Data Analysis with Python Programming for Early Career Ocean Professionals (ECOPs) [online]

Resumen

Given the development of new tools in studying the ocean in more detail and on larger scales, early-career ocean professionals, students and other oceanographers need programming skills for effective data management and analysis.  This course serves as an introduction to the Python programming language and software environment, enabling participants to process, analyze and visualize data more robustly. It is specially designed for early career ocean professionals who are beginners in Python programming, and who aim to explore different ocean datasets in their research. The training will utilize relevant oceanographic datasets, including physical, chemical and biological data, as well as fisheries datasets. This approach makes it easier to relate to, understand and apply concepts covered in the course.

Learning outcomes

• Be familiar with the Python environment.
• Acquire the ability to use Python for common data tasks such as loading, cleaning, and visualizing data.
• Generate meaningful descriptive statistics and informative graphs.
• Perform statististical analysis using Python.

Target audience and prerequisites

ECOPs who are enrolled in a Bachelor’s degree and ECOPs who are pursuing their postgraduate studies (Masters & PhD), and who intend to use Python in their academic or research related work are all encouraged to apply. No prior Python knowledge will be required.  The current course is specifically targeting ECOPs from and/or based in Central America.

Language of instruction

English >> Materials will be given in English. Classes will be imparted in English and Spanish.

Course content

• Setting up environment with MAMBA or Conda
• Installing and setting Python,
• Python basics
• Introduction to Google Colab
• Programming notebook

• Pandas  
• Plot vs. scatterplot 
• Datetime
• Data e.g National Data Buoy Centre (NDBC dataset)

• Basic statistics
• Histograms
• Running averages
• Annual Cycle by Month or Day
• Inter-annual Variation and Variability2
• Working with Xarray
• Anomaly plots 

Instructors

Facilitators

Dr. Leonardo Alvarado – Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research

Dr. Andrea Lira Loarca – MeteOcean research group, Department of Civil, Chemical and Environmental Engineering, University of Genoa

Rieke Schäfer - Physikalisch-Technische Bundesanstalt (PTB)

Moderators

Gabriel A. Juma – Early Career Ocean Professionals (ECOPs) Capacity Development Coordinator

Xochitl E. Elias Ilosvay – Coordinator, Early Career Ocean Professionals (ECOPs) Central America Node

Natalia Solis – Early Career Ocean Professionals (ECOPs) Training and Mentoring Task Team

Prior to the course, the learners will be required to:

1. Visit the Python software page and download the software.
2. Download the datasets that will be uploaded on the OTGA platform.
3. Do a self-introduction on the OTGA platform and highlight their expectations.
4. Read the course materials notes that will be prepared by the lecturers.

Learner assessment and certificate

• Going through all the modules either synchronously or asynchronously
• Submitting the assignments on time.
• Submitting a brief individual/team project report.

Certificate will be issued to all participants who will have completed the modules and attained the 50% pass mark.

Technology requirements

To complete this course, you should have access to: 

• A laptop or desktop from which the students can perform tasks and join the synchronous sessions.
• Latest version of either Chrome, Edge, Firefox, or Safari web browsers
• Linux (preferably ubuntu 16.04 or above)
• Access to internet