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ABOUT THE COURSE:
Statistical Data Analysis: Applications to Atmospheric Science provides a comprehensive introduction to data analytics through theory and hands-on practice. The course covers data classification, descriptive and inferential statistics, and exploratory data analysis.Learners will develop skills in visualization, trend analysis, stochastic modeling, and forecasting techniques. It also introduces multivariate methods such as regression, clustering, Principal Component Analysis (PCA), and Self-Organizing Maps (SOM/Kohonen Maps).Every concept is supported through practical implementation using Python code provided to learners. Although the examples are drawn primarily from climate and atmospheric datasets, the methods are broadly applicable across disciplines.
INTENDED AUDIENCE: UG/PG/PhD all disciplines
INDUSTRY SUPPORT: Research industries where data is being used.
Statistical Data Analysis: Applications to Atmospheric Science provides a comprehensive introduction to data analytics through theory and hands-on practice. The course covers data classification, descriptive and inferential statistics, and exploratory data analysis.Learners will develop skills in visualization, trend analysis, stochastic modeling, and forecasting techniques. It also introduces multivariate methods such as regression, clustering, Principal Component Analysis (PCA), and Self-Organizing Maps (SOM/Kohonen Maps).Every concept is supported through practical implementation using Python code provided to learners. Although the examples are drawn primarily from climate and atmospheric datasets, the methods are broadly applicable across disciplines.
INTENDED AUDIENCE: UG/PG/PhD all disciplines
INDUSTRY SUPPORT: Research industries where data is being used.
