Registry

Module Specifications

Archived Version 2022 - 2023

Module Title
Module Code
School
Online Module Resources
NFQ level	9	Credit Rating	10
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The main objective of this module is to develop the student's understanding and practical skills of basic statistical methods for Business Analytics. The lectures and tutorials will also develop student's ability to interpret the result provided a statistical model and to evaluate its performance. Students will be initially exposed to introductory statistical concepts and then to more advanced techniques. The course will be delivered with a practical approach and will cover the following topics: Basic statistical concepts, Statistical Analysis using R and SPSS, Linear Regression, and Logistic Regression.

Learning Outcomes

1. Understand the importance of statistical analysis in current business environment and how organisations can make use of it.
2. Understand the characteristics of different types of data and the differences between structured and unstructured data.
3. Identify and apply appropriate statistical techniques for gathering valuable business insights.
4. Interpret statistical output to drive business decisions.
5. Effectively present the output of statistical analyses.
6. Evaluate the fit-for-purpose of a statistical model and its analytical and predictive performance.
7. Implement statistical analysis and generate statistical reports.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	25	Class or online lectures
Tutorial	50	Completion of online tutorials or attendance at guest lectures, seminars or tutorials on general and discipline-specific topics.
Assignment Completion	75	Individual assignment design and completion.
Group work	50	Working on group assignment
Independent Study	50	Preparation for class and tutorials
Total Workload: 250

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Statistical analysis for business purposes.
Why is important to perform to statistical analysis? When and how do managers adopt it?

Business Analysis Planning and Monitoring
Organise and coordinate the efforts of business analysts and stakeholders, and identify business analysis performance improvements.

Elicitation and Collaboration
Stakeholders engagement and communication in the context of business analytics projects.

Requirements Analysis and Design Definition
Organise, specify and model requirements and designs, validate and verify information, and identify solution options that meet business needs.

Basic statistical concepts
A refresher of basic statistical concepts that will be useful in the rest of the course such as: data types, sampling, descriptive statistics, hypotheses testing, and confidence intervals.

Statistical Analysis using R and SPSS
R interface, import/export of data, vectors, variables attributes, basic data manipulation, and basic commands for univariate and multivariate analysis.

Linear Regression
Univariate and multivariate analysis, OLS estimation, model evaluation, heteroscedasticity, multicollinearity, and other factors that might bias the results.

Logistic Regression
Logit and Probit models, basic assumptions, applicability, and maximum likelihood estimation.

Assessment Breakdown
Continuous Assessment	%	Examination Weight	%

Type	Description	% of total	Assessment Date
Course Work Breakdown

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
Unavailable

Indicative Reading List

IIBA: 2015, A Guide to the Business Analysis Body of Knowledge, 3, 4, 7, 10, 11,
Ohri, A: 2013, R for Business Analytics, Springer, 978-1-4614-43
Johannes Ledolter: 2013, Data Mining and Business Analytics with R, Wiley, 978-1-118-447
Professor Melissa A Hardy (Editor), Professor Alan Bryman (Editor): 2009, Handbook of Data Analysis, Sage Publications Ltd, 9781848601161
Robert A. Stine,Dean P. Foster: 2017, Statistics for Business: Decision Making and Analysis, Pearson, 0134497163

Other Resources

27515, Software, 0, R and R Studio,

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