A Complete Python Training Pathway for Biologists, Ecologists and Data-Driven ResearchersModern biological research is increasingly data-intensive.

From ecological field surveys and genomics to environmental monitoring and experimental biology, researchers must now be able to handle, analyse, visualise and interpret complex datasets. Python has become one of the most powerful and widely used tools for this purpose.

PRStats offers a coherent four-course Python training pathway designed specifically for biologists and environmental scientists — taking you from complete beginner through to advanced data science and statistical computing.

This structured programme allows you to build confidence step-by-step, ensuring you not only learn how to code, but how to apply Python effectively to real biological and ecological data.

Course 1: Introduction to Python for Biologists (IPYB01)

https://prstats.org/course/introduction-to-python-for-biologists-ipyb01/

This course is designed for biologists and life-science researchers with little or no prior programming experience. It provides a gentle, carefully structured introduction to Python and computational thinking.

You will learn:Core Python syntax and programming conceptsWorking with numbers, text, lists, and dictionariesWriting simple scripts and functionsReading and writing data filesUsing Python as a practical research tool rather than a theoretical exercise.

By the end of this course, you will be able to automate basic data handling tasks and understand how Python fits into modern biological research workflows.

Course 2: Advanced Python for Biologists (APYB01)

https://prstats.org/course/advanced-python-for-biologists-apyb01/

This course builds on the fundamentals and takes your programming skills to the next level. It is ideal for researchers who already use Python occasionally but want deeper control, efficiency, and reproducibility.

You will learn:More advanced data structures and programming patternsWriting robust, modular, and reusable codeAutomating analysis workflowsHandling larger datasets efficientlyGood coding practice for collaborative and reproducible research.

This course is particularly valuable for researchers moving toward bioinformatics pipelines, large-scale data cleaning, or scripted analysis workflows.

Course 3: Python for Biological Data Exploration and Visualization (PYBD01)

https://prstats.org/course/python-for-biological-data-exploration-and-visualization-pybd01/

Effective data exploration and visualisation are essential for understanding biological patterns and communicating scientific results. This course focuses on practical data analysis and graphical presentation using Python.

You will learn:Importing and cleaning biological and ecological dataExploratory data analysisProducing high-quality scientific plotsVisualising trends, variation, and relationshipsPresenting results clearly for reports, publications, and presentations.

This course is ideal for researchers working with ecological surveys, experimental data, time series, spatial data summaries, or monitoring datasets.

Course 4: Python for Data Science and Statistical Computing (PYDS01)

https://prstats.org/course/python-for-data-science-and-statistical-computing-pyds01/

This advanced course integrates Python with modern data-science and statistical workflows. It is aimed at researchers who want to move beyond basic exploration into rigorous, reproducible quantitative analysis.

You will learn:Advanced data manipulation and processingStatistical analysis in PythonExploratory and confirmatory data analysisComputational approaches to modelling and predictionReproducible scientific workflows for large datasets

This course is particularly well suited to researchers working in ecology, environmental science, genomics, epidemiology, and applied biological modelling.

Why This Python Programme Is Ideal for Biologists and Environmental ScientistsDesigned specifically for biological researchers — No generic computer-science focus; all concepts are framed around real biological and ecological data.

Progressive skill development — You can start with no coding experience and progress to full data-science capability.

Hands-on and practical — Emphasis on real datasets, realistic workflows, and reproducible research.

Supports modern biological research — From field data analysis and experimental biology to bioinformatics, ecological modelling, and conservation science.

Flexible learning for working scientists — Live online delivery with supporting materials, allowing you to learn around lab, fieldwork, or teaching commitments.

Who These Courses Are For - Biologists, ecologists, conservation scientists, environmental researchers PhD students, post-doctoral researchers and research assistantsProfessionals working with biological, medical, or environmental data Anyone seeking to integrate Python into daily research workflows for data handling, analysis, visualisation and statistical computing

Email oliver@prstats.org with any questions.