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Climate Geospatial Analysis on Python with Xarray

Danilo Lessa Bernardineli

By the end of this project, you will be able to load, visualize, manipulate and perform both simple and grouped operations over geospatial multidimensional data through Xarray and Python.

We'll explore an dataset containing temperature, vegetation density and total precipitation over the Brazilian Amazon for the 1979-2019 period while the concepts are developed.

This will enable the learner to handle and extract knowledge from complex datasets such as the ones from satellite and climate re-analysis observations.

Read more

By the end of this project, you will be able to load, visualize, manipulate and perform both simple and grouped operations over geospatial multidimensional data through Xarray and Python.

We'll explore an dataset containing temperature, vegetation density and total precipitation over the Brazilian Amazon for the 1979-2019 period while the concepts are developed.

This will enable the learner to handle and extract knowledge from complex datasets such as the ones from satellite and climate re-analysis observations.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Enroll now

What's inside

Syllabus

Project Overview
By the end of this project, you will be able to load, visualize, manipulate and perform both simple and grouped operations over geospatial multidimensional data through Xarray and Python.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops skills and techniques that are applied in climate and geographic studies
Implements hands-on labs and interactive material
Emphasizes the use of Xarray and Python for manipulating and visualizing geospatial data
Focuses on a real-world dataset from the Brazilian Amazon, providing practical relevance
Requires additional software and tools that may not be readily available to all learners
Taught by Danilo Lessa Bernardineli, who has expertise in the field

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Reviews summary

Well-received climate course

Learners say this well-structured course is great for all learners and is very useful for those in geo-sciences. Many students appreciate that the instructor is knowledgeable and enthusiastic and liked that he uses a real-world dataset. Students also remark that projects are easy to revisit and recommend it for those interested in climate data.
Resources are available to students after course completion
"He uses a real-world dataset (Amazon rainforest) which I found interesting."
"I chose to do the examples in my own Jupyter notebook so I can easily go back and review."
"I recommend this guided project for those interested in exploring climate data using Python."
Organized course structure makes learning easier
"I really like this one. I think it's an amazing course for everyone (very clear, so didactit) and for those who studies geo-sciences is very useful."
"The instructor is knowledgeable and enthusiastic about his subject."
"I recommend this guided project for those interested in exploring climate data using Python."
Applicable skills and experience you can use
"He uses a real-world dataset (Amazon rainforest) which I found interesting."
"I recommend this guided project for those interested in exploring climate data using Python."
Experienced instructor who is passionate about teaching
"The instructor is knowledgeable and enthusiastic about his subject."
"I really like this one. I think it's an amazing course for everyone (very clear, so didactit) and for those who studies geo-sciences is very useful."
Course content is not worth the cost
"Not worth paying for at all. Avoid!!!If you have a NetCDF data file to use I am sure free youtube help videos offer you the exact same."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Climate Geospatial Analysis on Python with Xarray with these activities:
Watch tutorials on geospatial data analysis
Watching tutorials on geospatial data analysis will help you learn the basics of the subject and prepare you for the concepts that will be covered in this course.
Browse courses on Geospatial Data Analysis
Show steps
  • Find some tutorials on geospatial data analysis.
  • Watch the tutorials.
  • Take notes on the key concepts.
Read a book on geospatial data analysis
Review some of the fundamental concepts of geospatial data analysis prior to taking this course. This will strengthen your foundational knowledge and help better prepare you for success when this course begins.
View Geospatial Analysis on Amazon
Show steps
  • Purchase the book.
  • Set aside time each week to read the book.
  • Take notes as you read.
  • Complete the exercises at the end of each chapter.
Practice using geospatial data analysis tools
Practicing using geospatial data analysis tools will help you develop the skills you need to succeed in this course.
Browse courses on Geospatial Data Analysis
Show steps
  • Find some tutorials on geospatial data analysis tools.
  • Follow the tutorials and complete the exercises.
  • Experiment with the tools on your own.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a workshop on geospatial data analysis
Attending a workshop on geospatial data analysis will help you learn more about the subject and network with other professionals in the field.
Browse courses on Geospatial Data Analysis
Show steps
  • Find a workshop on geospatial data analysis.
  • Attend the workshop.
  • Participate in the discussions.
  • Network with the other attendees.
Start a project using geospatial data
Starting a project that uses geospatial data will help you apply the concepts of the course and reinforce what you learn.
Browse courses on Geospatial Data Analysis
Show steps
  • Choose a project idea.
  • Gather the data you need.
  • Clean and prepare the data.
  • Analyze the data.
  • Visualize the data.
Create a data visualization or map using geospatial data
Creating a data visualization or map using geospatial data will help you apply the concepts of the course and reinforce what you learn.
Browse courses on Geospatial Data Analysis
Show steps
  • Choose a dataset.
  • Clean and prepare the data.
  • Choose a visualization tool.
  • Create your visualization.
Create a blog post or tutorial on a geospatial data analysis topic
Creating a blog post or tutorial on a geospatial data analysis topic will help you reinforce your understanding of the concepts and share your knowledge with others.
Browse courses on Geospatial Data Analysis
Show steps
  • Choose a topic.
  • Do some research.
  • Write your post or tutorial.
  • Publish your post or tutorial.
Attend a networking event for geospatial professionals
Attending a networking event for geospatial professionals will help you connect with others in the field and learn about potential job opportunities.
Browse courses on Geospatial Data Analysis
Show steps
  • Find a networking event for geospatial professionals.
  • Attend the event.
  • Introduce yourself to other attendees.
  • Talk to people about your interests and goals.

Career center

Learners who complete Climate Geospatial Analysis on Python with Xarray will develop knowledge and skills that may be useful to these careers:
Forest Ecologist
A Forest Ecologist studies the interactions between trees, other plants, animals, and the environment in order to manage forests and protect biodiversity. The geospatial analysis learned in Climate Geospatial Analysis on Python with Xarray may be useful for studying forest ecosystems and understanding how they are impacted by climate change.
See salaries and explore the career path for Forest Ecologist
Geographer
Geographers study the Earth's surface and how it is used by humans. They may also develop solutions to environmental problems. Climate Geospatial Analysis on Python with Xarray helps build a foundation for geospatial data analysis, which is a key component of geographic work.
See salaries and explore the career path for Geographer
Remote Sensing Scientist
Remote Sensing Scientists use satellite and other remote sensing technologies to study the Earth's surface. They may also develop solutions to environmental problems. Climate Geospatial Analysis on Python with Xarray helps build a foundation for analyzing geospatial data, a key skillset for Remote Sensing Scientists.
See salaries and explore the career path for Remote Sensing Scientist
Atmospheric Scientist
An Atmospheric Scientist typically researches and forecasts conditions in the Earth's atmosphere to predict the weather, climate, and other atmospheric factors. Through the use of Climate Geospatial Analysis on Python with Xarray, an Atmospheric Scientist may find the course useful in better understanding the complex datasets that are commonly used in this career field.
See salaries and explore the career path for Atmospheric Scientist
Oceanographer
Oceanographers study the oceans and how they affect the climate. They may also develop solutions to ocean-related problems. Climate Geospatial Analysis on Python with Xarray may be useful for an Oceanographer as it develops foundational skills in using Python and Xarray for geospatial data analysis, which can be applied to oceanographic studies.
See salaries and explore the career path for Oceanographer
Meteorologist
Meteorologists study the atmosphere and how it affects the weather. They may also develop solutions to weather-related problems. Climate Geospatial Analysis on Python with Xarray may be useful to a Meteorologist as it develops a foundation in using Python and Xarray to analyze meteorological data.
See salaries and explore the career path for Meteorologist
Environmental Scientist
Environmental Scientists study the environment and how it is affected by human activity. They may also develop solutions to environmental problems. Climate Geospatial Analysis on Python with Xarray may be useful for this career as it builds a foundation for understanding how to use geospatial data analysis in an environmental context.
See salaries and explore the career path for Environmental Scientist
Hydrologist
Hydrologists study the movement and quality of water on the Earth's surface and underground. They may also develop solutions to water problems. Climate Geospatial Analysis on Python with Xarray may be useful for a Hydrologist as it develops foundational skills in using Python and Xarray for geospatial data analysis, which can be applied to hydrological studies.
See salaries and explore the career path for Hydrologist
Research Scientist
Research Scientists conduct research in a variety of fields, including climate science, environmental science, and geography. Climate Geospatial Analysis on Python with Xarray may be useful to Research Scientists as it builds a foundational understanding of using Python and Xarray for geospatial data analysis in various research contexts.
See salaries and explore the career path for Research Scientist
Wildlife Biologist
Wildlife Biologists study wildlife and their habitats. They may also develop solutions to wildlife-related problems. Climate Geospatial Analysis on Python with Xarray may be useful for a Wildlife Biologist as it helps build foundational skills in analyzing geospatial data, which can be applied to wildlife studies and conservation efforts.
See salaries and explore the career path for Wildlife Biologist
Water Resources Engineer
Water Resources Engineers design and manage water resources systems. They may also develop solutions to water-related problems. Climate Geospatial Analysis on Python with Xarray may be useful to a Water Resources Engineer as it develops foundational skills in analyzing geospatial data, particularly in a water resources context.
See salaries and explore the career path for Water Resources Engineer
Environmental Engineer
Environmental Engineers use the principles of engineering, soil science, biology, and chemistry to develop solutions to environmental problems. They are typically responsible for protecting human health and the environment. Climate Geospatial Analysis on Python with Xarray may be useful for an Environmental Engineer in helping them develop a skillset that supports analysis of geospatial data.
See salaries and explore the career path for Environmental Engineer
Soil Scientist
Soil Scientists study the soil and how it is used by plants and animals. They may also develop solutions to soil-related problems. Climate Geospatial Analysis on Python with Xarray may be useful to a Soil Scientist as it builds foundational skills in analyzing geospatial data, particularly in an agricultural or environmental context.
See salaries and explore the career path for Soil Scientist
GIS Specialist
GIS Specialists use geographic information systems (GIS) to create and manage maps and other geospatial data. They may also develop solutions to problems using GIS. Climate Geospatial Analysis on Python with Xarray may be useful for this role given its focus on the use of Python, a common programming and scripting language used in GIS.
See salaries and explore the career path for GIS Specialist
Urban Planner
Urban Planners develop plans for the use of land in urban areas. They may also develop solutions to urban problems. Climate Geospatial Analysis on Python with Xarray may be useful for an Urban Planner as it may facilitate the gathering, analysis, and visualization of geospatial data for urban planning purposes.
See salaries and explore the career path for Urban Planner

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Climate Geospatial Analysis on Python with Xarray.
Geospatial Analysis
Save
Offers a comprehensive overview of geospatial analysis, providing a solid theoretical foundation for the course's practical applications.
Geospatial Analysis
Paperback
Check
Geospatial Analysis
Kindle Edition
Check
Cover image
Python for Data Analysis
Save
Introduces Python programming for data analysis, handling, processing, cleanup, and visualization. Familiarity with Python will help in getting the most out of the course.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Python for Data Analysis
Save
Will help learners brush up on their data manipulation skills, as the course assumes familiarity with Pandas.
Python for Data Analysis: Data Wrangling with...
Paperback
$$
Python for Data Analysis
Kindle Edition
$$
Python for Data Analysis: Data Wrangling with...
Paperback
$$$
Cover image
Python Data Science Handbook
Save
The book offers a comprehensive guide to data science with Python, covering essential concepts and techniques.
Python Data Science Handbook: Essential Tools for...
Paperback
$$$
Cover image
An Introduction to Statistical Learning
Save
Offers a comprehensive introduction to statistical learning, providing a solid theoretical foundation for the course's practical applications.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Deep Learning with Python, Second Edition
Save
Delves into deep learning, a rapidly growing field that is revolutionizing data analysis and can provide additional context for the course's topics.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Machine Learning, Animated
Save
The book provides a great introduction to machine learning, a field that is closely related to data analysis and can enhance understanding of the course material.
Machine Learning, Animated
Paperback
Check
Machine Learning, Animated
Kindle Edition
Check

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Course image
Effort
45 minutes for the guided part + 45 minutes for practice
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Danilo Lessa Bernardineli
Language
English
Good to know
Develops skills and techniques that are applied in climate and geographic studies
Implements hands-on labs and interactive material
Emphasizes the use of Xarray and Python for manipulating and visualizing geospatial data
Focuses on a real-world dataset from the Brazilian Amazon, providing practical relevance
Requires additional software and tools that may not be readily available to all learners
Taught by Danilo Lessa Bernardineli, who has expertise in the field
Share and help others discover this course.
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