This course is focused on introducing basic data science concepts using the Python programming language. Throughout the course, students will learn the necessary syntax and semantics of the language to achive a solid foundation that will help them accomplish future projects in this field.

• Group #1: Starts on Wednesday, July 24, and will be held every Wednesday at 6:00 PM (GMT-6) for 8 weeks.
• Group #2: Starts on Thursday, July 35, and will be held every Thursday at 6:00 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).

This introductory Python program is designed to provide a solid understanding of basic programming concepts and Python fundamentals through theoretical and practical lessons, allowing participants to develop data science skills.

• Build a strong understanding of Python's basic syntax for writing and executing code.
• Experiment with basic programming concepts, including variables, data types, data structures, functions, object-oriented programming (OOP), and data visualization.
• Develop concepts related to data science, such as linear algebra, machine learning, exploratory data analysis, and geographic information systems, among others.
• Apply acquired knowledge and skills through practical labs to consolidate learning and develop Python programming expertise.
• Develop research projects applied to biodiversity conservation.

The course methodology is based on active and collaborative learning, through problem-solving in labs, research work, and inverted classroom, among others techniques. The aim is to guide students to strengthen their research abilities and apply new concepts based on previously acquired knowledge and course content.

The course program is theoretical/practical, allowing participants to apply theoretical knowledge through case studies, labs, and research projects.

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

1. Python Fundamentals
1. Introduction to Python.
2. Work environment setup (Jupyter Notebooks).
3. Variables and data types.
4. Control flow structures.
5. Functions.
2. Introduction to Data Analysis with Python
1. Introduction to OOP and Pandas.
2. Data management and file formats.
3. NumPy, matrices, and vectors.
4. Exploratory data analysis.
5. Data visualization and graphing.
6. Data transformation and preprocessing.
3. Introduction to Geospatial Data Representation
1. Introduction to Geographic Information Systems (GIS) with biodiversity data.
4. Introduction to Machine Learning
1. Introduction to machine learning.
2. Example of application.

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

Each evaluation will have a previously established due date. The deadline for submission is 11:45 PM (GMT-6). Late submissions will not be accepted.