Modeling Climate Anomalies with Statistical Analysis
Learn to analyze and visualize climate data using Python, Pandas, and Matplotlib.This course introduces statistical analysis techniques
Learn to analyze and visualize climate data using Python, Pandas, and Matplotlib.This course introduces statistical analysis techniques
This course introduces statistical analysis techniques for modeling climate anomalies using Python. Students will learn to use Pandas for data manipulation, Matplotlib for visualization, and APIs to collect climate data from sources like NOAA and USGS. The curriculum covers data visualization, predictive model development, and various regression techniques. Participants will gain hands-on experience in gathering, analyzing, and visualizing climate data, focusing on air temperature, precipitation, groundwater levels, and soil conditions. This course provides a strong foundation in Python programming for climate data analysis and is part of CU Boulder's Master of Science in Data Science program.
Instructors:
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What you'll learn
Use Pandas for efficient climate data manipulation and analysis
Create informative visualizations of climate data using Matplotlib
Access and collect climate data from NOAA and USGS using APIs
Analyze and interpret various climate datasets (temperature, precipitation, etc.)
Identify climate anomalies through statistical analysis
Develop basic predictive models for climate data
Skills you'll gain
This course includes:
1 Hours PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to statistical analysis of climate data using Python. Students will learn to use Pandas for data manipulation and Matplotlib for creating insightful visualizations. The curriculum covers accessing climate data from government portals using APIs, and analyzing various climate datasets including air temperature, precipitation, groundwater levels, and soil conditions. Participants will gain hands-on experience in identifying patterns, trends, and anomalies in climate data through statistical analysis and visualization techniques. The course emphasizes practical skills in Python programming for climate data analysis and interpretation.
Introduction to Python for Data Analysis
Module 1 · 2 Hours to complete
Collecting Climate Data
Module 2 · 2 Hours to complete
Visualizing & Analyzing Climate Data
Module 3 · 2 Hours to complete
Fee Structure
Instructor
Assistant Professor at the University of Colorado Boulder
Dr. Osita Onyejekwe is an Assistant Professor at the University of Colorado Boulder, where he specializes in multivariate regression models and machine learning techniques. His research focuses on estimating weather patterns, analyzing glacier recession behavior, and developing financial models related to profit gains, losses, and revenue. In addition to his quantitative research interests, Dr. Onyejekwe explores topics in planetary systems, abiogenesis, philosophy, and theology, reflecting a diverse academic curiosity that bridges the sciences and humanities. His interdisciplinary approach aims to contribute valuable insights across various fields while enhancing the understanding of complex systems and their interactions.
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