University of Colorado Boulder
Modeling and Predicting Climate Anomalies Specialization

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University of Colorado Boulder

Modeling and Predicting Climate Anomalies Specialization

Master Climate Data Analysis and Modeling. Learn to analyze climate data, evaluate global policies, and apply machine learning to predict extreme climate events.

Osita Onyejekwe

Instructor: Osita Onyejekwe

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Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyze global climate policies and their impact

  • Apply statistical analysis techniques in Python to model and interpret climate data using tools like SciPy and NumPy

  • Develop and implement machine learning models to predict extreme climate events and analyze climate anomalies using real-world datasets

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Taught in English
Recently updated!

November 2024

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Specialization - 3 course series

What you'll learn

  • Identify climate goals and policies, such as the Kyoto Protocol and the Paris Agreement. 

  • Describe the impacts of climate change.

  • Evaluate the technological, economic, and policy challenges associated climate change mitigation strategies.

Skills you'll gain

Category: Climate Policy
Category: Renewable Energy
Category: Climate Change Mitigation
Category: Nuclear Technology

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

Category: Data visualization
Category: Statistical Analysis
Category: Data Analysis
Category: Data Visualization
Category: Pandas (Python Package)

What you'll learn

  • Analyze and differentiate between various machine learning algorithms, including unsupervised and supervised methods

  • Apply dimensionality reduction techniques, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), to complex datasets

  • Implement supervised learning algorithms using Python, and evaluate their performance through practical exercises and real-world case studies.

  • Develop and apply effective clustering methods to analyze and segment data

Skills you'll gain

Category: Unsupervised Learning
Category: Climate Modeling
Category: Supervised Learning
Category: Statistical Analysis
Category: Machine Learning

Instructor

Osita Onyejekwe
University of Colorado Boulder
5 Courses1,564 learners

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