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My fascination with data science began when during the early years of my
career, I learnt how monitoring and prediction of Madden Julian Oscillation (MJO)
is accomplished across a major research center, they use indices derived from principal
component analysis to keep track of a phenomenon that affects weather and
climate across the globe. Such efficient use of a statistical procedure
certainly piqued my interest in the field of data analytics.  Since then I have been using analytical tools
such as regression analysis to study the propagation of MJO, Empirical
Orthogonal Function analysis to identify the associated dominant spatial
variability and other such tools to study the simulation of MJO in the state of
the art weather and climate models. But the analytics I use are basic data analysis
methods.  

 Moreover, the current models in
the field are handicapped by the inherent error that are introduced when
assumptions are made to linearize the state equations, With an increasing inclination
of modeling committee towards AI, to grow further in my career I should possess
strong technical and computational skills which when added to my domain
knowledge would certainly give me an edge. With this objective in mind I aspire
to pursue Master’s in data analytics and science from Ryerson University.

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I have consistently been a top performer in academics, be it high
school, under graduation or job based induction training. My aptitude for
mathematical logic allowed me to excel in all four mathematics courses in my
bachelors program. The branch of study being Electronics &
Telecommunication, computer science and programming was an intrinsic part of
the curriculum.  I aced in languages such
as C, C++, JAVA and have an understanding of data structures and operating
systems.

 I ranked under 600 in a national
level aptitude test for engineers in my branch, which opened the opportunity
for me to work as a researcher in Indian Institute of Tropical Meteorology,
Pune. I use scientific data analysis and visualization tools such as NCAR
Command Language (NCL), Grads, Ferret and FORTRAN, to analyze satellite data
and model output, compare them and suggest improvements in the model. 

University of Ryerson’s data science and analytics program provides a
well-designed interdisciplinary curriculum which encompasses mathematical and
computational technical skills and develops flair for research through its
major research project milestone. The faculty in the program reflects the
multi-disciplinary aspect of data science; the program has experts in
mathematics, computer science, management and arts. I believe they will bring
their academic proficiency and experience through instruction which is highly
desirable to me. The industry partnership of Ryerson University enables access
to data from varied fields, working and experimenting on which would give me
hands on opportunity to translate my theoretical knowledge into practice and
would better prepare for my future career.

 

 

 

 

 

 

 

 

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