Long Title:Advanced Business Data Analysis
Language of Instruction:English
Module Code:H8ABDA
Credits: 5
NFQ Level:LEVEL 8
Field of Study: Software and applications development and analysis
Module Delivered in 4 programme(s)
Module Coordinator: EUGENE O'LOUGHLIN
Module editor: Jer Hayes
Teaching and Learning Strategy: Teaching and Learning will take place via lectures, case studies and lab-based tutorials. The lab-based tutorials will reinforce the concepts introduced in the lectures and give the students practical experience in carrying out advanced business data analysis techniques. These labs will also allow the students to develop skills in using a statistical package to explore data and select the most appropriate tool(s) to conduct analyses. Students will be introduced to how statistical results are reported correctly via a number of case-study research papers. These case studies will also highlight the practical application of statistical techniques.
Learning Environment: Learning will take place in a classroom environment with access to IT resources. Learners will have access to library resources, both physical and electronic. Module materials will be placed on Moodle, the College’s virtual learning environment.
Module Description: The module aims to enable learners to address statistical problems in data analytics on a practical level so that learners are in a position to conduct more advanced analyses independently and critically evaluate research papers. Topics covered include an elaboration on inferential statistics learned in the module Business Data Analysis, p-values revisited and confidence intervals, SPSS analyses of inferential tests, non-parametric tests, effect size, power and sample size, meta-analysis, cluster and factor analysis, etc.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Evaluate and choose between different options for inference statistics so that a motivated decision between two or more options can be made
LO2 Critically evaluate statistical applications in a particular discipline using advanced topics (Power analysis, sample size calculation, cluster and factor analysis)
LO3 Conduct advanced statistical analyses using a statistical package (e.g. SPSS/SAS)
LO4 Interpret the results output of a statistical package (e.g. SPSS/SAS)
LO5 Work out and apply a strategy for a statistical analysis when presented with a real-world problem from business
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).
19343 H8BDA1 Business Data Analysis
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.
Pre-requisite Module: Business Data Analysis (H8BDA1)
 

Module Content & Assessment

Indicative Content
Conducting Statistical Analyses (40%)
• Practical: Students will be presented with simple research questions and should design mock studies involving inferential statistics that allow one to answer the question at stake. For instance: Is the mental concentration to study and learn in the morning higher than in the evening? Or Is going to the Gym linked to loss of body weight? • Data available on the WWW for teaching purposes or data collected via Super Lab is used for these analyses. • Examples of carrying out interference tests using SPSS Applying t-test, ANOVA, correlation analysis, and regression. • Which types of inference statistics would be insightful? Which methodological challenges can be expected? Non-parametric alternatives to classical tests: when to use them?
Effect Size, Sample Size, Power (30%)
• Understanding the interdependencies between the triangle of effect size, power and sample size and how all this relates to tests of significance. • Effect size Power analysis Sample size.
Factor Analysis and Cluster Analysis (30%)
• Aggregation that generates dimensions: PCA, Exploratory Factor. • Analysis Aggregation that generates groups: Cluster Analysis.
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Continuous Assessment (0200) Sample Assessment: The continuous assessment (practical) will be an individual assessment. Learners will be provided with a research scenario & will be asked to work out a plan for a statistical analysis that involves descriptive & inferential statistics. The expected output will be a plan for a statistical analysis incorporating appropriate conclusions & justifications regarding the decisions made. Within this plan for a statistical analysis, learners will be expected to demonstrate their ability to link abstract concepts to a research scenario & to critically evaluate the plan chosen against the backdrop of possible alternative design decisions. 1,2,3,4,5 40.00 n/a
End of Module Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Terminal Exam The end of semester examination paper which is two hours in duration usually contains three questions, with students required to answer two of the three questions. Question format will usually be of essay-style but may also include other formats (e.g., a plan for an extended business data analysis project or a technical figure). Marks will be awarded based on clarity, structure relevant examples, depth of topic knowledge and an understanding of the potential and limits of solutions 1,2,3,4,5 60.00 End-of-Semester
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 Time No Description 7.5 Every Week 7.50
Total Hours 10.50
Total Weekly Learner Workload 10.50
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 Time No Description 89 Every Week 89.00
Total Hours 92.00
Total Weekly Learner Workload 92.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Cortinhas, C. and Black, K. 2012, Statistics for Business and Economics, 1st European Edition edition Ed., John Wiley & Sons [ISBN: 1119993660]
Supplementary Book Resources
  • Frederick L. Coolidge, Statistics, Sage Publications, Inc [ISBN: 1412991714.]
  • Peter Dalgaard, Introductory Statistics with R, Springer [ISBN: 0387790535]
  • Jean Dickinson Gibbons 1993, Nonparametric statistics, Sage Publications Newbury Park, Calif. [ISBN: 0803939515.]
  • Bill Jelen, PowerPivot for the Data Analyst: Microsoft Excel 2010, Que [ISBN: 0789743159.]
  • Wayne L. Winston Ph.D., Microsoft Excel 2010, Microsoft Press [ISBN: 0735643369]
  • Timothy C. Urdan, Statistics in Plain English, 3rd Ed. [ISBN: 978-041587291]
This module does not have any article/paper resources
Other Resources
  • Website: The Khan Academyhttp://www.khanacademy.org/
  • Website: Learn with Dr Eugene O’Loughlinhttp://www.youtube.com/eoloughlin
  • Website: Central Statistics officehttp://www.cso.ie
  • Website: Glossary of Statistical Termshttp://bit.ly/LIRYpQ
  • Website: HyperStat Online Statistics Textbookhttp://davidmlane.com/hyperstat/
  • Website: The R Project for Statistical Computinghttp://www.r-project.org/
 

Module Delivered in

Programme Code Programme Semester Delivery
BSHTM B.Sc. (Hons) in Technology Management 8 Group Elective 1
BSHC BSc (Honours) in Computing 8 Optional
BSHBIS BSc (Hons) in Business Information Systems 8 Optional
HDSDA Higher Diploma in Science in Data Analytics 2 Core Subject