DSS 583—DECISION MAKING TECHNIQUES
Formatting requirements for both paper options (#1 or #2): (5 pages (the body of paper excluding cover page and references)
1. 12-point font
2. Double-spaced
3. Proper citation of references using your preferred citation format
In Friedman’s book The World is Flat and in the video, he identifies ten flatteners: “the opening of the Berlin Wall, Netscape, work flow, outsourcing, offshoring, open-sourcing, insourcing, supply-chaining, in-forming, and the steroids” that have made the world flat. Additionally, he suggests rules and strategies companies should follow in order to “flourish” in the 21st century such as moving horizontally instead of vertically.
Search out two articles related to The World is Flat that interest you. (My definition of an article is at least five pages—if you find a good “article” that is only 1 or 2 pages—you can use it—just in the end you would need more than 2 articles.)
Read the articles and write a paper summarizing the articles. (NON-TECHNICAL).
Helpful Resources for World is Flat paper:
http://video.mit.edu/watch/the-world-is-flat-9145/
Please site the 2 articles found (related to Friedman’s book The World is Flat ) as well as the 3 videos listed above.
Here is some info on the course syllabus for DSS583-ECISION MAKING TECHNIQUES
COURSE OBJECTIVES
The aim of this course is to provide the student with a fundamental understanding of the potential and implementation of business analytics/business intelligence into an organization. To demonstrate this opportunity a few data analytics techniques will be examined, so as to provide some insight into how these tools may be used to analyze complex business problems and arrive at a rational solution. The techniques to be studied are modeling, regression, forecasting, and simulation. For each of the analysis techniques, the methodology will be developed and applied in a real business context. Cases of increasing complexity will be used to emphasize problem description, definition and formulation. The computer will be used extensively throughout the course, primarily by using available programs to perform the calculations after the problem has been correctly formulated including Excel (and if time permits JMP). Emphasis will be placed on the interpretation and implementation of results. Time will be spent visualizing the data in multiple formats including an introduction to Tableau. In addition, we will examine the forward-looking work of Davenport in the area of Competing on Analytics.
After completing this course, you should be able to:
Understand the data in the background and effectively build decision support models in Excel
Generate regression and forecasting models, know the key metrics that need to be interpreted and be able to clearly explain and discuss the output in a non-technical tone. It is imperative to consider managerial usefulness of the results from these models while comparing and contrasting results with the goal of identifying the most useful model.
Create data analytics models using excel and visual dashboards that together allow you to explain patterns and insights from the data and the managerial usefulness of the results from these models.
Communicate (written, orally, and visually) the results of quantitative analysis.
Be familiar with the process and issues to upgrading an organization using analytical concepts.
