This course introduces the use of statistical analysis in Python programming to study and model climate data, specifically with the SciPy and NumPy package. Topics include data visualization, predictive model development, simple linear regression, multivariate linear regression, multivariate linear regression with interaction, and logistic regression. Strong emphasis will be placed on gathering and analyzing climate data with the Python programming language.
Modeling Climate Anomalies with Statistical Analysis
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Modeling Climate Anomalies with Statistical Analysis
This course is part of Modeling and Predicting Climate Anomalies Specialization
Instructor: Osita Onyejekwe
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
What you'll learn
Visualize and interpret climate anomalies using statistical analysis.
Use APIs to import climate data from government portals.
Visualize data in Python with matplotlib.
Skills you'll gain
Details to know
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Recently updated!
July 2024
Assessments
3 assignments
Taught in English
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This course is part of the Modeling and Predicting Climate Anomalies Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
In this module, we'll start with an introduction to the Python library, Pandas. You'll also learn the fundamentals of data visualization using Matplotlib, a powerful library for creating insightful plots and graphs. At the end of the module you will practice manipulating data with Pandas and visualizing your findings using Matplotlib.
What's included
4 videos4 readings1 assignment1 programming assignment
In this module, you will be introduced to APIs and the Python requests library, enabling you to connect and interact with web-based data services. You'll explore climate data sources from NOAA, USGS, and NWIS, and practice accessing data using the dataretrieval library.
What's included
4 videos6 readings2 assignments
In this module, you will delve into visualizing and analyzing various climate data sets, including air temperature, precipitation, groundwater level (GWL), and soil temperature and moisture. You will learn to create informative visualizations to identify patterns, trends, and anomalies in the data.
What's included
4 videos1 programming assignment1 peer review1 discussion prompt1 ungraded lab
Instructor
Offered by
Recommended if you're interested in Data Analysis
Rice University
University of Geneva
University of Colorado Boulder
Imperial College London
Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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