Module Code: H8BIA
Long Title Business Intelligence and Analtyics with Social Media
Title Business Intelligence and Analtyics with Social Media
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: Simon Caton
Module Author: Simon Caton
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 Identify, apply and distinguish between foundational theories of social media analysis for business intelligence use cases and case studies
LO2 Construct and infer business value from social media applications and scenarios
LO3 Evaluate pertinent theories and methods of social media analysis in the context of business intelligence
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).
20650 H7BID Business Intelligence and Data Warehousing I
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
Overview and Foundations
• Business Intelligence and Analytics 1.0 - 3.0. • Applications of Social Media for Business Intelligence. • Business Uses of Social Media.
Accessing Social Media Data
• Tools for accessing and transforming social media data, e.g. NodeXL and Wandora.
Foundations of Network Analysis
• Foundations of Graph Theory Centrality Indices and Concepts Network Models and Connectivity.
Analysing the Social Web
• Tie strength Trust Network Propagation Location-based Analysis Ego-centric and socio-centric networks.
Text Analysis, Mining and Analytics
• Content Analysis Bags of Words Sentiment Analysis Topic Modelling.
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Assignment % of total: 40
Assessment Date: n/a Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
Group-based Case Studies: in each case study, learners should define a business intelligence pipeline using self-curated online social media data sets. Learners propose several business intelligence use cases and construct proof-of-concept analysis frameworks that leverage appropriate methods of analysis to illustrate potential business value.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,3
Non-Marked: No
Assessment Description:
End-of-Semester Final Examination
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 2 Every Week 2.00
Independent Learning No Description 17 Every Week 17.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Tutorial No Description 2 Every Week 2.00
Independent Learning No Description 17 Every Week 17.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Jennifer Golbeck. (2013), Analyzing the Social Web, Morgan Kaufmann, p.290, [ISBN: 978-012405531].
  • Ulrik Brandes (Editor), Thomas Erlebach (Editor). Network Analysis : Methodological Foundations, Springer, p.471, [ISBN: 9783540249795].
Supplementary Book Resources
  • Derek Hansen, Ben Shneiderman, Marc A. Smith. Analyzing Social Media Networks with NodeXL, Morgan Kaufmann, p.304, [ISBN: 9780123822291].
  • Matthew A. Russell. (2013), Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, O'Reilly, p.444, [ISBN: 9781449367619].
  • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang. Fundamentals of Predictive Text Mining, Springer, p.283, [ISBN: 1849962251].
Recommended Article/Paper Resources
  • Negash, S.. (2004), Business intelligence, The Communications of the Association for Information Systems, 13(1).
  • Chen, H., Chiang, R. H., & Storey, V. C.. (2012), Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, MIS Quarterly: Management Information Systems, 36(4).
Supplementary Article/Paper Resources
  • Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S.. (2011), Social media? Get serious! Understanding the functional building blocks of social media., Business horizons, 54(3), p.241-2.
  • Lim, E. P., Chen, H., & Chen, G.. (2013), Business intelligence and analytics: research directions., ACM Transactions on Management Information Systems (TMIS), 3(4).
  • Chau, M., & Xu, J.. (2012), Business intelligence in blogs: Understanding consumer interactions and communities, MIS Quarterly: Management Information Systems, 36(4).
Other Resources
Discussion Note: