Overview
Description
Technology has driven rapid advancements in environmental sensing, but data science applications require careful field experiments and data management. Developing effective environmental monitoring requires knowledge across a variety of disciplines: field methods, electronics, computer programming, time series analysis, machine learning, and physical modelling. In this course, students will develop skills in these fields through hands-on activities and projects.
Units
Lecture2
Laboratory Studio1
Catalog Details
Offering
Offered: Every Fall
Terms
fall
Attributes
Standard
Learning Outcomes
- deploy an environmental sensor system by choosing sensors, programming dataloggers, estimating power needs, and understanding basic electronics in a hands-on setting.
- create a quality assurance plan and quality control procedures for their sensor system or existing public dataset.
- create a data management plan, associated metadata, and release the data in a public format using data their own data or a public dataset.
- analyze existing time series data, improve its quality, and apply it in a model to make predictions using lab assignments and a class project.
- apply time series analysis and other data science tools to an independent dataset to test a research hypothesis using lab assignments and a class project.