Module: Business Analytics 2

Module Specifications.

Current Academic Year 2024 - 2025

All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).

As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.

Date posted: September 2024

Module Title	Business Analytics 2
Module Code	MT223 (ITS) / BAA1014 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Gerard Conyngham
Module Teachers	Andreas Robotis
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

Business Analytics 2 follows from Business Analytics 1 and uses a blended learning approach to develop students’ skills in the broad area of “Data Analytics”. In this module students will develop the core Data Analytics statistical skills and more advanced data visualization and MS EXCEL spreadsheet skills. They will also be introduced to Data Management and Databases

Learning Outcomes

1. Explain the nature of sample error and calculate this error for a number of sample parameters
2. Choose the appropriate statistical techniques for testing a variety of statistical hypotheses
3. Build a basic Predictive Analytics model using Linear Regression and test assumptions and limitations of these models
4. Use TABLEAU to create data visualisations and a customised dashboard
5. Use advanced modelling skills in MS EXCEL
6. Be able to explain the role and applications of analytics in data driven organisations

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	20	No Description
Online activity	50	Online learning - Data Analysis and Modelling Skills in MS EXCEL. Data Visualisation skills in TABLEAU, SQL Database Skills
Assignment Completion	20	No Description
Online activity	20	Weekly Exercises / Quizzes
Independent Study	15	No Description
Total Workload: 125

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

The role of Analytics in Organisations
Analytics function in an organisation, Building an effective Analytics Team, Applications of Analytics

Probability
- Basic Probability - Discrete Probability Distributions; Binomial Distribution, Poisson Distribution - Normal Distribution

Statistical Estimation
- Sampling - Sample Error - Confidence Intervals

Hypothesis / Statistical Tests
Purpose of a Statistical Test, Choosing the appropriate Test, Statistical Significance / P-Value

Regression and Forecasting / Predictive Analytics
- Times Series Models - Linear Regression - Cause and Effect

Building Models using MS EXCEL
Building a basic Pricing Model using Goalseek and SOLVER - Forward Looking Business Models - Building a Model using Decision Trees and Scenario Analysis Discrete Event Simulation Models

Data Management and Databases
Introduction to Databases - Open Source Relationship Database based on SQL. Tables, Relationships, Joins, Subqueries, Regular Expressions - Big Data and Big Data Management Tools

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Case Study applying variety of statistical techniques to a real world dataset.	40%	Week 12
Group project	Using Tableau to create a Data Visualisation Dashboard	20%	Week 6
Participation	Participation / Weekly Exercises	20%	Every Week
Digital Project	Build a Simulation Model in MS EXCEL	20%	Week 12

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Jaggia, Sanjiv: 2021, Business analytics: communicating with numbers,, McGraw Hill,
Tang Chunlei: 2016, The data industry: the business and economics of information and big data, Wiley,
Frye, Curtis.: 2016, Microsoft Excel, Microsoft Press,
EMC Education Services: 2015, Data science & big data analytics: discovering, analyzing, visualizing and presenting data, John Wiley and Sons,
Stephen L Nelson: 2016, EXCEL Data Analysis for Dummies,, Wiley,

Other Resources

43945, Online Training, Kubicle, 0, Learn data analysis skills for the future of work, http://www.kubicle.com, 43946, E Book:, Lex Holmes, Barbara Illowsky, Susan Dean, 2017, /introductory-business-statistics, Open Stax, https://open.umn.edu/opentextbooks/textb ooks/introductory-business-statistics-20 17, 43947, E Book:, Thomas K. Tiemann, 2010, Introductory Business Statistics,, BCcampus, https://open.umn.edu/opentextbooks/textb ooks/introductory-business-statistics, 43948, E Book, Cole Nussbaumer Knaflic, 2015, Storytelling with Data : A Data Visualization Guide for Business Professionals, ,, ,John Wiley & Sons,