| Module Code: |
H8ABDA |
|
Long Title
|
Advanced Business Data Analysis
|
|
Title
|
Advanced Business Data Analysis
|
| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| Module Coordinator: |
Margarete Silva |
| Module Author: |
EUGENE O'LOUGHLIN |
| Departments: |
School of Computing
|
| 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 |
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 |
| 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).
|
| 19343 |
H8BDA1 |
Business Data Analysis |
Co-requisite Modules
|
| No Co-requisite modules listed |
Module Content & Assessment
| Indicative Content |
|
Introduction 10%
Inferential Statistics Revisited, The R Programming Language, Statistical Tools (eg SPSS)
|
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Reporting Results (10%)
Stating Hypotheses, Making decisions, p values, Visuals (eg Boxplots)
|
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Tests for Normality (10%)
Normal distributions, Q-Q/P-P Plots, Shapiro-Wilk Test, Kolmogorov-Smirnov Test
|
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Analysis of Variance (10%)
One-way ANOVA, Two-Way ANOVA, Post-hoc Tests
|
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Regression (10%)
Simple Linear Regression, Multiple Linear Regression, Forecasting
|
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Non-parametric statistical tests (15%)
Mann-Whitney Test, Wilcoxon Sign-Rank Test, Kruskal-Wallis Test, Chi-Square Test
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Time Series Analysis (10%)
Smoothing data, ARIMA (Seasonal, Non-seasonal)
|
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Meaningful data reports (10%)
Sample size, Confidence intervals, Effect size, Power, Cohen’s d
|
|
Factor Analysis (15%)
Data reduction, Cross correlation, Principal Component Analysis, Eigenvalues, Clusters
|
| Assessment Breakdown | % |
|---|
| Coursework | 50.00% |
| End of Module Assessment | 50.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Assignment 1 |
% of total: |
25 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5 |
| Non-Marked: |
No |
Assessment Description:
In this assignment you will prepare a report based on three statistical tests:
• Student's t-Test (Paired or Unpaired)
• One-way ANOVA (including TukeyHSD if appropriate)
• Two-way ANOVA
Each test should be completed in Excel, SPSS, and R.
Data.
For this assignment you will source your own data. You may collect your own data if you wish, or use third-party data available online or in the literature. Each dataset should be at least 50 records - more than 100 records is not required. You may extract samples from larger datasets if you wish. Make sure you cite sources of data. You may not use data from sample files used in the Business Data Analysis or the Advanced Business Data Analysis |
|
| Assessment Type: |
Assignment 2 |
% of total: |
25 |
| Assessment Date: |
n/a |
Outcome addressed: |
3,4 |
| Non-Marked: |
No |
Assessment Description:
In this assignment you will use statistical tests for non-normal data. You may use methods (non-parametric statistics tests) and tools (R, Excel, or SPSS) of your own choice - please don't rely on one tool or method, variety is expected. It is not necessary to replicate any test you carry out, ie if you perform a test in R it is not necessary to repeat in SPSS and/or Excel. A data file (from the 2011 Census of Ireland) is suggested, though students are permitted to choose a different file if they wish (subject to approval by Lecturer). Your task is to prepare a statistical report based on the data in the file. |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
50 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5 |
| Non-Marked: |
No |
Assessment Description:
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 |
|
| 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 Time |
No Description |
7.5 |
Every Week |
7.50 |
| 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 Weekly Contact Hours |
3.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Andy Field. (2013), Discovering Statistics Using IBM SPSS Statistics, 4th. Sage Publications Inc, London, [ISBN: 9781446249].
-
Peter Dalgaard. Introductory Statistics with R, Springer, p.364, [ISBN: 0387790535].
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John Maindonald. (2008), Using R for data analysis and graphics. Introduction, code and commentary, http;//cran.r-project.org/doc/contrib./usingR.pdf.
| | Supplementary Book Resources |
|---|
-
Cortinhas, C. and Black, K.. (2012), Statistics for Business and Economics, 1st European Edition edition. John Wiley & Sons, [ISBN: 1119993660].
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EMC Education Services. (2015), Data Science and Big Data Analytics, John Wiley & Sons, Incorporated, [ISBN: 111887613X].
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John W. Foreman.. (2013), Data Smart: Using Data Science to Transform Information into Insight, Chichester; John Wiley and Sons, [ISBN: 111866146X].
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Neil Salkind. (2014), Statistics for People Who (Think They) Hate Statistics, 5th. Sage, [ISBN: 9781452277].
| | This module does not have any article/paper resources |
|---|
| Other Resources |
|---|
-
[Website], The Khan Academy. http://www.khanacademy.org/.
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[Website], Learn with Dr Eugene O’Loughlin. http://www.youtube.com/eoloughlin.
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[Website], Central Statistics office. http://www.cso.ie.
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[Website], Glossary of Statistical Terms. http://bit.ly/LIRYpQ.
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[Website], HyperStat Online Statistics Textbook. http://davidmlane.com/hyperstat/.
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[Website], The R Project for Statistical Computing. http://www.r-project.org/.
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