Latest Module Specifications
Current Academic Year 2025 - 2026
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Description 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”. It begins by introducing students to the developing role and applications of Data Analytics in Business Functions and gives them an overview of the Data Analytics function in data driven organisations. In Semester 1 students will develop the core statistical skills and more advanced data visualization and MS EXCEL spreadsheet skills required for roles in modern data driven organisations. In the Second Semester the module builds students’ knowledge of Artificial Intelligence and its role in the key Business functions in an organisation. This includes using AI to do some basic Data Analysis. In addition, the module gives students the option to choose from a selection of Business Analytics topics in Semester 2. These will vary from topics on developing an Data Analytics strategy and Data Security and Ethics to more technical topics like learning Databases, Big Data, Python, Google Analytics, SQL,….). Options will also include specialist topics linked to Digital Business, Accounting, Finance and Aviation tailored to the three Degree Programmes currently taking the module. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. 1E495B46-81F6-0001-94AC-9E7512909980 2. 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. 5. 1 6. 1E495B46-8E15-0001-ECE5-EAFD16A017E7 7. Explain the nature of sample error and calculate this error for a number of sample parameters 10. 2 11. 1E495B46-97D9-0001-5D1D-1DF013C0103D 12. Choose the appropriate statistical techniques for testing a variety of statistical hypotheses 15. 3 16. 1E495B46-A139-0001-83ED-12596093121E 17. Build a basic Predictive Analytics model using Linear Regression and test assumptions and limitations of these models 20. 4 21. 1E495B46-E8B9-0001-2E7F-232015B42640 22. Explain the key concepts in Managing Data and databases and use SQL to create basic database queries 25. 5 26. 1E495B46-EBE8-0001-7F87-1F1718506AA0 27. Develop key analytics skills in their own chosen specialism (e.g Aviation, Marketing, Finance, Accounting,...) 30. 6 31. 1E495B46-F2E3-0001-BDC9-8926151051F0 32. Use TABLEAU to create data visualisations and a customised dashboard 35. 7 36. 1E495B46-F782-0001-464D-9B2011001664 37. Use advanced modelling skills in MS EXCEL 40. 8 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 |
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Indicative Content and Learning Activities
Business Analytics Overview Definition of Business Analytics, Key Steps in Business Analytics, Business Intelligence, Managing the Analytics Function; Data Strategy and Governance; Key Software Tools in Business Analytics Probability Basic Probability - Discrete Probability Distributions; Binomial Distribution, Poisson Distribution - Normal Distribution Statistical Estimation Sampling - Sample Error - Confidence Intervals Statistical/Hypothesis Testing Statistical Significance - Key Steps a Statistical Test - Independent Sample t-test, One WAY ANOVA, Chi-Square Test, Other Tests Regression and Forecasting / Predictive Analytics Times Series Models - Linear Regression - Cause and Effect - Predictive Analytics Data Visualisation and POWER BI Visual Data Thinking and Applying Data Visualisation Skills, Introduction to POWER BI, Creating Charts and Dashboards in POWER BI, Linking to Data Data Management and Databases Database Management Models; Data Access and Security; Data Ethics and Regulations Big Data and Big Data Management Definition of Big Data, The 5 Vs (Volume, Velocity, Variety, Veracity, and Value), Big Data Management Tools Artificial Intelligence What is Artificial Intelligence, Role of AI in business operations, AI Model Categories and Applications; Basic Data Analysis using Gemini 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 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. Specialism Option - Programming in SQL RDMS (Relationship Database Management Systems; Overview of SQL; Tables, Relationships, Joins, Subqueries, Regular Expressions in SQL. Specialism Options - Role of Analytics Role of Analytics in Accounting and Consultancy; Role of Analytics in Operations (Operations Research and Management Science); Role of Analytics in Aviation | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
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Other Resources
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