Milestone Due Dates

Abstract (5 pts)	Sunday Feb. 06 Feb. 13, 11.59pm, on Colab, Submit your Colab URL to Piazza Thead
Progress Checks I and II (10 pts)	March 06   March. 13, April 10
Final Report (20pts) and YouTube Presenation/Demo (5 pts)	April 24, 11.59pm, Colab and Youtube

Introduction

The data project is an opportunity to tackle a more challenging data science activity. For the project, you will work in a team of 2-3 students on a problem of your choosing that is interesting, significant, and relevant to data science. The ultimate goal of your course project is to tackle some interesting real-world problem.  All members of a group will receive the same grade on group work. Therefore, it is in your interest to choose other group member (ideally, first week of the class) who have the same goal in the class as you do. It is also in your interest to work together and ensure that all tasks are completed effectively. Your scores on group work may be adjusted based on your contribution. 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). You can utlize any rosources for this project, but I highly reocmmend using 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.

The assignment is flexible: choose a topic of interest to you and your group and carry out a cohesive, complete project based around it. The range of possible topics that you can choose among is broad. However, the project you pick should incorporate a dataset and wide range of data science techniques. I think the most interesting problems will be ones in which you identify and work with some "client" to develop a solution to one of their problems. Such clients can include organizations with which you are involved, work site, etc. You can propose to carry out a project as part of a larger effort. However the caution here is that you will need to be able to separate out the contribution made by this class' project from the rest.

You will need to prepare a written project abstract at Google Colab and get it approved by the instructor, continue your Colab to make project progress report, and use the same Colab to prepare a final report (including your code/snippets), and give a demo (YouTube link in the sam Colab). Peer-assessment: Individual student's grades for projects will be influenced by their teamwork as evaluated by their project group members. This will be applied as an overall weight to the term project grade.

Project Abstract

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

○    Each member name, email, web portfolio link in the very first lines of the Colab.

 ○    Raw Data  Source (avoid using pre-processed datasets like Kaggle, ask the instructor if you are unsure!)

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

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

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

○     Project Plan/ Gantt Chart.  Team member contribution plan (if a team project)

●     You need to have an acceptable abstract submitted by the deadline. Without abstract you'll recive zero for your project grade. 

●     Submit your Colab link to Piazza thread.

Project Progress Checks I and II (continue using same report at Colab)

In this progress checks, you should assess the progress you are making on your project and update the work plan as necessary. Continue your earlier Colab document documenting your progress towards the project.Start with your proposal or previous progress report (if any) and add the following content to your progress report .

The progress should inform about, 

• Design decisions,
• The target audience for the project (who the users are)
• What tools/technologies will you use and why?
• Data Models  - optional
• System architecture and implementation details (if any) - optional
• What is the overall design? What are the core features? 
• How does the requirements/design satisfy the needs of your users?
• Screen shots of the user interface or a prototype (if any). 
• Code snippets or if Colab project, then complete code segments.
• Current Status
• What is the current status of the implementation?
• What is left to do?
• Complete the report assuming that ultimately this will be your final project report submission.
  

Project Presentation (10 Minutes Video)

Use Zoom (you have access to zoom pro via ODU https://www.odu.edu/ts/collaboration-tools/zoom) or any other video recording tool to record a 10 minute or less (2-3 pts penelty will be applied if more than 10 minutes) video of your project work and upload it to YouTube. You can show your implementation/demo (use the screenshare option) and also your presentation slides or Google Colab. Your presentation/Demo should succinctly tell us *why* we should care and *what* interesting insight you have about the chosen data project. 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.

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 very first in your Summary Section of the Colab or have it in your very first slide. 
• 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 punch line first, tell it like a story, with a beginning, middle and an end. It’s not easy to condense your project into a short presentation, 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.
• Engage your audience!
• Embed your YouTube Video in your Colab at the Summary Section.

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.    

• This is the continuation of your same Google Colab project document (Abstract, Progress checks). 
• Project Title
• Team member name, email, web portfolio link in the very first lines of the Colab.
• 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