Long Title:Programming for Big Data
Language of Instruction:English
Module Code:H8BGD
Credits: 5
NFQ Level:LEVEL 8
Field of Study: Software and applications development and analysis
Module Delivered in 2 programme(s)
Module Coordinator: EUGENE O'LOUGHLIN
Module editor: Ioana Ghergulescu
Teaching and Learning Strategy: The learning strategy involves the use of lectures, tutorials and practical labs work as appropriate. Furthermore, the module uses a learner-centred approach that acknowledges the variety of learning preferences in the classroom. It is highly interactive incorporating opportunities for learning by doing. Students will participate in activities designed to facilitate the development of their learning skills and strategies, aimed to enhance their ability to engage with and participate in modules taken during the course of their studies.
Learning Environment: Learning will take place in both a classroom and computer laboratory environment with access to IT resources. Learners will have access to library resources, both physical & electronic and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the Colleges virtual learning environment. The labs will concentrate on implementing programs and manipulating data for analysis, and how best to implement the theory learned during the module.  
Module Description: Enable learners to acquire the necessary key programming skills required to develop applications for processing big data. identify and illustrate the challenges associated with developing programs and applications for big data. Learners will gain practical experience in programming for big data through the use of a range of relevant and appropriate programming languages for given processing tasks (e.g., data extraction, aggregation, reporting etc.). Utilise cloud computing platforms and develop programs for data processing in distributed computing environments.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Design algorithms and implement key programming patterns and constructs for big data
LO2 Assess the challenges associated with processing big data datasets and compare and contrast programming for big data vis-à-vis programming for conventional datasets
LO3 Investigate parallel and distributed computing and write programs for processing datasets in distributed computing and cloud computing environments using relevant programming paradigms (e.g., MapReduce) and relevant programming languages (e.g., Pig, Hive)
LO4 Apply practical skills using a professional tool/language of data analytics (e.g., R)
Pre-requisite learning
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
Requirements

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
 

Module Content & Assessment

Indicative Content
1. Introduction to Data Programming (50%)
Algorithm design Program I/O Data types and data structures Program control and process models Programming constructs Programming types (imperative, declarative, functional, logic) Programming languages for data analytics (e.g., R, Python) Developing programs for data processing activities (e.g., data extraction, cleaning, merging, aggregation, analysis, reporting)
2. Big Data Programming (50%)
Challenges associated with programming for big data Parallelism for computational processes Storage and compute locality Distributed computing Utilisation of cloud computing platforms for big data processing Distributed programming paradigms Distributed programming environments (e.g., Hadoop/HBase) MapReduce algorithm design Big data programming tools and languages (e.g., Pig, Hive)
Learning Environment
Learning will take place in both a classroom and computer laboratory environment with access to IT resources. Learners will have access to library resources, both physical & electronic and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the College’s virtual learning environment. Labs The labs will concentrate on implementing programs and manipulating data for analysis, and how best to implement the theory learned during the module.
Assessment Breakdown%
Coursework100.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical (0260) Assessment will be through a series of continuous assessment practical assignments given throughout the semester. Sample assessment: create a Python program that computes a company’s inventory and returns the stock for a product requested by the user. 1,2,3,4 50.00 n/a
Project Learners will be assessed through a project with both practical and research elements. Sample project: You are required to carry out a series of analyses of two datasets utilising appropriate programming languages and programming environments. For each of the chosen datasets you are required to compile a report of the analysis (circa 3,000 words for the report) 1,2,3,4 50.00 n/a
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.
Reassessment Description
Learners will be afforded an opportunity to repeat the final examination and all learning outcomes will be assessed in the repeat sitting.

NCIRL 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 No Description 2 Every Week 2.00
Tutorial No Description 1 Every Week 1.00
Independent Learning No Description 7.5 Once per semester 0.63
Total Hours 10.50
Total Weekly Learner Workload 3.63
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Tutorial No Description 1 Every Week 1.00
Independent Learning No Description 89 Once per semester 7.42
Total Hours 92.00
Total Weekly Learner Workload 10.42
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Paul Teetor, R Cookbook, O'Reilly Media [ISBN: 0596809158]
  • Tom White, Hadoop: The Definitive Guide, O'Reilly Media [ISBN: 1449311520]
Supplementary Book Resources
  • Thomas H. Cormen... [et al.] 2009, Introduction to algorithms, MIT Press Cambridge, Mass. [ISBN: 0262033844.]
  • Donald Miner, Adam Shook, MapReduce Design Patterns, O'Reilly Media [ISBN: 1449327176.]
This module does not have any article/paper resources
Other Resources
  • Website: MIT Open Courseware videolectures.nethttp://videolectures.net/mit6046jf05_int roduction_algorithms/
  • Website: Cloudera Universityhttp://university.cloudera.com/onlineres ources/hadoopecosystem.html
  • Website: MIT Open Coursewarehttp://ocw.mit.edu/courses/electrical-en gineering-and-computer-science/6-00sc-in troduction-to-computer-science-and-progr amming-spring-2011/index.htm
  • Website: Andrew M. Raim 2013, Introduction to Distributed Computing with pbdR at the UMBC
    http://userpages.umbc.edu/~gobbert/paper s/pbdRtara2013.pdf
  • Book: Wes McKinney 2012, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly
  • Book: Anand Rajaraman, Jeffrey David Ullman 2014, Mining of Massive Datasets, Cambridge University Press
 

Module Delivered in

Programme Code Programme Semester Delivery
BSHTM B.Sc. (Hons) in Technology Management 7 Group Elective 1
HDSDA Higher Diploma in Science in Data Analytics 1 Core Subject