Environmental Science Minor

An introduction to the biological sciences. Topics include the principles of genetics, evolutionary theory, ecology, and conservation biology. Students enrolling in this course must enroll in the parallel laboratory section BIO 101L.

The laboratory course parallels the topics in BIO 101 and provides lab-based investigations of the material covered in BIO 101. Students must register for both BIO 101 and the lab section concurrently. Students who have previously taken BIO 101 and only need the lab credit should discuss this possibility with their advisor and the class professor.

This case study based course serves as the bridge experience for students completing their introductory course requirements for the ESS major or the ENV minor and who are now moving into the upper-level courses (However it is open to all interested students meeting the prerequisite). Through detailed examination of several case studies at the local, regional, and global levels, students synthesize material from introductory level courses to explore the interdisciplinary nature of today’s environmental issues. They examine what different disciplines offer to our understanding of and attempt to solve these issues.

The course exposes students to a range of quantitative methods used in the environmental sciences. It will introduce students to the science of geographic information systems (GIS) and their use in understanding and analyzing environmental issues. Students will gain hands-on experience with GIS software. This course will also examine statistical methods commonly applied in quantitative environmental research. It assumes students already possess a background in statistics and environmental science.

This computer-based course presents the main concepts in Statistics: the concept of random variables, frequency, and probability distributions, variance and standard deviation, kurtosis and skewness, probability rules, Bayes theorem, and posterior probabilities. Important statistical methods like Contingency analysis, ANOVA, Correlation analysis and Regression Analysis are introduced and their algorithms are fully explained. The most important probability distributions are introduced: Binomial, Poisson, and Normal distribution, as well as the Chebyshev theorem for non-known distributions. Inferential statistics, sampling distributions, and confidence intervals are covered to introduce statistical model building and single linear regression. Active learning and algorithmic learning are stressed. Emphasis is put both on algorithms –methods and assumptions for their applications. Excel is used while calculators with STAT buttons are not allowed. Ultimately students are required to make a month-long research project, select the theoretical concept they want to test, perform a literature review, find real data from Internet databases or make their surveys, apply methods they studied in the class, and compare theoretical results with their findings. Research is done and presented in groups, papers are Individual. Selected SPSS or Excel Data Analysis examples are also provided.