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As such, this is a point in time view of data which will be refreshed periodically.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
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Coursework Only |
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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 “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 | |||||||||||||||||||||||||||||||||||||||||||
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
Probability- Basic Probability - Discrete Probability Distributions; Binomial Distribution, Poisson Distribution - Normal DistributionStatistical Estimation- Sampling - Sample Error - Confidence IntervalsStatistical/Hypothesis Testing- What is a Statistical Test? - Steps in involved in a Statistical Test - Independent Sample t-test, One WAY ANOVA, Chi-Square TestRegression and Forecasting / Predictive Analytics- Times Series Models - Linear Regression - Cause and EffectData Management and DatabasesIntroduction to Databases - Open Source Relationship Database based on SQL. Tables, Relationships, Joins, Subqueries, Regular Expressions.Big Data and Big Data ManagementOverview of "Big Data" and Big Data ManagementAdvanced MS EXCELFinance 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 MacrosSpecialism Options 1 - Web AnalyticsWhy 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 campaignsSpecialism Options - Financial Modelling using MS EXCELFinance 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 ValuesSpecialism Options - Building Models using MS EXCELBusiness 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 EXCELSpecialism Option - Programming in PythonBasic Introduction to Python Course - Open source object orientated programming language with many Data Analytics applications.Specialism Option - Introduction to RBasic Introduction to R Open source programming language used extensively in Statistical Analysis and Data Analytics | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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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, | |||||||||||||||||||||||||||||||||||||||||||