Dec 04, 2022  
2020-2021 Catalog 
    
2020-2021 Catalog [ARCHIVED CATALOG]

Data Science for Environmental Applications Certificate


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Department of Environmental Sciences, Huxley College of the Environment

20 credits

Introduction

This 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 four, four-credit classes in experimental design, multivariate statistics, time series, and spatial analysis combined with three 1- to 2-credit seminar classes in topics including data visualization, machine learning, and interactive 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
Jenise Bauman
Department of Environmental Sciences
360-394-2756
Jenise.Bauman@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 – Experimental Design (4 credits)
  • ESCI 599 – Data Visualization (1 credit seminar)

Winter

  • ESCI 503 – Statistical Ecology (4 credits)
  • ESCI 504 – Time-Series Analysis for Environmental Data (4 credits)
  • ESCI 599 – Introduction to Machine Learning (2 credits seminar)

Spring

  • ESCI 505 – Spatial Analysis for Environmental Data (4 credits)
  • ESCI 599 – App Development (1 credit seminar)

Grade Requirements

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

Requirements


This is an online 20-credit graduate certificate. Students must begin in the fall.

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