AIM(S)
The success of modern information systems is dependent upon the development of the databases upon which modern information systems are built. This module will provide students with a comprehensive understanding of how data systems work, how their benefits can be optimised and how to analyse and evaluate the information in the systems in order to obtain maximum benefit.
LEARNING OUTCOMES
Upon the successful completion of this module, the student should be able to demonstrate the ability to:
- Identify and critically evaluate the current trends in data warehousing, business intelligence and data mining.
- Demonstrate a comprehensive knowledge and systematic understanding of essential concepts and principles by using predicative analytic software.
INDICATIVE CONTENT
- Knowledge management fundamentals
- Database systems and management
- Data governance, architecture and analysis
- Data security and quality management
- Data warehousing and business intelligence
- Practical issues in data management
- Online Analytic Processing (OLAP)
- The Data Mining Cycle
- Reporting
BIBLIOGRAPHY
Essential
Sharda, R., Dursun, D. and Turban, E., (2014) Business Intelligence and Analytics, Systems for Decision Support, 10th Ed., Pearson, Boston, MA.
Sharda, R., Dursun, D. and Turban, E., (2014) Business Intelligence, A Managerial Perspective, 3rd Ed., Pearson, Boston, MA.
Recommended
Blenkhorn, D.L. and Fleisher, C.S. (Eds.), (2005) Competitive Intelligence and Global Business, Westport, Conn.
Connolly, T. and Begg, C (2014), Database Systems: a Practical Approach to Design, Implementation, and Management, 5th Ed., Pearson, Boston, MA.
Collier, K.W., (2011) Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing: Delivering the Promise of Business Intelligence, Addison-Wesley
Han, J., Pei, J., Kamber, M. (2011) Data Mining: Concepts and Techniques, London
Howson, C., (2013), Successful Business Intelligence: Secrets to Making BI a Killer App, McGraw-Hill Osborne
Ishikawa, A. and Nakagawa, J. (2013) Introduction to Knowledge Information Strategy, An: From Business Intelligence to Knowledge Sciences, World Scientific Publishing
Laursen, G.H.N. and Thorlund, J. (2010) Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, Winchester, John Wiley & Sons
Raisinghani, M.S (Ed.), (2004 ) Business Intelligence In The Digital Economy:
Opportunities, Limitations And Risks, Hershey, Pa. : Idea ; London
Refaat, M. (2006) Data Preparation for Data Mining Using SAS, Morgan Kaufmann
Sabherwal, R. and Becerra-Fernandez, I. (2010) Business Intelligence, , John Wiley and Sons: Chichester
Van der Lans, R. (2012) Data Virtualization for Business Intelligence Systems, Morgan
Websites
http://www.businessweek.com/
http://www.ecommerceexpo.co.uk/
https://www.gov.uk/government/publications
http://ec.europa.eu/internal_market/e-commerce/directive/index_en.htm
http://www.ictparliament.org/legislationlibrary/e-Commerce
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DRAFT – BACHELORS DEGREE ASSIGNMENT SPECIFICATION |
| Student name: | Student P number: | ||
| Programme: | BA Business Portfolio | ||
| Module: | Data Handling and Business Information | Module Level (4, 5, 6): | 5 |
| Module code: | SBUS5105 |
Contribution to OverallModule Assessment (%): |
100% |
| Lecturer: | Chris Thomas | Internal Verifier: | Dr Roisin Mullins |
| Assignment Titles: |
|
Assignment No (x of x): | 1 & 2 |
| Hand Out Date: | January 2016 | Submission deadline: | 1. 21st April 2016
Feedback by 9th May 2. 12th May 2016 Feedback by 9th June |
| Referencing: | In the main body of your submission you must give credit to authors on whose research your work is based. Append to your submission a reference list that indicates the books, articles, etc. that you have read or quoted in order to complete this assignment (e.g. for books: surname of author and initials, year of publication, title of book, edition, publisher: place of publication). | ||
| Disclosure:
|
I declare that this assignment is all my own work and that I have acknowledged all materials used from the published or unpublished works of other people. All references have been duly cited. | ||
| Student’s Signature: | (Only where hard copies are required) | Date: |
|
| Turnitin: All assignments must be submitted to Turnitin unless otherwise instructed by the Lecturer.
Note: the Turnitin version is the primary submission and acts as a receipt for the student. Late submission of the electronic version of the assignment will result in a late penalty mark. Penalties for late submission: Up to four weeks late, maximum mark of 40%. Over four weeks late, Refer. Only the Extenuating Circumstances Panel may grant an extension. |
| Learning Outcomes tested
(from module syllabus) |
Assessment Criteria. To achieve each outcome a student must demonstrate the ability to: | |
| Identify and critically evaluate the current trends in data warehousing, business intelligence and data mining. |
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· |
· Explore the use of a data mining tool for predicative analysis for various business scenarios.
· Produce a written report |
- Please submit the assignment parts in a suitable report folder– not in polypockets.
- This form (ALL PAGES) MUST be at the front of the paper submission. Assignments will not be accepted without this form as it is a requirement that you sign the disclosure regarding referencing convention.
- DO NOT put this form into Turnitin or it will match many similarities with other students’ submissions.
TASK 1 DESCRIPTION |
Assessment Component 1 – 75%
You are a data analytics assistant, responsible for interpreting data from a company’s website. You use traditional software packages such as Excel for day-to-day analysis and more specialised data for mining the data.
Part 1 – 50%
Using the Superstore Sales.xls data set provided, critically evaluate the strengths of using Excel for pre-processing the data, analysing the data and visualising the data.
You will also need to demonstrate how you can do this practically with the use of Excel functions such as: IF, LOOKUP, PIVOT TABLES, charts and graphs. (1500 words)
Part 2 – 50%
Using the same data set explain the data mining approaches that might be employed to find out more about customers using more sophisticated environment such as WEKA. (1500 words)
Scenario
The data analytics company you work for has been approached by ‘We Sell Things’ an online retailer operating in Canada. Over the past few years they have noticed a reduction in profits and are not sure why this should be the case.
‘We Sell Things’ has engaged your data analytics company to help them to analyse the information that they have and provide recommendations on how they can engage with analytics in the future to better inform their decision making process. ‘We Sell Things’ are looking for a solution that they can employ using their existing infrastructure which includes Excel records on sales which are populated daily by their sales system. They are prepared to look at data mining systems but cost is an issue.
Students should explore the use of excel to assess and analyse the problem and to investigate what WEKA can do for them.
The assignment submission should include the report along with the spreadsheet (and any other files prepared) showing your handling of the data, formulae and pivot tables etc. The combination of files should be in a zip file and uploaded to moodle.
