Module Code: H9DATA
Long Title Data Analytics
Title Data Analytics
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Simon Caton
Module Author: EUGENE O'LOUGHLIN
Departments: School of Computing
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse and evaluate large data sets
LO2 Extract, transform, and load data to interpret value
LO3 Create strong analytical predictive models
LO4 Transform information from data analysis for human perception, cognition, and communication through data analysis and visualization
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
Introduction to Data Analytics
• History and context of big data • Examples of big data • Data analytics articulated • Data analytics technology landscape
Exploratory Data Analysis
• Data Types • Numeric/non-numeric data • Data Hierarchy • Databases • Querying databases • Data mining • Statistical analysis
Data preparation
• Normalization and standardization • Basic transformations of value types • Handling missing values • Outliers • Data sampling • Joins • Aggregation • Changing value types • Balancing data
Predictive Models
• Correlations • k-Nearest neighbour analysis • Predictive data mining • Generalized linear regression models • Model evaluation • Decision trees
Time Series Analysis
Frequency-domain methods;  Time-domain methods;  Seasonal cycles (e.g. Holt-Winters exponential smoothing)
Data visualization
• Data visualization tools • Infrastructure for data visualization • Charts/Graphs • KPI Dashboards • Interactive data visualization
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 25
Assessment Date: n/a Outcome addressed: 1
Non-Marked: No
Assessment Description:
In-class test 1: Learners will analyse and evaluate a financial data set to generate reports such as descriptive statistics and charts to represent the data.
Assessment Type: 430 % of total: 25
Assessment Date: n/a Outcome addressed: 2
Non-Marked: No
Assessment Description:
In-class test 2: Learners will be provided with a database file in order to extract, transform, and load (ETL) data to interpret value and answer specific questions about the data.
Assessment Type: Project % of total: 50
Assessment Date: Sem 1 End Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Project Learner projects will be an investigation into large data sets. Data are to be analysed with a view to generating a detailed report on how these data can be used to inform decision-making and to add value to a business. Learners will be free to choose their own data sets from either on-line resources or to generate their own data. Datasets selected will be submitted for approval by project supervisor. Is it intended that learners will in the main examine financial, economic, marketing, or other business data.
No End of Module Assessment
No Workplace Assessment
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.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Every Week 24.00
Tutorial No Description 24 Every Week 24.00
Independent Learning No Description 202 Every Week 202.00
Total Weekly Contact Hours 48.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Every Week 24.00
Tutorial No Description 24 Every Week 24.00
Independent Learning No Description 202 Every Week 202.00
Total Weekly Contact Hours 48.00
 

Module Resources

Recommended Book Resources
  • EMC Education Services. (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons, [ISBN: 111887613X].
  • Chisholm, A.. (2013), Exploring Data with RapidMiner, Packt Publishing, [ISBN: 1782169334].
  • John W. Foreman.. (2013), Data Smart: Using Data Science to Transform Information into Insight, Chichester; John Wiley and Sons, [ISBN: 111866146X].
Supplementary Book Resources
  • Provost, F. & Fawcett, T.. (2013), Data Science for Business, United States; O'Reilly Media, Incorporated, [ISBN: 1449361323].
  • Hofmann, M. & Klinkenberg, R.. (2013), RapidMiner: Data Mining Use Cases and Business Analytics Applications, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, [ISBN: 1482205491].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: