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

Modeling Climate Anomalies with Statistical Analysis
This course is part of Modeling and Predicting Climate Anomalies Specialization

Instructor: Osita Onyejekwe
Access provided by Vivekananda Global University
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
7 hours to complete
Flexible schedule
Learn at your own pace
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
Tools you'll learn
Details to know

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Assessments
3 assignments
Taught in English
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Build your subject-matter expertise
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.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate

There are 3 modules in this course
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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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