Quant Methods of Data Analysis · Earth And Environmental Sciences
I learned a good deal of Matlab coding in this course. I also think many of the homework problems were on topics that will remain relevant to me for my research. I particularly liked how much time we spent on spectral analyses.
Statistical techniques for data science such as hypothesis testing, Fourier analysis, interpolation, regression, and more
I learned various data analysis methods and how they would be applicable to geoscience datasets.
I had a math deficiency, and this course was helpful as an introductory course to widely used data analysis methods. As a result, I now have a better understanding of linear algebra, statistics, and Matlab coding applied to natural sciences.
Would recommend if you have a decently strong math background 1 Columbia University: Arts & Sciences Fall 2021 Course: EESCGR6908_001_2021_3-QUANTMETHODSOFDATAANALYSIS : EESCGR6908_001_2021_3 - QUANT METHODS OF DATA ANALYSIS Instructor: Alberto Malinverno
This course was very useful. The quantitative skills I learned (eg. statistics and linear algebra) as well as computation skills (eg. python) are useful for research.
Quantitative data analysis- Fourier transforms, empirical orthogonal functions, least squares, spectral analysis.
A variety of statistics and data analysis methods that feature prominently in the earth sciences, along with the strengths and weakness associated with each method.
the greatest skill learnt is how to code in MATLAB and being able to translate my code from R to MATLAB for debugging and vice versa.
Generalized versions of and rationales behind many interesting and useful data analysis techniques