Short Title:Data Analysis and Programming
Full Title:Data Analysis and Programming
Language of Instruction:English
Module Code:DATA H4001
 
Credits: 5
Field of Study:Computing
Module Delivered in 2 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:JOHN BURNS
Module Description:This module provides the student with an understanding of state-of-the-art big data analysis concepts and techniques, and the ability to develop solutions to big data problems using suitable algorithms and software.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Be able to apply a range of appropriate statistical techniques to analyze a variety of data sets
LO2 Demonstrate a detailed knowledge and understanding of data analysis scripting tools and techniques
LO3 An understanding of the theory, concepts and method practical applications of Big Data.
LO4 Apply best practice when using Big Data solutions from the public cloud providers
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project A group project in which students will program and implement an example of a big data applications using software such as Hadoop, HDFS, and MapReduce or R Statistical Software. 1,4 25.00 Week 8
Project Students will write a report which summarises a topic or application related to big data, eg. to propose an approach to solve a specific problem and describe how to implement and test the solution on a big data set. 2,3,4 25.00 Week 4
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam n/a 1,2,3 50.00 End-of-Semester

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
Lecture Lectures 2.00 Every Week 2.00
Lab Labs 2.00 Every Week 2.00
Independent Learning Independent Work 3.00 Every Week 3.00
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
Lecturer/Lab Lecture/ Lab 4.00 Every Second Week 2.00
Independent Learning Independent Work 5.00 Every Week 5.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
 

Module Resources

Recommended Book Resources
  • EMC Education Services 2015, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley
  • B. Baesens 2014, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley
  • B. Lublinsky, K. Smith, A. Yakubovich 2013, Professional Hadoop Solutions, Wiley
  • K. Cukier, V. Mayer-Schönberger 2013, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Harcourt
  • E. Sammer, Hadoop Operation, O’Reilly
  • D. Miner, A. Shook, MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, O'Reilly
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KACOD_B Bachelor of Science (Hons) in Computing with Data Analytics 8 Mandatory
TA_KCODA_B Bachelor of Science (Hons) in Computing with Data Analytics (Add-On) 2 Mandatory