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

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.

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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

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.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

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

4 videosTotal 54 minutes

Introduction to the Course2 minutesPreview module
Meet the Instructor1 minute
Introduction to Pandas for Data Loading and Exploration25 minutes
Introduction to Matplotlib and Seaborn for Data Visualization25 minutes

4 readingsTotal 45 minutes

Earn Academic Credit for your Work!10 minutes
Course Support10 minutes
Pandas15 minutes
Course Assignments with Jupyter10 minutes

1 assignmentTotal 15 minutes

Pandas and Matplotlib15 minutes

1 programming assignmentTotal 30 minutes

Introduction to Pandas and Matplotlib30 minutes

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

4 videosTotal 31 minutes

Introduction to APIs9 minutesPreview module
Introduction to NOAA Website for Data Collection9 minutes
Introduction to USGS Website and Available Data5 minutes
Collecting GWL Data FROM USGS via NWIS7 minutes

6 readingsTotal 105 minutes

APIs, JSON, and Python Requests15 minutes
API Hands-On (Lab Activity)30 minutes
NOAA API and Available Data10 minutes
Datasets by USGS10 minutes
Methods and Datasets via NWIS10 minutes
Access Streamflow Data via NWIS (Lab Activity)30 minutes

2 assignmentsTotal 16 minutes

API Hands-On15 minutes
Access Streamflow Data via NWIS1 minute

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.