BIOEE 3550

BIOEE 3550

Course information provided by the Courses of Study 2022-2023.

Ecology and Environmental Science are running into a 'big data' era. The unprecedented data sources provide opportunities for novel scientific exploration and solutions to real-world problems, which, however, usually requires robust quantitative analysis and informative visualization. This course aims to increase students' literacy and hands-on skills on common quantitative methods in ecology and environmental sciences, including accessing and curating data, statistical inference, regression, data-based predictions (also known as machine learning), and visualizing the results. Students will be using public data sets from organismal to landscape scales, including spatial data sets from the Google Earth Engine platform. Example codes will be provided in both Python and R.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: Introductory Calculus and Statistics, BIOEE 1610 or equivalent, or permission of instructor. Recommended prerequisite: experience in Python/R.

  • Demonstrate quantitative reasoning and computational thinking skills over heterogenous data sets.
  • Contrast motivation, theoretical basis, limitation, and applicable scenarios for common statistical inference and machine learning methods.
  • Compare and evaluate different quantitative models to explain realistic ecological/environmental questions.
  • Design and conduct scientific visualization on quantitative analysis results in Python/R.
  • Access and analyze public spatial environmental data set on Google Earth Engine.

View Enrollment Information

  •   Regular Academic Session.  Choose one lecture and one laboratory. Combined with: BIOEE 6550

  • 3 Credits Graded

  • 19737 BIOEE 3550   LEC 001

  • Instruction Mode: In Person
    Introductory Calculus and Statistics, BIOEE 1610 or equivalent, or permission of instructor. Experience in Python/R is preferred but not required.

  • 19738 BIOEE 3550   LAB 401

  • Instruction Mode: In Person