Milestone Due Dates

Abstract	Friday, Sep. 27, On Google Colab, Submit your URL to Piazza	2 pts
Progress Checks	Oct 15, Nov 01, Nov.19	3 pts
Presentation/ Demo	Nov. 26 in Class, 3 minute talk presentation	5 pts
Final Report	Dec. 06 (Complete report in Colab)	5 pts

Introduction

The data project is an opportunity to tackle a more challenging data science activity. For the project, you are required to individually work on a dataset of your choosing that is interesting, significant, and relevant to Data Science. The ultimate goal of your data project is to apply the techniques learn in each week of the class towards your dataset (exploration, wrangling, machine learning, visualization). We are going to use Google Colab (Colaboratory) (https://colab.research.google.com/), a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser

Project Abstract

The abstract (in Googel Colab) should include the following information: 

○     Data  Source

• URL, 
• a short description
• first few records of the dataset (head())

○     Your end goal with this dataset (build a recommender system, prediction model/classifier, evaluaiton of models, visualizing something, infer something, or something else)

○     Any secondary datasets you are planning to utillize to augment your primary dataset (should be clearly specified that this is a secondary dataset)

●     You can take as much space as you need for the project abstract, but I would guess that most would be in the two-three page range. 

●     You need to have an acceptable abstract submitted by the deadline.

Project Presentation (3 Minute Talk)

Your presentation/Demo should briefly and succinctly tell us *why* we should care and *what* interesting insight you have about the chosen dataset. Give us some insight into the tough / cool / interesting aspects of your project. This is your time to shine, so carefully prepare what exactly you want to show off that will impress us in this summary. View the audience as potential upper management in your company -- so convince us that your problem is important, that you have the appropriate insight about the dataset.

During the 3 Minute Talk session, the author for each dataset should be prepared to present a less than three minute (180 second) preview talk about the main idea(s) of your project. The 3-minute time limit will be strictly enforced by a timer and buzzer. You'll be stopped right after the 3 minute mark whether you finish your summary talk or not!  Practice, practice, practice, and time your self before the presentation.

Follow the Guidelines preparing your Summary section for the talk (This should be at the very end of your Colab)

• Have a title of the project and your name at the verfy first in your Summary Section
• Have a clear outcome presented (charts, graphs, key conclusions)
• Know what you want your audience to take away from your presentation. Ideally, you would like the audience to leave with an understanding of what you’re doing and why you’re doing it.
• Tell a Story
• You may like to present your 3 Minute Talk like a story, with a beginning, middle and an end. It’s not easy to condense your project into three minutes, so you may find it easier to break your presentation down into smaller sections. Try writing an opener to catch the audience attention, then highlight your key findings, and finally have a summary to restate the importance of your work.
• Remember your peers are evaluating your presentation (5 pt is the weighted average of your peer score report). Engage your audience!

Project Final Report

A comprehensive report describing the project. This should be a "complete" document, so it should include front matter (title page, abstract, table of content, chapters), or a sidebar index that connect to your report elements. These should include problem statement, explain your design and implementation, results and evaluation. This report should stand by itself as the archival description of the project.   

 

• The is the continuation of your same Google Colab project document. 
• Colab file title should be "YourLastName_CS620_DataProject"
• Your results and evaluation (or evaluation strategy)
• What metrics used (or will you use) to evaluate the success of your project?
• Performance measures (how you measure them)?
• Other criteria?
• You should address the same questions as those you have addressed in the previous reports (abstract, progress checks), only with more details, especially regarding some of the challenges that you need to solve and your experimental results if any. 
• You should also include your conclusions from the study and point out how your work can be further extended (i.e., future work).
• References if available (this should be the very last section of your Colab)
• Provide as much context as you can—any kind of diagrams and illustrations that will make it easier for us to understand and evaluate your effort. Graphics are always helpful, you have probably heard the saying “a picture is worth a thousand words”!
• Do not just show the diagrams—for all figures, tables, charts, and diagrams provide some narrative discussion! Unfortunately, diagrams, particularly technical diagrams, are rarely if ever self-explanatory. You should document the alternative solutions that you considered as well as the arguments for the final choice. Diagrams only represent your final solution, but do not explain why you decided on this solutions and what alternatives were considered. Hence, all diagrams must be accompanied with explanation and discussion of alternatives and tradeoffs. Anything that could lead to ambiguity or misunderstanding on the reviewer’s part, should be clearly explained. Explanations should be written in prose and key arguments highlighted in bullet points. 
• There is no limit on the number of pages (or size) for the report. Of course, you should avoid stuffing your report with redundant or irrelevant material. 

Data sources for projects