BIOEE 6550

BIOEE 6550

Course information provided by the Courses of Study 2023-2024.

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.

Outcomes
  • 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.
  • Organize quantitative analysis into a report in the format of a typical research manuscript.

View Enrollment Information

Syllabi:
  •   Regular Academic Session.  Choose one lecture and one laboratory. Combined with: BIOEE 3550

  • 3 Credits Graded

  •  2763 BIOEE 6550   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. Graduate students need to additionally submit a written paper in the format of a typical research manuscript.

  •  2764 BIOEE 6550   LAB 401

  • Instruction Mode: In Person