successes, failures, new developments and unusual applications of analytics.
Information and Business Systems for Data Analytics
Where does the data come from? Information systems theory, and various systems architectures - relational databases, data warehouses, OLAP, NoSQL, main characteristics of cloud computing. How people and culture of an organisation impact on information systems and analytics.
Data Quality
Cleaning/scrubbing data, data modelling, ETL (Extract, Transform, Load) systems and methods.
Data Mining Techniques and Software Technologies
Introduction to various data mining techniques and methods. Use some data mining technologies e.g. SAS, RapidMiner and/or R. How to load data, and carry out initial data analysis and visualisation.
Technical Report Writing
How to write a technical report - structure and narrative of documents, referencing, bibliography and awareness of expected audience.
Ethics, privacy and security
Investigate ethics, privacy, security, data protection legistation and related topics in data governance.
Analytics in a Business Setting
How data analytics is used within a business setting to monitor performance. Key performance indicators (KPIs), dashboards, balanced score cards, performance prism. How data analytics is incorporated into an organisation’s strategy and vision.
Big Data
Definitions, differences in structured/unstructured data. Parallel processing systems, NoSQL. Search and big data. Investigative visualisation technologies for data mining and anomaly detection.
Assessment Breakdown%
Course Work40.00%
End of Module Formal Examination60.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Solve a data analytics problem using a data mining software package and produce a report. 2,3,4 40.0 Week 9
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,4,5 60.0 End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute 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
Lecture Theory and Case Studies 2.0 Every Week 2.00
Lab Computer-based lab 1.0 Every Week 1.00
Tutorial Theory/Practical 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Independent Study 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theaory and case Studies 2.0 Every Week 2.00
Lab Computer-based lab 2.0 Every Second Week 1.00
Independent Learning Independent Study 4.0 Every Week 4.00
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00

Module Resources

Recommended Book Resources
  • Efraim Turban , Ramesh Sharda, Dursun Delen 2011, Decision Support and Business Intelligence Systems, 9th Ed., Pearson Prentice Hall New Jersey [ISBN: 013610729X]
  • Andy Field, Jeremy Miles, Discovering Statistics Using SAS, 1st Ed. [ISBN: 1849200920]
Supplementary Book Resources
  • Ramez Elmasri, Shamkant B. Navathe 2007, Fundamentals of database systems, 5th Ed., Pearson Addison Wesley Boston [ISBN: 0321369572]
Recommended Article/Paper Resources
  • Watson, Hugh 2011, Business Analytics Insight: Hype or Here to Stay?, Business Intelligence Journal, vol. 16, No. 1, 1-8
This module does not have any other resources

Module Delivered in

Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory
CR_SDAAN_9 Master of Science in Data Science and Analytics 1 Mandatory

Cork Institute of Technology
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