Data Science for Environmental Applications Certificate

Department of Environmental Sciences, Huxley College of the Environment

18 credits

Introduction

This graduate certificate will allow students to develop skills, tools, and techniques to address increasingly complex environmental questions using data science. The core of the certificate will be three 4-credit classes starting with applied statistics in environmental science and a choice of two out of three 4-credit classes that include multivariate methods, time-series analysis and spatial analysis. This is combined with three 2-credit seminar classes in topics including data wrangling & data visualization, machine learning, and dashboard & app development. All work will be done in an open-source environment using R, R markdown, and Shiny.

Why Consider a Data Science for Environmental Applications Certificate?

The role of big data, statistical analysis, and programming are critical aspects of today’s environmental job market. The emphasis throughout the certificate will be on applied methods to increase job-ready skills.

Contact Information

Academic Program Director Department of Environmental Sciences Jenise Bauman 360-394-2756 Jenise.Bauman@wwu.edu

Huxley Graduate Program Specialist Ed Weber 360-650-3646 Ed.Weber@wwu.edu

Sample Careers

Environmental Scientist | Environmental Consultant | Project Manager | Data Analyst | Statistician

Major/Career Resources

www.wwu.edu/careers/students_choosingamajor.shtml

How to Declare (Admission and Declaration Process):

Applicants will apply via the Graduate School. Admission to the certificate is based on available space with first preference given to students in pursuing careers in data analysis and modeling.

Prerequisites: A bachelor’s degree with completion of a 300-level statistics course.

This is an online 1-year graduate certificate that begins in the fall and it will typically follow this schedule of core courses:

Fall

• ESCI 502 – Applied Statistics in Environmental Science (4 credits)
• ESCI 599 – Data Wrangling and Data Visualization (2 credit seminar)

Winter

• ESCI 503 – Multivariate Methods for Environmental Science (4 credits)
• ESCI 599 – Machine Learning (2 credits seminar)

Spring

• ESCI 504 – Time-Series Analysis for Environmental Data (4 credits) 
  or ESCI 505 – Spatial Analysis for Environmental Data (4 credits)
• ESCI 599 – Dashboard and App Development (2 credit seminar)

Grade Requirements

A grade of C- or better is required for a student’s certificate courses, and supporting courses for certificates.