Run by School of Computer Science and Electronic Engineering

20.000 Credits or 10.000 ECTS Credits

Semester 1 & 2

This modules aims to:

• provide exposure to the key concpets surrounding large scale data storage, management, queries and analysis.
• provide practical instruction in storing large-to-big data sets.
• expose students to core Big Data concepts, such as the 4 Vs.
• explore the OSEMN lifecycle/process.
• illustrate why traditional storage mechanisms are inappropriate for analysis workloads.

Indicative content includes:

• Data Marts and Warehouses and how they manage data collections.
• Big Data and the 4 Vs (Volume, Variety, Velocity and Veracity).
• Data Quality, the meaning and methods to raise it.
• Data Mining, extracting meaning from data.
• Analysis Tools and processes used to describe and evaluate data collections.

good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

threshold

Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

1. Critically discuss the various methods for storage large or Big Data data sets.

2. Construct an appropriate data warehouse for a given data set.

3. Demonstrate skills to suitably sanitise a given data set.

4. Explain the definitions and examples of the various types of analytical data.

Type	Name	Description	Weight
	Identify and Sanitise Exercise		42.50
	Construct and Document a Data Warehouse		57.50

		Hours
Lecture	Traditional lecture (2 hrs x 24 weeks).	48
Laboratory	Practical laboratories (S1 - 1 hrs x 12; S2 - 2 hrs x 12)	36
Private study	Private study including completing assignments.	116

• Literacy - Proficiency in reading and writing through a variety of media
• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
• Information retrieval - Able to access different and multiple sources of information
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.

• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Deploy theory in design, implementation and evaluation of systems
• Deploy tools effectively
• Development of general transferable skills
• Deploy systems to meet business goals
• Specify, deploy, verify and maintain information systems
• System Design
• Knowledge and/or understanding of appropriate scientific and engineering principles
• Knowledge and understanding of computational modelling
• Specify, deploy, verify and maintain computer-based systems
• Principles of appropriate supporting engineering and scientific disciplines

Talis Reading list

http://readinglists.bangor.ac.uk/modules/ice-3601.html

Reading list

• Star Schema The Complete Reference, Christopher Adamson, ISBN 9780071744324.
• Getting Started with Data Science: Making Sense of Data with Analytics: Making Sense of Data with Analytics, Murtaza Haider, ISBN 9780133991024.
• The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Ed.), Ralph Kimball, ISBN 9781118530801.

Compulsory in courses:

• H116: BSc Applied Data Science (Degree Apprenticeship) year 3 (BSC/ADS)
• N109: BSc Bus Analytics w Financial Tech year 3 (BSC/BAFT)
• N312: BSc Banking with Financial Tech year 3 (BSC/BKFT)
• I110: BSc Computer Information Systems year 3 (BSC/CIS)
• I11B: BSc Computer Information Systems (4 year with Incorp Found) year 3 (BSC/CIS1)
• IN00: BSc Computer Information Systems for Business year 3 (BSC/CISB)
• IN0B: BSc Computer Information Sys for Bus (4 year w Incorp Found) year 3 (BSC/CISB1)
• IN02: BSc Computer Information Systems for Business (Franchised) year 3 (BSC/CISBF)
• I111: BSc Computer Information Systems with International Exp year 4 (BSC/CISIE)
• I11P: BSc Computer Information Systems with Industrial Placement year 4 (BSC/CISP)
• H118: BSc Data Science & Artificial Intelligencetellig year 3 (BSC/DSAI)
• H113: BSc Data Science and Machine Learning year 3 (BSC/DSML)
• H114: BSc Data Science and Visualisation year 3 (BSC/DSV)

Optional in courses:

• G400: BSC Computer Science year 3 (BSC/CS)
• G40B: BSc Computer Science (4 year with Incorporated Foundation) year 3 (BSC/CS1)
• G40F: BSc Computer Science year 3 (BSC/CSF)
• I102: BSc Computer Science (with International Experience) year 4 (BSC/CSIE)
• H117: MComp Computer Science year 3 (MCOMP/CS)