Module: Applied Business Analytics

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Applied Business Analytics
Module Code	MT224 (ITS) / BAA1015 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Gerard Conyngham
Module Teachers	Cliona Mcparland
NFQ level	8	Credit Rating	10
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.Students are also introduced to databases and data management and given an introduction to SQL data querying language. In Semester 2 students are introduced to the developing role and applications of Data Analytics and given an overview of the Data Analytics function in data driven organisations. A unique feature of the module is the option to choose from a selection of topics in the second semester. This will vary from topics on developing a Data Analytics strategy to more technical topics like learning a program language like Python. Options will also include specialist topics linked to marketing, accountng, finance, aviation and other specialsims.

Learning Outcomes

1. Provide an overview of the 'Data Analytics Function” in an organisation and how it links to other functions and gain an insight into the applications of data analytics in increasing data driven businesses. .
2. Explain the nature of sample error and calculate this error for a number of sample parameters
3. Choose the appropriate statistical techniques for testing a variety of statistical hypotheses
4. Build a basic Predictive Analytics model using Linear Regression and test assumptions and limitations of these models
5. Explain the key concepts in Managing Data and databases and use SQL to create basic database queries
6. Develop key analytics skills in their own chosen specialism (e.g Aviation, Marketing, Finance, Accounting,...)
7. Use TABLEAU to create data visualisations and a customised dashboard
8. Use advanced modelling skills in MS EXCEL

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Online activity	30	TABLEAU online training and assignmnet
Lecture	40	No Description
Portfolio Preparation	20	Reflective E-Portfolio outlining the development of students Data Analytics skills with links to students future career needs.
Online activity	50	Self Directed Specialist Online Training
Assignment Completion	40	Statistics Data Analysis Assignment
Online activity	40	Weekly Online Exercises
Online activity	30	SQL Online training and Assignment
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

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

Statistical Estimation
- Sampling - Sample Error - Confidence Intervals

Statistical/Hypothesis Testing
- What is a Statistical Test? - Steps in involved in a Statistical Test - Independent Sample t-test, One WAY ANOVA, Chi-Square Test

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

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
Overview of "Big Data" and Big Data Management

Advanced MS EXCEL
Finance Functions and Introduction to Valuation using MS EXCEL Calculating Present Values, Calculating NPV, Calculating IRR using MS EXCEL, Investing with Loans, Market Based Valuation and Multiples, Growth Rates and Terminal Values Creating Dashboards in MS EXCEL Introduction to MS EXCEL Macros

Specialism Options 1 - Web Analytics
Why digital analytics? How Google Analytics works, Google Analytics setup, How to set up views with filters. The Google Analytics Interface; Navigating Google Analytics, Understanding overview reports, Understanding full reports, How to share reports, How to set up dashboards and shortcuts. Basic Reports: Audience reports, Acquisition reports, Behavior reports. Campaign and Conversion Tracking, How to measure Custom Campaign, Tracking campaigns with the URL Builder, Use Goals to measure business objectives, How to measure Google Ads campaigns

Specialism Options - Financial Modelling using MS EXCEL
Finance Functions and Introduction to Valuation using MS EXCEL Calculating Present Values, Calculating NPV, Calculating IRR using MS EXCEL, Investing with Loans, Market Based Valuation and Multiples, Growth Rates and Terminal Values

Specialism Options - Building Models using MS EXCEL
Business Models in MS EXCEL Case Study: Building a basic Pricing Model for an Airline using MS EXCEL using Goalseek and SOLVER. Forward Looking Business Models using MS EXCEL Building a Model in MS EXCEL using Decision Trees and Scenario Analysis Discrete Event Simulation Model, Case Study; Overbooking on a Flight using MS EXCEL

Specialism Option - Programming in Python
Basic Introduction to Python Course - Open source object orientated programming language with many Data Analytics applications.

Specialism Option - Introduction to R
Basic Introduction to R Open source programming language used extensively in Statistical Analysis and Data Analytics

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Portfolio	E-Portfolio where students demonstrate the Data Analytics skills gained over two years with links to their chosen specialism and future career plan.	20%	Sem 2 End
Assignment	Case Study applying variety of statistical techniques to a real world dataset.	20%	n/a
Group project	Using Tableau to create a Data Visualisation Dashboard	10%	n/a
Completion of online activity	Completion of Online Specialised Training and associated task, for example - Building a Financial// Operations Management Model in MS EXCEL - Completing Google Analytics / Web Analytics training - Completing training in Python Programming .......	30%	n/a
Digital Project	SQL Database Query Assignment	10%	n/a
Participation	Participation / Weekly Exercises	10%	n/a

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

43894, Online Training, Sage, 0, Sage Methods, https://classroom.sagepub.com/, 43895, Online Training, Kubicle, 0, Learn data analysis skills for the future of work, www.kubicle.com, 43950, E Book, Lex Holmes, Barbara Illowsky, Susan Dean, 2017, Introductory Business Statistics, OpenStax, https://open.umn.edu/opentextbooks/textbooks/introductory-business-statistics-2017, 43906, E Book, Thomas K. Tiemann, 2010, Introductory Business Statistics, BCcampus, https://open.umn.edu/opentextbooks/textbooks/introductory-business-statistics, 43912, E Book, Cole Nussbaumer Knaflic,, 2015, Storytelling with Data : A Data Visualization Guide for Business Professionals, ,, John Wiley & Sons,