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 IT Education Association

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

3 modules

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

Shareable certificate

Add to your LinkedIn profile

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 videos5 readings1 assignment1 programming assignment

4 videos Total 55 minutes

Introduction to the Course 3 minutes
Meet the Instructor 1 minute
Introduction to Pandas for Data Loading and Exploration 26 minutes
Introduction to Matplotlib and Seaborn for Data Visualization 25 minutes

5 readings Total 46 minutes

Course Updates and Accessibility Support 1 minute
Earn Academic Credit for your Work! 10 minutes
Course Support 10 minutes
Pandas 15 minutes
Course Assignments with Jupyter 10 minutes

1 assignment Total 15 minutes

Pandas and Matplotlib 15 minutes

1 programming assignment Total 30 minutes

Introduction to Pandas and Matplotlib 30 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 videos Total 32 minutes

Introduction to APIs 9 minutes
Introduction to NOAA Website for Data Collection 9 minutes
Introduction to USGS Website and Available Data 6 minutes
Collecting GWL Data FROM USGS via NWIS 8 minutes

6 readings Total 105 minutes

APIs, JSON, and Python Requests 15 minutes
API Hands-On (Lab Activity) 30 minutes
NOAA API and Available Data 10 minutes
Datasets by USGS 10 minutes
Methods and Datasets via NWIS 10 minutes
Access Streamflow Data via NWIS (Lab Activity) 30 minutes

2 assignments Total 16 minutes

API Hands-On 15 minutes
Access Streamflow Data via NWIS 1 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.

What's included

4 videos1 programming assignment1 peer review1 discussion prompt1 ungraded lab

4 videos Total 28 minutes

Visualizing & Analyzing Air Temperature Data 7 minutes
Visualizing & Analyzing Precipitation Data 5 minutes
Visualizing & Analyzing GWL Data 6 minutes
Visualizing & Analyzing Soil Temperature and Moisture Data 9 minutes

1 programming assignment Total 45 minutes

Analyzing Climate Data 45 minutes

1 peer review Total 15 minutes

Visualizing Climate Data 15 minutes

1 discussion prompt Total 10 minutes

Anomalies and Climate Data 10 minutes

1 ungraded lab Total 45 minutes

Visualizing Climate Data 45 minutes

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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