Modeling Climate Anomalies with Statistical Analysis

University of Colorado Boulder via Coursera

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Overview

This course introduces the use of statistical analysis in Python programming to study and model climate data, specifically with the Sci Py and Num Py 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. This course can be taken for academic credit as part of CU Boulders Master of Science in...

Syllabus

Introduction to Python for Data Analysis

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.

Collecting Climate Data

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.

Visualizing & Analyzing Climate Data

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.