Modeling Climate Anomalies with Statistical Analysis
About 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
83/100
CourseAsk score
- What the provider tells you
- 32/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- data visualization using Python
- predictive model development
- simple and multivariate linear regression
- logistic regression
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Udemy
Coursera
Compared on these lists
Where this course ranks against the alternatives.