| Module Title |
Applied Business Analytics |
| Module Code |
BAA1015 (ITS: MT224) |
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Faculty |
DCU Business School |
School |
DCU Business School |
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NFQ level |
8 |
Credit Rating |
10 |
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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 Ethics in Data Analytics and Databases, Big Data and Data Management.
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.
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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. Develop the ability to idenitfy the role of analytics in their own business specialism and identify key analytical tools and skills required by professional in modern data driven organsations in their chosen field
3. Will gain an insight into the ethical and legal obligations and rights required when working with data, including the principles of data protection, GDPR and other data protection rules and ethics in data analytics.
4. Explain the nature of sample error and calculate this error for a number of sample parameters
5. Choose the and apply the appropriate statistical techniques for testing a variety of statistical hypotheses
6. Build a basic Predictive Analytics model using Linear Regression and test assumptions and limitations of these models
7. Explain the key concepts and tools in Managing Data and Databases, including 'Big Data', and use SQL to create basic database queries
8. Use advanced modelling tools including Simulation in MS EXCEL
9. Develop key analytics skills in their own chosen specialism (e.g Aviation, Marketing, Finance, Accounting,...)
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Online activity | 30 | Onlne Courses on Databases and Data Management , Big Data and Data Ethics | | Lecture | 25 | No Description | | Portfolio Preparation | 30 | Reflective E-Portfolio outlining the development of students Data Analytics skills with links to students future career needs. | | Online activity | 75 | Self Directed Specialist Online Training using Kubicle , Google Analytics, LinkedIn Learning and Other Tools | | Assignment Completion | 40 | Statistics Data Analysis Assignment | | Online activity | 40 | Weekly Online Exercises | | Workshop | 10 | Workshops on Jamovi Software and Business Modelling Tools using MS EXCEL |
| Total Workload: 250 |
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| Section Breakdown | | CRN | 10095 | Part of Term | Semester 1 & 2 | | Coursework | 0% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | Y | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Gerard Conyngham | Module Teacher | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Portfolio | E-Portfolio where students demonstrate the Data Analytics skills gained over two years with links to their chosen specialism and future career plan. | 40% | Sem 2 End | | Assignment | Case Study applying variety of statistical techniques to a real world dataset. | 20% | n/a | | Group project | Build a Simulation Model of a Process/System for a real world business applcation | 10% | n/a | | Digital Project | Probability and Confidence Interval Assignment | 15% | n/a | | Participation | Participation / Weekly Exercises | 15% | n/a |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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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
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Indicative Reading List
Books:
- 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,
Articles:
None |
Other Resources
- Online Training: LinkedIn, LinkedIn Learning, https://www.linkedin.com/learning
- Online Training: Kubicle, Learn data analysis skills for the future of work, www.kubicle.com
- E Book: Lex Holmes, Barbara Illowsky, Susan Dean, 2017, Introductory Business Statistics, OpenStax, https://open.umn.edu/opentextbooks/textbooks/introductory-business-statistics-2017
- E Book: Thomas K. Tiemann, 2010, Introductory Business Statistics, BCcampus, https://open.umn.edu/opentextbooks/textbooks/introductory-business-statistics
- E Book: Cole Nussbaumer Knaflic,, 2015, Storytelling with Data : A Data Visualization Guide for Business Professionals, ,, John Wiley & Sons,
- Online Training: Google, Google Academy, https://analytics.google.com/analytics/academy/
- Online Training: Google, Google Skillshop, https://skillshop.withgoogle.com/
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