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Curricular Courses

Reproducible Data Science - WILD 6900

Reproducibility is a pillar of the scientific method. Today, scientists rely on software and code as fundamental tools to carry out their science and ensure it is reproducible. Being proficient in the use of programming tools and effectively apply them to store, process, manage, analyze, and visualize data have become must-have skills to take part in the scientific discourse and to be successful on the job market, academic or not. In this course, students learn best practices in data management and processing for reproducible science. Topics covered encompass tools to effectively manage data throughout their life cycle, from the moment they get entered into a computer to the moment they are published:

  • Spreadsheets for data entry; 

  • SQL relational databases for data management;

  • R and the tidyverse for data cleaning, processing, analysis, and visualization; 

  • Git for version control of code scripts and data files; 

  • GitHub for code sharing and efficient collaboration.

​The goal of this course is to help students establish a reproducible workflow for their day-to-day research endeavors, by building long-lasting habits in efficient project organization, data management, and processing.

Institution: Utah State University (2021, 2023)

Level: Graduate, 3 credits

Role: Instructor of Record & Developer

Course Materials: 

Download Syllabus

Plant and Animal Populations - WILD 3810

Population ecology is the foundation of modern wildlife conservation and management. This course introduces fundamentals of population ecology, including single-species, multi-species, age-structured, stage-structured, and spatial models of population dynamics, as well as life-history strategies. Concepts will be illustrated with real-world examples that highlight the importance of population ecology for conservation and management. The course includes traditional lectures as well as hands-on practice during labs using the software R.

Institution: Utah State University (2022)

Level: Undergraduate, 3 credits

Role: Instructor of Record

Download Syllabus

Next-Generation Data Management in Movement Ecology - WIS 6934

The emergence of movement ecology theory in recent decades has been paralleled by technological advancements allowing ecologists to collect a wealth of data on animal movement at increasingly finer spatio-temporal resolutions. Animal locations need to be integrated with information derived from remote-sensing imagery, climate data, geospatial layers, and more. These complex, multidimensional datasets create a need for adequate data management and processing tools and techniques to bridge the gaps between data acquisition and scientific inference. In this course, students are first introduced to fundamentals of relational database design and the SQL programming language; then, these general concepts are applied to the specific data management requirements of wildlife tracking data. Students learn how to design, create, and populate a PostgreSQL database including millions of locations for hundreds of tracked individuals, and how to seamlessly integrate animal locations with raster and vector data using the PostGIS database extension. Finally, the database is interfaced with the R software for a seamless transition from data management to statistical analysis. 

Institution: University of Florida (2016, 2017)

Level: Graduate, 2 credits

Role: Co-Instructor

Extra-Curricular Courses

  • Version Control and Collaborative Science with Git and GitHub
    Two-day workshop @ University of Wyoming, 2021

  • Data Carpentry for Ecologists
    Two-day workshops @ University of Florida, 2017-2019

  • Data Management, Manipulation, and Visualization in R
    Two-day workshop @ Florida Atlantic University, 2019


Quote Mark
"Your class is the most useful class I have ever taken, graduate or undergraduate. Things I used to feel like I was botching, like having 100 versions of a code and none of them making sense a week after I wrote them, I now feel like I could do as part of my career."
Graduate Student, Reproducible Data Science 2021
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