Module Code: A8BDA
Long Title Business Data Analysis
Title Business Data Analysis
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: EUGENE O'LOUGHLIN
Module Author: Madita Feldberger
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 Describe and summarise quantitative data using a range of descriptive statistics and visuals
LO2 Demonstrate and apply the concepts of probability and hypothesis testing
LO3 Distinguish between and apply statistical methods for analysing financial data
LO4 Apply and interpret prediction and forecasting techniques to make current decisions for forecasting and planning
LO5 Develop analytical skills using software tools for business data analysis (e.g. SPSS, Excel, Tableau)
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
Statistics and Data Thinking
Types of data, Collecting data, Sampling
Describing and Charting Data Sets
Arrangement, pre-processing and representation of data, Measures of central tendency (mode, median, mean), Measures of dispersion (range, variance, standard deviation) Statistical graphics & visuals (e.g., box-plot, histograms)
Introduction to Probability
Sample points, sample space, events, Calculating probabilities, Venn diagrams, Combinatorial mathematics
Hypothesis Testing
Null/Alternative Hypothesis, Single sample z test, One-tail tests, Two-tail tests
Independent (unpaired) Samples Test
Test for Equality of Variance, Student’s t-Test (independent samples)
Dependent (paired) Samples Test
Student’s t-Test (dependent samples)
One-Way Analysis of Variance (ANOVA)
One-way ANOVA, Post Hoc tests
Goodness of Fit
Chi squared (χ2) test for independence, Observed vs Expected values
Time Series Analysis
Simple Moving Average, Weighted Moving Average, Exponential Smoothing, Hot Winters Method
Correlation
Pearson’s correlation coefficient, Scatter Diagrams
Linear Regression
Prediction, Simple Linear Regression, Multiple Linear Regression
Principal Component Analysis
Dimension reduction, Defining clusters, Multivariate analysis
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be included by the provision of exercises and short answer questions during weekly tutorials. Feedback will be provided individually or as a group in written and/or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning. Apprentices will be encouraged to share exercises for peer review – in particular for data visualizations.
Assessment Type: CA 1 (0380) % of total: 25
Assessment Date: n/a Outcome addressed: 1,2,5
Non-Marked: No
Assessment Description:
The first test will assess apprentices’ knowledge and understanding of descriptive statistics and basic probability.
Assessment Type: CA 2 (0390) % of total: 25
Assessment Date: n/a Outcome addressed: 2,3,5
Non-Marked: No
Assessment Description:
The second test will assess apprentices’ knowledge and understanding of setting null and alternative hypotheses for single sample and two sample statistical tests. Apprentices will also be required in the test to calculate test statistics (z and t), and to report on results
Assessment Type: CA 3 (0420) % of total: 25
Assessment Date: n/a Outcome addressed: 3,5
Non-Marked: No
Assessment Description:
The third test will assess apprentices’ knowledge and understanding of three statistical tests (ANOVA, Chi-Square, and Time Series Analysis).
Assessment Type: Continuous Assessment (0200) % of total: 25
Assessment Date: n/a Outcome addressed: 4,5
Non-Marked: No
Assessment Description:
The final test will assess apprentices’ knowledge and understanding of Linear Regression and Principal Component Analysis.
No End of Module Assessment
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
 

Module Resources

Recommended Book Resources
  • Neil J. Salkind. (2014), Statistics for People Who (Think They) Hate Statistics, 4th Edition. Sage Publications, Inc Thousand Oaks.
  • Cortinhas, C. and Black, K.. (2012), Statistics for Business and Economics, 1st European Edition. John Wiley & Sons.
  • McClave, James & Sincich, Terry. (2012), 12th edition, Pearson.
Supplementary Book Resources
  • Andy Field. (2013), Discovering Statistics Using IBM SPSS Statistics, 4th Edition. Sage Publications Inc London.
  • McClave, James T., Benson, George & Sincich, Terry. (2013), Statistics for Business and Economics, 12th Edition. Prentice Hall.
  • Frederick L. Coolidge,. (2012), Statistics, Sage Publications.
  • Wayne L. Winston. (2014), Microsoft Excel 2013, Microsoft Press.
  • Timothy C. Urdan. (2016), Statistics in Plain English, 4th Ed.
This module does not have any article/paper resources
Other Resources
Discussion Note: