I am a research scholar working in the field of cloud and aerosol modelling at the Centre for Atmospheric Sciences, IIT Delhi. My research interests include designing equilibrium and transient climate simulations, running the global climate model and statistically analyzing the model output together with various in-situ and satellite observations in order to understand various highlights and challenges in the model parametrization schemes.
This site is a personal blog. I will be posting blogs related to my work, some on programming and may be some random thoughts on life in general. And since I am also a big foodie, I am gonna share some of my food adventures too… 😀
PhD in Atmospheric Sciences, 2021
IIT Delhi
MSc in Physics, 2012
Jamia Millia Islamia
BSc in Instrumentation, 2009
Jamia Millia Islamia
Instead of finding K in K means clustering through visualization, let us find K mathematically.
Second part of bootstrapping and monte carlo. Applying the theory through python and practical examples
Second part of initial introduction to R. Topics include arrays, factors and dataframes.
Initial introduction to R. Topics include data types, data structures such as named lists, vectors and matrices.
Extracting relevant results from a particular dataset requires a rigorous understanding of its statistical properties - mainly the type of distribution the data possesses. For some underlying statistical distribution in the data, parametric tests are applied to infer statistical properties of a particular estimator. It may also happen that the data does not possess any specific statistical distribution in which case, nonparametric tests are applied. In statistical paradigm, we define an estimator to be a rule to conclude statistic of some unknown parameter from the data.