Programming and Data Science for Biologists (PDSB) will introduce students to fundamental computational skills and concepts for working with large biological data sets. This will include an introduction to several programming languages (Python, R, Julia), and in-depth training in one language in particular (Python). We will cover tools for collaboration and version control (git, GitHub), and how these tools can be used to host and share code, data, and websites. A core focus throughout the course will be reproducibility and learning tools (jupyter) and practices for this purpose. We will learn to organize and structure data for statistical analyses (DataFrames, arrays, datatypes), and explore tools for scientific analyses (scipy, pymc3, scikit-learn, keras) and visualization (matplotlib, toyplot, bokeh). Exercises and assignments will introduce students to large empirical datasets used in the biological sciences, from studies of genomics to biodiversity. The latter half of the class is organized around individual projects, in which students will be guided to design a command-line program and/or API for performing a specific type of analysis. Computer programs are ubiquitous in biology, but few biologists receive formal training in designing and writing software. This course offers a deeper introduction to computational techniques and algorithms commonly applied to biological datasets.

Prerequisite(s): One year of introductory biology or permission from the instuctor

Prerequisite(s): Prerequisites: (EEEB UN1010) or (EEEB UN1011)

In this course we take an integrative and comparative approach to understanding the sexual lives of primates.  Focusing on mating and reproductive behavior with an explicitly evolutionary perspective, we will identify the fundamental principles of how and why selection has favored particular behaviors and morphologies in different primate species.

Genome sequencing, the technology used to translate DNA into data, is now a fundamental tool in biological and biomedical research, and is expected to revolutionize many related fields and industries in coming years as the technology becomes faster, smaller, and less expensive. Learning to use and interpret genomic information, however, remains challenging for many students, as it requires synthesizing knowledge from a range of disciplines, including genetics, molecular biology, and bioinformatics. Although genomics is of broad interest to many fields—such as ecology, evolutionary biology, genetics, medicine, and computer science—students in these areas often lack sufficient background training to take a genomics course. This course bridges this gap, by teaching skills in modern genomic technologies that will allow students to innovate and effectively apply these tools in novel applications across disciplines. To achieve this, we implement an active learning approach to emphasize genomics as a data science, and use this organizing principle to structure the course around computational exercises, lab-based activities using state-of-the-art sequencing instruments, case studies, and field work. Together, this approach will introduce students to the principles of genomics by allowing them to generate, analyze, and interpret data hands-on while using the most cutting-edge genomic technologies of today in a stimulating and engaging learning experience.

Enrollment limit 25

The Amazon Basin is one of the largest equatorial forests on earth.  Far from being an untouched bioma the Amazon has a rich and instigating sociobiodiversity that can be apprehended in its uniqueness since pre-colombian times.  History, culture, politics correlated with hydrology, climate and ecology are elements for the understanding of contemporary dynamics in the Amazon.  The course aims towards an interdisciplinary approach of the Amazon as a unique ecosystem in Latin America which reflects a myrad of questions crucial for the understanding not only of South America but of nature and society in modern times.

EEB graduate students only.

Supervised directed readings and literature review in areas relevant to a student’s research program.

Prerequisite(s): One graduate semester completed.

Research opportunities for graduate students: experimental design, development of tests for specific hypotheses, acquisition of new techniques, analysis of data, interpretation and discussion of results.

Prerequisite(s): Biology, Ecology, Genetics, and Evolution.

This course is an introduction to the applied science of maintaining the earth’s biological diversity, its landscapes, and wilderness. The course will focus on the biological principles relevant to biodiversity conservation at the genetic, population, and community and landscape levels.

How to formulate research hypotheses, choose a design, and collect, analyze and present data. Students undertake individual projects on zoo or park animals.

Prerequisite(s): Preference given to graduate students in EEB or Anthropology.

The assumptions upon which community-based conservation and development projects are based, their successes and shortcomings within the context of history and the environment. Experienced guest lecturers.

Prerequisite(s): Degree in Biological Sciences.

Seminar series includes lectures on methods and case studies of specific biological research projects; emphasis on evolution, ecology, and conservation biology. Weekly lectures by Columbia faculty, researchers, students and guest speakers.

Graduate students in EEB program have priority.

This course will introduce the Coupled Natural and Human system framework to study socio-ecological systems and introduce active learning of Agent-based models (ABM) as one of the most common methods to study CNH systems.