Final Project Details

##FINAL PROJECT REQUIREMENTS:

Address a data-related problem in your professional field or in a field you're interested in. Pick a subject that you're passionate about; if you're strongly interested in the subject matter it'll be more fun for you and you'll probably produce a better project! Apply modeling techniques (regression, recommendation, classification, etc.) and data analysis principles (cross-validation, caution against overfitting, etc.) and report your results.

PRESENTATIONS:

All students are required to give a 5 – 7 minute presentation that summarizes their project. The presentations should target a non-technical audience and serve the purpose of having students practice the highly sought after communication skills that data scientists need.

What to cover in presentation:

• Overview of problem and hypothesis
• Overview of data
• Modeling techniques used and why
• What decisions your findings allow you to make.

###GRADING:

• EXCELLENT: Student's presentation is engaging, clear, and informative, describing the project, approach, and conclusions, and is suitable for a non-technical audience.

• GOOD: Student's presentation is as above but is either inadequately engaging, clear, or informative.

• FAIR: Student's presentation fails on two out of three of engaging, clear, and informative.

• POOR: Student's presentation fails on all three or is off-topic with respect to his or her paper.

##PAPER: (4 - 6 PAGES)

Students are also required to submit a 4 – 6 page paper that describes the project’s technical details. The paper should target a technical audience. What to cover in paper:

• Description of problem and hypothesis.
• Detailed description your data set.
• How did you decide what features to use in your analysis?
• What challenges did you face in terms of obtaining and organizing the data?
• Describe what kinds of statistical methods you used, and perhaps others you considered but did not use, and how you decided what to use.
• What business applications do your findings have?

###GRADING:

• EXCELLENT: Student's paper demonstrates thorough understanding of statistical techniques, data management, and the application of these in programming, and is clearly communicated to a reasonably technical audience.
• GOOD: Student's paper demonstrates above knowledge, but lacks some necessary rigor, detail, and/or exploratory depth or is not well communicated.
• FAIR: Student's paper demonstrates some learning of principles taught in class, but is clearly lacking in rigor and/or depth.
• POOR: Student's paper is incomplete or does not conclusively demonstrate understanding of statistics or programming.