Project
This is the final project for the course. You can work in teams of 1-3 to complete a data science project of your choosing.
There are multiple graded components to the project that will sum up to a final project grade. The project is worth 100 points total. Here are the points breakdown for each component.
Proposal : 25 points
Code / Github Contents: 25 points
Project Report: 25 Points
3-5 Minute Video Presentation: 25 Points
Project Proposal¶
Due AoE, Thursday, 28 April
The proposal is split into 3 sections:
Overview:¶
Think of the overview section as the equivalent of an abstract in a research paper or an elevator pitch for the project. The following questions will help you frame your thoughts if you ever have to succinctly describe your project in an interview:
Title: should capture the topic/theme of your project.
Objective: In 1 to 2 sentences, succinctly describe what you are hoping to accomplish in this project in simple, non technical English.
Importance: In 1 to 2 sentences, describe why this project has personal significance to you.
Prior work:¶
What will you be basing your visualisation on? Go to D3 Graph Gallery and find a visualisation that intrests you. It should not be a simple line/bar plot but instead something unique that will give a insight that a regular visualisation would not.
Your visualisation must use either D3, or NetworkX.
Make sure to cite the work as something that you use as a base for building your own visualisation.
Data:¶
With your visualisation in mind, find some data that interests you that would provide some intresting results under the visualisation of your choice. You can use any of the open data platforms we discussed in class such as:
and so forth.
Make sure to comment on the following:
Data Source: Include a list of your planned data source(s), complete with URL(s) for downloading. All data must be publicly available.
Data Volume: How many columns in your dataset? How many rows? If you are joining multiple datasets together, please tell us how many rows and columns remain after the data has been merged into a single dataset.
Data Richness: What type of data is in your dataset? You don’t need to describe every column. A generalized overview is fine. (e.g. “My data contains 311 complaint types, the date the complaints are created and closed, as well as a description of the complaint”). If you found a data dictionary, feel free to link us to that as well.