Data Analysis Methods in Geosciences

Catalog description

Credits 4. 3 Lecture Hours. 2 Lab Hours. COURSE OVERVIEW. This course examines how geoscientists conceptualize research questions, collect and process observations and apply statistical and computational methods to data drawn from environmental, atmospheric and oceanographic contexts. Students explore core questions such as how to select appropriate analytical techniques for different data types, how to quantify error and uncertainty and how to formulate and test hypotheses using real-world datasets. The course emphasizes hands-on programming in Python, R or MATLAB to build medium-length scripts that analyze, visualize and interpret geosciences data while developing reproducible workflows. Students should expect challenging, authentic problem-solving with messy data, rigorous model diagnostics and engagement with complex issues like experimental design, variance partitioning and responsible data management. RELEVANCE AND APPLICATION. The skills developed in this course are foundational for research in the geosciences and directly transferable to professional roles in environmental consulting, resource management, climate services, marine operations and public sector decision-making. By learning statistical analysis, visualization and programming, students gain tools to interpret real-world signals in noisy data, communicate evidence clearly and make informed, data-driven decisions. The emphasis on hypothesis testing, regression modeling, ANOVA and principal component analysis prepares students to evaluate claims, design robust studies and translate complex analyses into practical insights for stakeholders and communities. KEY TOPICS AND THEMES. Key topics include probability, hypothesis development, field study design with attention to replication, independence, and randomization, correlation and data visualization; linear and multiple regression, ANOVA and hypothesis testing. Students develop computational proficiency in Python, R or MATLAB; practice variance partitioning across regression, ANOVA and PCA; and build fluency in interpreting graphical and tabular representations of geosciences data. Prerequisites: Junior or senior classification; MATH 151 ; STAT 211 , STAT 301 , STAT 302 or STAT 303 , or concurrent enrollment; or approval of instructor.