This course is focused on handling, visualizing, and analyzing geospatial biodiversity data using the Python programming language. It covers the fundamentals of Python, its geospatial modules, and its application to processing biodiversity data. The course demonstrates how data science methodologies and techniques can be applied to the geospatial component of biodiversity data.

• Starts Monday, January 13 2025, and will be held every Monday from 6:00 PM to 8:00 PM (GMT-6) for 8 weeks.

• Modality: Virtual.
• Theoretical/Practical: To complete the program, participants must attend more than 75% of virtual synchronous classes and achieve an average greater than or equal to 70 in evaluations.
• Cost: Free.

• Availability of at least 16 hours during the entire program to attend eight virtual synchronous sessions. (2 hrs/class)
• Availability of at least 24 hours during the entire the program for completing short assignments, labs, and a final project. (3 hrs/week)
• Fill out the form to Participate in Redbioma activities (previously circulated, please fill out only once).

To learn how to develop Python programs focused on processing geospatial biodiversity data.

• Apply a data science approach to import, transform, visualize, analyze, and communicate geospatial biodiversity data.
• Develop replicable solutions to computational problems using Python.
• Integrate tabular, graphical, and geospatial visualizations of biodiversity data into documents and interactive applications developed in Python.

The course is delivered through theoretical and practical virtual synchronous classes. Theoretical concepts are explained by the instructor during class sessions and through assigned readings. Practical sessions are dedicated to programming exercises performed by students.

Lesson content is available on the course website, which includes links to bibliographic resources and other learning materials such as tutorials and videos.

• All synchronous sessions will be recorded and published on the redbioma website.
• Final research projects will be published on the redbioma website.

1. Introduction to the course
1. Course program review.
2. Data science and reproducibility.
3. Markdown language.
4. Jupyter notebooks.
2. Python programming language
1. Basic data types.
2. Variables.
3. Expressions.
4. Comments.
5. Conditionals.
3. Python programming language (continued)
1. Loops.
2. Data structures.
1. Lists.
2. Tuples.
3. Sets.
4. Dictionaries.
4. Introduction to biodiversity data
1. Darwin Core standard.
2. Pygbif library.
5. Data Analysis and visualization
1. Pandas library.
6. Statistical plotting.
1. Matplotlib library.
2. Plotly library.
7. Introduction to geospatial data
1. Data models.
2. Geographic information systems.
8. Vector data
1. GeoPandas library.
9. Raster data
1. Rasterio library.
10. Visualization of biodiversity geospatial data
1. Leafmap library.

Students will complete assignments and a final project. Evaluation items are as follows: