Visualization of Oceanographic Data using Python
during 15 – 26 September 2025
Organized by
International Training Centre for Operational Oceanography (ITCOocean) INCOIS, Hyderabad, India.
This course provides a practical guide to visualizing oceanographic data, from basic to advanced techniques. Alongside Python libraries like Matplotlib, Cartopy, xarray, and Plotly, it introduces Ferret, widely used in oceanography for analyzing and visualizing large multidimensional datasets. Learners will gain the skills to turn complex data into clear, impactful visual stories.
Basic Computer Literacy: You should be comfortable with general computer opera-tions and file management.
Fundamental Math Concepts: A basic understanding of algebra and statistics is help-ful.
No Prior Python or Ferret Experience Required: This course is designed to start from the very beginning of Python programming and introduce Ferret/PyFerret for oceanographic data visualization.
Students, researchers, and professionals interested in strengthening their skills in Python programming for oceanographic data analysis and visualization. Individuals eager to apply computational tools to address challenges in ocean science, and to make meaningful contributions in academia, research, industry, and government sectors.
This course is organized into modules that build upon each other, guiding you through a logical progression of skills.
Module 1: Python Fundamentals and Data Handling
Focus: Establish a strong foundation in Python programming and essential data manipulation libraries. You'll learn core Python syntax, how to work with numerical data using NumPy, and efficiently manage tabular and multi-dimensional datasets with Pandas and xarray.
Outcome: You'll be able to load, inspect, clean, and prepare various oceanographic datasets for analysis.
Module 2: Static Plotting and Initial Visualizations
Focus: Master the creation of standard static plots. This module covers Matplotlib for basic charting (line, scatter, bar, histograms) and Seaborn for creating more aesthetically pleasing and statistically oriented visualizations. You'll learn to customize plots for clarity and impact.
Outcome: You'll be able to generate informative 2D plots to represent oceanographic variables and trends.
Module 3: Geospatial Mapping
Focus: Dive into visualizing data on geographical maps using Cartopy. You'll understand map projections, learn to add geographical features, and effectively overlay oceanographic scalar fields (like sea surface temperature or chlorophyll) onto maps.
Outcome: You'll be able to produce professional-quality maps that display spatial oceanographic data.
Module 4: Advanced Data Analysis and Specialized Plots
Focus: Explore more advanced data analysis techniques, particularly for time-series data, including resampling and anomaly calculations. You'll also learn to create specialized plots like vertical cross-sections to visualize data along depth profiles.
Outcome: You'll gain skills in in-depth temporal and vertical analysis of oceanographic datasets.
Module 5: Visualizing Vector Fields and Flow
Focus: Learn to visualize dynamic oceanographic phenomena like currents. This module covers quiver plots to show direction and magnitude, and streamlines to represent flow patterns, integrating these visualizations onto geographical maps.
Outcome: You'll be able to effectively visualize and interpret ocean current data.
Module 6: Interactive and 3D Visualizations
Focus: Elevate your visualizations with Plotly, enabling you to create interactive charts that allow for zooming, panning, and detailed data exploration. You'll also get an introduction to basic 3D plotting for visualizing bathymetry or other volumetric data.
Outcome: You'll be able to produce dynamic and engaging visualizations that enhance data understanding.
Module 7: Capstone Project
Focus: Consolidate all the skills acquired throughout the course by working on a practical, self-selected mini-project. This involves planning, data integration, creating multiple visualization types, and effectively communicating your findings.
Outcome: You'll have a tangible project demonstrating your ability to apply Python for oceanographic data visualization from start to finish.
Recommended Resources
Official Python Documentation: For core language reference.
NumPy, Pandas, Matplotlib, Seaborn, Cartopy, Plotly Documentation: Comprehensive guides for each library.
Online Tutorials: Platforms like Real Python, DataCamp, and Kaggle for additional practice and learning.
Ferret & PyFerret Documentation: NOAA Ferret User’s Guide for working with multidimensional oceanographic data.
Oceanographic Data Sources: Key repositories such as NOAA, Copernicus Marine Service, and Argo data for real-world datasets.
International Training Centre for Operational Oceanography (ITCOocean),
Indian National Centre for Ocean Information Services (INCOIS),
Ministry of Earth Sciences, Government of India,
"Ocean Valley", Pragathi Nagar (B.O), Nizampet (S.O),
Hyderabad - 500 090, INDIA
For any queries related to this course: Dr. T V S Udaya Bhaskar
Head, Ocean Data Management (ODM) & Programme Planning and Coordination Group (PPC)
E-mail: itcoocean@incois.gov.in/uday@incois.gov.in
Indian National Center for Ocean Information Services (INCOIS)
