Project
Hands-on Data Science Project
Students in EEPS 1960D will complete a data science project as individuals or teams of up to 3 students. Projects should apply the techniques from the course (exploratory data analysis, predictive modeling, and/or big data analysis) to a real-world data set. Students may propose a novel project or choose to replicate and extend the results of a peer-reviewed study. Projects focused on topics in Earth, Environmental or Planetary sciences are welcomed, but NOT required. Resources for selecting a project and identifying data sets are provided by the instructor below. Theory or methodological projects may also be permitted, subject to approval by the instructor.
Student projects for Spring 2021 can be viewed here
Project Requirements
Project Milestones
Milestone #0: Brainstorming [Part A due Feb 8th, Part B due Feb 11th]
Milestone #1: Project Proposal [due Feb 25th]
Milestone #2: Exploratory Data Analysis [due March 11th]
Milestone #3: Initial Draft [due April 1st]
Project Report
Project due date: April 22nd @ 11:59am ET
All students must submit: a Project Coversheet, which includes four written questions about the project
Each project team must submit: a Project Report (see Project Guidelines)
Grading Rubric for the project
Getting Started
Start by familiarizing yourself with the Project Guidelines and Grading Rubric
Formulating a project
Watching this video: The 7 steps of machine learning [10 minutes] by Yufeng Guo @ Google Cloud
Recommended module: Introduction to Machine Learning Problem Framing module (developed by Google)
This module is designed to help you define a machine learning problem and propose a solution. Although designed for professionals in industry, this module is relevant for anyone starting a data science project. This module should take < 1 hour to complete.
Acquiring data
Explore the list of data sets and data resources related to Earth sciences and other topics. The list is only meant as a resource; students are also welcome to use data from other sources in their projects.
Recommended video:
[7 minutes] How to Create a Dataset for Machine Learning by Jordan Harrod
Working with image data
Code example: Extracting image features using pretrained neural networks (Brown Only)
Additional Resources
Collaborating on a Group Project
Group Project Tools (Eberly Center @ CMU): including team roles and team contracts
Collaborative writing tools: Overleaf or Google Docs