Students organize groups of up to 4 people. This group project offers students an opportunity to explore a frontier BI topic that is of interest and to apply your BI knowledge to real business for the managerially relevant and/or technically valuable insights. For a group project, each group must conduct (1) business case analysis (or BI technique analysis) or (2) data analysis. For example, for the option (1), any business which is expected to achieve success by its choice of relevant BI technology or which already has shown the success (or crushing defeat) via BI technology is relevant. Also, any Business Intelligence techniques or tools are relevant to study for the project.
Selectively, for the option (2), your group can choose to collect data and conduct your own analysis, in which you must provide the reasonable analysis in depth and professional business insight. If you decide to do so, you must notify your detailed plan to the instructor before the 1 page proposal submission.
We cover basics of BI in class while there are numerous frontier BI topics and applications that might interest you.
You will extend the breadth of your knowledge by studying a BI topic in depth and later share your knowledge on this topic with the class in case you choose the option (1). The objective of the option (2) is for your group to walk through the data mining process using real-life data that is of interest to you.
Possible BI topics for option 1 :
Think wide and ask yourself;, e.g., can we trust the so-called big data analysis?, what’s new to the business intelligence?, what is the sophisticated measure to evaluate the effectiveness/efficiency?, what are the imminent issues about big data?, what are the current/future trend regarding big data analysis and business intelligence?, what are the skeptical views and are they valid?, what do we learn from the cases (successful examples and terrible failure)?, and etc.
• Find any BI topic you find interesting. You may need search (e.g., kdnuggest.com)
• Suggested topics (but not limited to): o Evaluation of recommendation systems (e.g., Netflix and Amazon) o BI in cloud computing o BI in SaaS (software as service), on-demand computing on CRM (e.g. Salesforce business model) o BI using user-generated contents (e.g. twitter, facebook, consumer reviews) o Personalization service opportunities using BI o BI in healthcare o BI in sports o Security issue for BI -- Credit card fraud or profiling in airport security o BI in education -- what drives success for students or schools?
Possible Data sources for option 2:
o First-hand data (i.e., data directly from your job or your connections). If you choose first-hand data, make sure your employer gives you clearance. Keep the dataset as simple as possible (as the less headache in the data, the more you can focus on BI). You should have a clear idea on your data mining objective – e.g., association rules, clustering, or classification/prediction.
o Second-hand data. Browse any online data source you are aware of for available datasets. Pick one that is not only interesting, but also understandable to you.
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Publicly Available Dataset you may want to consider for your project:
o https://data.medicare.gov/
o http://www.kdnuggets.com/datasets/index.html
o http://archive.ics.uci.edu/ml/
o http://lib.stat.cmu.edu/DASL/allsubjects.html
Deliverables :
• One-page proposal : Explain your chosen topic/analysis. Provide a brief summary of your search on this topic/analysis (Your decision may be made based on particular reasons. Provide your basic information), especially on the business value of this topic (Your future job interviewer will not irk when seeing it on your resume). Also show how it may benefit your career. Make sure your group finds it interesting and useful!
• Final report : This should be a professionally prepared report that addresses the following parts: cover page, executive summary, project motivation, introduction to the chosen BI topic or data analysis, in-depth description of the topic in terms of its business applications and business value (or in-depth data analysis results and interpretation),
BI techniques used (get this part as clear and detailed as you can), a case study if you have, software and data if you conduct data analysis, your view on remaining issues on this topic (and your suggested solutions if any), your conclusion (including what you learned in this project), and references. Feel free to add other sections if needed. If you feel any of the above required parts should not be included in the final report, you need to get my prior approval by explaining why. There is page limit ( no more than 15 ), and concise writing is encouraged.
• Presentation and the slide : See later part for my general requirement on presentation. This is the opportunity for you to share your knowledge with your classmates, so focus on what's new and valuable to this course.
Evaluations
• One-page proposal : 3 points max. No needs to revise/resubmit the proposal (or to choose a new one) as directed (the revision can be shown in your final report). No penalty is given unless the group apparently does not do a good homework before putting the proposal together, or the proposal is too ambitious to accomplish in a module.
Penalty is imposed for missing the due date or for extremely sloppy proposal.
• Final report : 12 points max for this final report. If the due date is missed, report receives 0 point. Penalty is imposed on missing parts, poor analysis, and on messy writing, and also includes the penalty already imposed on the previous deliveries if any.
• Presentation : 5 points max. Submission of presentation slide is required. Without submission, you receive 0 point regardless of presentation delivery. Factors considered for presentation: o Is the presentation logical and interesting? Are the visuals presented clearly? o Do the speakers look confident and prepared? o Is clear, meaningful conclusion/take-away provided?
Note: Students can consult with me in the beginning stage to determine whether the project is adequate for the course. Analysis should be sufficient in depth for either/both business perspective or/and technology perspective.
Note: No hand-written reports are accepted; it must be typed legibly (e.g., Microsoft Word). Assignments or project reports should be submitted only through course website . Submission via other formats or media is NOT allowed.
Report
All written reports must be submitted online on the corresponding Group Project section of the course Sakai website on the specified date and time. Late submissions will NOT be accepted and extensions or exceptions will NOT be made. Any edits made to content after the posted deadlines will not be graded and may result in penalties.
Submission of the presentation slide is required. No printed reports are required for any part of the group project. It is important that
• Your report should be written in a professional manner in clear and concise English.
• All sources must be cited according to the APA format (guidelines available at http://www.cws.illinois.edu/workshop/writers/citation/apa/ ). This is a very important aspect of report writing. If you include information (text or figures) on your report that has not been cited appropriately, this is a case of plagiarism and the entire group will have to face the consequences. The Turning system in Sakai MUST be used to detect plagiarism. If your project report is found to violate the University’s academic honesty guidelines, all members of the group will be held responsible for the same, and the entire group will receive 0 credit for the entire project grade, and additional action will be taken to report the incident to the department and dean of students.
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