Curriculum and Courses

Program Structure

  • Part-time
  • 36 points (credits) for degree completion
  • Online instruction with two in-person residencies
    • First residency: Fall 2026 (August 26–28, 2026)
    • Second residency: August 2028 (exact dates TBD)
  • Fall intake
  • Capstone or culminating experience
  • *A pre-program bootcamp in R and Data Visualization will be required for students not previously exposed to R programming. 

The program is designed to be hands-on, with students acquiring skills in handling different types of nature data and applying systems thinking and knowledge from their own fields to tackle and solve concrete problems with data. The program culminates in a capstone project that will see diverse groups of students with a range of expertise, interests, and backgrounds working directly with a sponsor organization towards incorporating nature and biodiversity data in sustainable solutions to real-world problems. Students will gain:

  • Biostatistical skills for analyzing ecological data at population, species, community, and ecosystem levels using programming language.
  • Geospatial data analysis skills applied to nature and biodiversity datasets.
  • The ability to evaluate and interpret nature metrics used in biodiversity impact and dependency disclosure reporting.
  • Critical analysis of current conservation issues from national and global policy, social, and economic perspectives.
  • An understanding of ethics and justice in nature data collection, analysis and use.

The program follows a cohort module with students taking the same courses at the same time on a part-time basis for six semesters (including two summer sessions). In addition to conducting online coursework, students participate in two in-person three-day residencies in New York City at the opening and conclusion of the program. These residencies consist of group activities that apply concepts and develop career advancement strategies, as well as networking opportunities among students, faculty, and industry professionals.

Course Sequence

The program requires 36 points (credits) for degree completion. For required courses, students take six core courses that build data literacy, analytical tools, and interdisciplinary skills needed to drive effective, evidence-based decisions that benefit people and nature across sectors and scales. In addition, students will choose courses from two selective areas: Spatial Data Analysis and Visualization, and Finance, Policy, and Decision-Making.

Capstone

The capstone is the culminating experience of the program, where students work in teams to solve real-world biodiversity decision-making challenges. Guided by faculty mentors, students apply analytical tools and program knowledge to scope problems, analyze data, and deliver actionable recommendations. Acting as consultants, students will develop solutions grounded in data and aligned with sustainability goals.

The University reserves the right to withdraw or modify the courses of instruction or to change the instructors as may become necessary.

Core Courses (21 credits)

  • Graduate Seminar in Conservation Science (EEEB 6905): (3 credits) Introduction to the applied science of maintaining the earth’s biological diversity; its landscapes; and wilderness, including biological principles relevant to biodiversity conservation at the genetic; population; community; and landscape levels.
  • Intro Statistics Ecology & Evolutionary Biology  (EEEB 5005): (3 credits) An introduction to the theoretical principles and practical application of statistical methods in ecology and evolutionary biology. The course will cover the conceptual basis for a range of statistical techniques through a series of lectures using examples from the primary literature. The application of these techniques will be taught through the use of statistical software in computer-based laboratory sessions.
  • Advanced Methods in Biodiversity Data Analysis I (TBD): (3 credits) Introduces population ecology and statistical modeling in R to develop species-based biodiversity metrics for conservation and policy use.
  • Advanced Methods in Biodiversity Data Analysis II (TBD): (3 credits) Expands on biodiversity analytics by teaching community-level data analysis and ecosystem services modeling using R, InVEST, and GIS.
  • Nature Data in Environmental Management Decisions (TBD): (3 credits) Covers conservation decision science frameworks and tools like PrOACT, optimization, and cost-effectiveness analysis to solve real-world biodiversity challenges.
  • Introduction to Landscape Analysis (EEEB GU4192): (3 credits)  Changes in land use and land cover underlie multiple environmental and sustainability concerns, including conservation of biodiversity, impacts of climate change, climate mitigation through terrestrial carbon storage, urbanization and watershed protection. This class provides basic theory in landscape analysis and training methods for analyzing landscapes, focusing on interpretation of satellite images.
  • Capstone (TBD): (3 credits) The capstone is the culminating experience of the program, where students work in teams to solve real-world biodiversity decision-making challenges for a sponsoring organization. Guided by faculty mentors, students apply analytical tools and program knowledge to scope problems, analyze data, and deliver actionable recommendations. Acting as consultants, students will develop solutions grounded in data and aligned with sustainability goals.

Sample Courses in Selective Areas

Selective Area 1: Spatial Data Analysis and Visualization (6 credits)*

  • Introduction to GIS (EEEB4670): (3 credits) Geographic information systems (GIS) are powerful tools for analyzing fundamental geographic questions. GIS involves generating, linking, manipulating, and analyzing different sorts of spatial data; creating cartographic outputs and geovisualizations This course introduces major topics in GIS with applications for the broad field of biology and natural sciences, using QGIS and R.
  • Python for Data Analysis (APAN 5210): (3 credits) Python is a leading open-source programming language for data science. This course introduces the fundamentals of Python, including data structures, functions, object-oriented programming, and file I/O. Students use platforms such as IPython and Jupyter Notebook to analyze and visualize real-world datasets. Key libraries include pandas for data analysis, NumPy for arrays, and Matplotlib for plotting. No prior programming experience is assumed.
  • Storytelling with Data and AI (APAN 5800): (3 credits) Effective data communication requires more than numbers—it requires storytelling. This course develops written, visual, and verbal techniques to communicate analytical insights to technical, professional, and executive audiences. Students practice strategic storytelling, persuasive presentations, and active listening while creating deliverables such as reports and presentations. Emphasis is placed on adapting messages for technical and non-technical audiences, with feedback to refine clarity and impact.
  • Landscape Ecology (EEEB4160): (3 credits) This course examines how spatial patterns shape ecological processes across individual, population, community, and ecosystem levels. Students explore the role of landscape structure in biodiversity conservation, ecosystem services, and land management. Lectures and labs provide experience with mapping and analysis tools while applying landscape-scale thinking to ecological research and sustainability planning.

Selective Area 2: Finance, Policy, and Decision-Making (9 credits)*

  • Conservation Policy (EEEB 4005): (3 credits) Students explore the science behind conservation policy, assess how effective policies are in meeting conservation goals, and examine the critical role scientists play in shaping environmental decision-making at the local, state, federal, and international levels. 
  • Climate Finance and Sustainable Development (SUMA 5156): (3 credits) Examines funding strategies for climate action, emphasizing carbon accounting, financial tools, and equitable investment design.
  • Cost-Benefit Analysis (SUMA 5020): (3 credits) Introduces practitioners to the techniques of preparing a cost-benefit analysis, including microeconomic foundations, valuation methods, discounting, the impact of uncertainty and optionality, and distributional consequences.
  • Accounting, Finance and Modeling of Sustainable Investments (SUMA 5195): (3 credits)  This course equips students with foundational knowledge in accounting, finance, and ESG/impact investing to support decision-making in sustainable companies. Students will learn financial literacy, valuation techniques, and how to screen investments for ESG performance. Through hands-on projects, they'll create investment memos and financial models, develop sustainability-based operating plans, and engage with tools for financial and non-financial reporting. The course prepares students to lead in finance-driven sustainability roles across sectors.
  • Impact Finance for Sustainability Practitioners (SUMA 5445): (3 credits)  This course explores seven key tools of impact investing—private equity, ESG integration, mergers and acquisitions, mission investments, social impact bonds, green bonds, and crowdfunding—within their policy and regulatory contexts. Students will follow the investment process from deal sourcing to exit, learning due diligence, documentation, and transaction structuring. Case studies connect financial tools to real-world events such as the Arab Spring, federal budget shifts, and the Paris Climate Agreement. Designed for students pursuing careers in nonprofit, public administration, or finance, the course builds skills in evaluating impact finance strategies, ESG integration, and policy analysis. No prerequisites are required, though familiarity with finance is helpful.
  • GHG Emissions: Measuring and Minimizing the Carbon Footprint (SUMA 5035): (3 credits) This course examines the science and practice of greenhouse gas (GHG) emissions accounting and reporting, with an emphasis on strategies to reduce carbon footprints across sectors. Students gain hands-on experience designing and executing GHG inventories, including setting boundaries, acquiring data, calculating emissions, and reporting results. Workshops, papers, and group projects provide opportunities to analyze current protocols, evaluate emerging standards, and propose improved methods for carbon accounting and mitigation.