| Long Title: | Business Data Analysis |
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| Language of Instruction: | English |
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| Field of Study: |
Management and administration
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| Module Coordinator: |
EUGENE O'LOUGHLIN |
| Module editor: |
EUGENE O'LOUGHLIN |
| Teaching and Learning Strategy: |
Learning and teaching will take place in computer laboratories to facilitate access to the necessary tools to conduct data analysis tasks. Classes will be divided into lectures and tutorials. Use will be made of case studies and real-world problems and data. In-class discussions will take place with online tools such as Twitter and Moodle to facilitate out of class discussion. Video support for problem-solving techniques will also be provided. |
| Learning Environment: |
Learning will take place in a class room/lab environment with IT access. Module materials will be placed on Moodle, the college's virtual learning environment. |
| Module Description: |
The module aims to give the student practical experience of analysing a range of data sets relevant to business decision making. Students examine how the concepts of statistics can help with business management and solve business related problems through the use of case study material. |
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| LO1 |
Explain the principles and uses of descriptive statistics and inferential statistics. |
| LO2 |
Use Principles of statistical Inquiry |
| LO3 |
Carry out analyses based on descriptive and inferential statistics within a business context |
| LO4 |
Demonstrate the usage of methodologies applied in prediction (forecasting) |
| LO5 |
Use and understand software tools for business data analysis (e.g. SPSS, R, Excel) |
| 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).
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| No recommendations listed |
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.
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| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Descriptive Statistics/Data Presentation (30 %)
Arrangement, pre-processing and representation of data
Measures of central tendency (mode, median, mean)
Measures of dispersion (range, variance, standard deviation)
Scales of Variables
Statistical graphics & figures (e.g., pie chart, bar chart)
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Inference Statistics (35 %)
Standard Errors
Hypothesis Testing
Parametric Tests (e.g., T-Test, ANOVA, regression)
Non-parametric Tests (e.g., chi-square tests)
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Prediction/Forecasting (35 %)
Simple Linear Regression
Correlation
Smoothing and filtering of data
Nature of time series
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| Assessment Breakdown | % |
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| Coursework | 50.00% |
| End of Module Assessment | 50.00% |
Full Time
| Coursework |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Assignment |
Assessment will consist of week graded tutorials to carry our statistical analysis on sample data sets using tools such as Excel, R, and SPSS. |
1,2,3,4,5 |
50.00 |
n/a |
| End of Module Assessment |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Terminal Exam |
End-of-Semester Final Examination |
1,2 |
50.00 |
End-of-Semester |
| 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.
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Reassessment Description
Learners will be afforded an opportunity to repeat the final examination and all learning outcomes will be assessed in the repeat sitting.
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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 |
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 |
| Total Hours |
2.00 |
| Total Weekly Learner Workload |
2.00 |
| Total Weekly Contact Hours |
2.00 |
Module Resources
| Recommended Book Resources |
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- Neil J. Salkind 2014, Statistics for People Who (Think They) Hate Statistics, 5th Ed., Sage Publications, Inc Thousand Oaks [ISBN: 978-1-4522-77]
- McClave, James & Sincich, Terry 2012, Statistics, 12th edition. Ed., Pearson
| | Supplementary Book Resources |
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- Andy Field 2013, Discovering Statistics Using IBM SPSS Statistics, 4th Ed., Sage Publications Inc London [ISBN: 978-1-4462-49]
- Peter Dalgaard, Introductory Statistics with R, 2008 Ed., Springer [ISBN: 9780387790534]
- Maindonald John 2008, JohnUsing R for data analysis and graphics. Introduction, code and commentaryhttp;//cran.r-project.org/doc/contrib./usingR.pdf. .
- McClave, James T., Benson, George & Sincich, Terry 2013, Statistics for Business and Economics, 12th Ed., Prentice Hall
| | This module does not have any article/paper resources |
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| This module does not have any other resources |
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Module Delivered in
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