Students will prepare a data analysis and paper for the course. Students will need to download a data set which includes important information for decision makers, then perform an analysis of the data, being sure to include appropriate statistical procedures for the types of data included. The analysis should demonstrate your knowledge of the course material (descriptive statistics, data visualization, linear regression or time series analysis, and/or data mining) and must be practical to decision makers. A short statement of the hypothesis/problem under study should be included, with literature cited where appropriate.
Each student should submit a one-page abstract, stating the title, purpose, and a tentative table of contents for their paper by no later than Monday, February 14th. If time allows, students will discuss with the class their progress sometime afterwards. Each student will provide a 5-minute presentation of their work to the class website on the last day of class (March 9th).
The 7-paper page should be double spaced and typed and submitted in electronic format. All ideas, quotes, etc., must be referenced. When in doubt, reference! Although the paper should be proofread for grammar and punctuation, the paper will be graded mostly on content and development of ideas. You don’t need a cover page. The paper should show that learning took place for this course. You need to develop original ideas synthesized with the ideas of others and supported with scholarly references, but don’t regurgitate material from another work. Real world examples make the paper more relevant. Give the name of companies with dates and results.
For the course project I’m asking for a 7-page paper and 5-minute ppt slide presentation with voice-over (I AM LOOKING FOR A WOMAN VOICE) and closed captioning (can be in notes section below slides). You don’t have to use ppt. Other software will work. And it could be an mp4 file or Zoom recording. Please keep in to 5 minutes.
Here are some things I will be looking for:
Clarity of graphics and relevant visualizations of the data/analyses
Display is orderly, logical, and appealing
Data analysis takes into account the data types
Choice of data analytic methods is justified and clearly described
Appropriate application and documentation of methods
Analytic method/model assumptions are clearly stated and satisfied
Conclusions/interpretations are consistent with output
Ideas for future analysis
Creative use of statistical analysis, visualizations or presentation of results.
Clearly, you won’t be able to go into a lot of detail in 7 pages, so you can’t cover everything and conciseness counts.