Module Code: H8BDANAL
Long Title Business Data Analysis
Title Business Data Analysis
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: Simon Caton
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 Develop a strategy for a statistical analysis when presented with a real- world problem from business
LO3 Apply methodologies used in prediction (forecasting), and interpret the results
LO4 Use and compare software tools for business data analysis (e.g. SPSS, R, Excel, SAS)
LO5 Critically evaluate statistical applications in a particular discipline using advanced topics (Power analysis, sample size calculation, cluster and factor analysis)
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).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
Descriptive Statistics/Data Presentation
Arrangement, pre-processing and representation of data  Measures of central tendency (mode, median, mean)  Normal distributions  Measures of dispersion (range, variance, standard deviation)  Scales of Variables  Statistical graphics figures (e.g., box-plot, histograms)
Probability
Sample points, sample space, events  Calculating probabilities  Venn diagrams  Combinatorial mathematics
Tests for Normality
Normal distributions  Q-Q/P-P Plots  Shapiro-Wilk Test  Kolmogorov-Smirnov Test
Inferential Statistics Parametric Tests
Single sample z test  Students t-Test (independent/dependent samples)  One-way ANOVA  Two-Way ANOVA  Post-hoc Tests
Inferential Statistics Non-parametric Tests
Mann-Whitney Test  Wilcoxon Sign-Rank Test  Kruskal-Wallis Test  Chi-Square Test
Reporting Results
Stating Hypotheses  Making decisions  p values  Visuals (eg Boxplots)
Prediction Testing
Simple Linear Regression  Multiple Linear Regression  Correlation Smoothing and filtering of data
Time Series Analysis
Smoothing data  Weighted averages  Exponential smoothing  ARIMA (Seasonal & Non-seasonal)
Meaningful data reports
Sample size Con dence intervals  Effect size  Power  Cohens d
Factor Analysis
Data reduction  Cross correlation  Principal Component Analysis  Eigenvalues  Clusters
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 25
Assessment Date: n/a Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
In this assignment learners will be required to analyse a data set of their own choosing (see sample assessment below).
Assessment Type: Continuous Assessment % of total: 25
Assessment Date: n/a Outcome addressed: 4
Non-Marked: No
Assessment Description:
In this assignment, leaners will be required to use non-parametric tests on data that are not normally distributed (eg census data). See sample assessment below.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
The examination will be a minimum of two hours in duration and may in- clude a mix of: short answer questions, vignettes, essay based questions and case study based questions. Marks will be awarded based on clarity, appropriate structure, relevant examples, depth of topic knowledge, and evidence of outside core text reading.
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: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Every Week 24.00
Tutorial No Description 24 Every Week 24.00
Independent Learning No Description 202 Every Week 202.00
Total Weekly Contact Hours 48.00
 

Module Resources

Recommended Book Resources
  • Pallant, Julie.. (2016), SPSS Survival Manual., Open University Press, [ISBN: 033526154X.].
  • Salkind, Neil J. (2016), Statistics for People Who (Think They) Hate Statistics: Using Microsoft Excel 2016., SAGE Publications, Inc, [ISBN: 1483374084.].
  • Andy Field.. Discovering statistics using IBM SPSS statistics, Thousand Oaks; Sage Publications, [ISBN: 1446249182].
Supplementary Book Resources
  • McClave, Terry T. Sincich James T. (2013), Statistics., Pearson Education Limited, [ISBN: 1292022655].
  • Cortinhas, Carlos and Ken Black. (2012), Statistics for Business and Economics., John Wiley & Sons, [ISBN: 1119993660].
  • Wayne L. Winston Ph.D.. Microsoft Excel 2010, Microsoft Press, p.720, [ISBN: 0735643369].
  • Bill Jelen. PowerPivot for the Data Analyst: Microsoft Excel 2010, Que, p.576, [ISBN: 0789743159].
  • Timothy C. Urdan. Statistics in Plain English, Third Edition, Taylor and Francis, p.232, [ISBN: 041587291X.].
  • Peter Dalgaard. Introductory Statistics with R, Springer, p.364, [ISBN: 9780387790534].
  • Maindonald, J H. Using R for Data Analysis and Graphics Introduction, Code and Commentary.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: