DTSA 5741 Modeling Climate Anomalies with Statistical Analysis

  • Specialization: Climate Modeling for Data Science
  • Instructor: Osita Onyejekwe

Learning Outcomes

  • Visualize and interpret climate anomalies using statistical analysis.
  • Use APIs to import climate data from government portals. 
  • Visualize data in Python with matplotlib. 

Course Content

Duration: 2h

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. 

Duration: 2h

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.

Duration: 2h

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.

Duration: 2h

You will complete a peer reviewed final project worth 30% of your grade. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.


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