Module Code: H8BGD
Long Title Programming for Big Data
Title Programming for Big Data
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: EUGENE O'LOUGHLIN
Module Author: Margarete Silva
Departments:  
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 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 Formulate and compose data flow and software documentation including flowchart, commenting and use-case diagram generation
LO4 Develop practical skills using a professional tool/language of data analytics (e.g. Python, R)
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).
21358 H8BGD Programming for Big Data
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

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%

Assessments

Full Time

Coursework
Assessment Type: Practical (0260) % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
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.
Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
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)
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 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 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 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].
  • Stinerock, R.. (2018), Statistics with R: A Beginner's Guide, 1. Sage.
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.net. http://videolectures.net/mit6046jf05_int roduction_algorithms/.
  • [Website], Cloudera University. http://university.cloudera.com/onlineres ources/hadoopecosystem.html.
  • [Website], MIT Open Courseware. http://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.
Discussion Note: