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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Analytics for Business 2
Module Code MT225
School DCUBS
Module Co-ordinatorSemester 1: Gerry Conyngham
Semester 2: Gerry Conyngham
Autumn: Gerry Conyngham
Module TeachersGerry Conyngham
NFQ level 8 Credit Rating 10
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

Business Analytics 2 follows from Business Analytics 1 and uses a blended learning approach to develop students’ skills in the broad area of “Business Analytics”. In this module students will develop the core statistical skills and more advanced data visualization and MS EXCEL spreadsheet skills required in data driven organsations. Students are also introduced to databases and data management and given an introduction to SQL data querying language. In the second half of the course 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 Business Analytics topics in the second half of the module. These will vary from topics on developing a Data Analytics strategy to more technical topics like learning a program language (Python or R). Options will also include specialist topics linked to marketing, accountng, finance, aviation and other specialsims.

Learning Outcomes

1. Provide an overview of the 'Business 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

Workload Full-time hours per semester
Type Hours Description
Online activity60Self Directed Specialist Online Training
Lecture40No Description
Online activity40Weekly Online Exercises
Independent Study40No Description
Online activity30Statistics Data Analysis Assignment
Portfolio Preparation20Reflective E-Portfolio outlining the development of students Data Analytics skills with links to students future career needs.
Assignment Completion20Business Modelling and Simulation 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

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 - Relationship Database Systems - SQL. Tables, Relationships, Joins, Subqueries, Regular Expressions.

Big Data and Big Data Management
Overview of "Big Data" and Big Data Management Tools

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

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

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentCase Study applying variety of statistical techniques to a real world dataset.35%n/a
Group project Business Modelling / Simulation. Building a model of a Business System in MS EXCEL10%n/a
PortfolioE-Portfolio where students demonstrate the Data Analytics skills gained over two years with links to their chosen specialism and future career plan.20%n/a
Digital ProjectCompletion 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 .......20%n/a
ParticipationParticipation and Weekly Exercises15%n/a
Reassessment Requirement Type
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
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,
  • EMC Education Services: 2015, Data science & big data analytics: discovering, analyzing, visualizing and presenting data,, John Wiley and Sons,
Other Resources

50198, Online Training, Kubicle, 0, Kubicle E-Learning: Learn Data Analysis Skills for the Future of Work, www.kubicle.com, 50199, Online Training, Datacamp, 0, Datacamp: Build Data Skills Online, www.datacamp.com, 50200, E-Book, Lex Holmes, Barbara Illowsky, Susan Dean, 2017, Introductory Business Statistics, OpenStax, https://open.umn.edu/opentextbooks/textb ooks/introductory-business-statistics, 50201, E-Book, Thomas K. Tiemann, 2010, 2010, Introductory Business Statistics, BCcampus, https://open.umn.edu/opentextbooks/textbooks/introductory-business-statistics, 50202, E-Book, Cole Nussbaumer Knaflic,, 2015, Storytelling with Data : A Data Visualization Guide for Business Professionals, John Wiley & Sons,
Programme or List of Programmes
AFBA in Accounting and Finance
EBCBA in Global Business (Canada)
EBFBA in Global Business (France)
EBGBA in Global Business (Germany)
EBSBA in Global Business (Spain)
EBTBA in Global Business (USA)

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