P824- JULIA FOR
INSURANCE DATA SCIENCE (NON-LIFE)
Background:
Julia is a relatively new programming language
that has been gaining importance in Data Science space. The main advantage of
Julia over other Machine Learning languages is speed & security. Julia is
compiler-based language, having great capabilities for parallel processing also.
It is relatively easy to learn Julia to those having background of Python or R
or Java or C++. Julia Community is growing & its Libraries are becoming more
and more mature. Julia can be run in Jupyter Notebook also. Julia is extremely
powerful in entire Data Science value chain. Therefore, this hands-on training
has been offered for the benefit of Insurance industry.
Contents:
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What & Why Julia for Data Science
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Julia Development Environments
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Julia Language: Datatypes, Operators, Data
Structures, main programming structures and functions,
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Data Engineering using Julia
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Julia for EDA & Visualizations
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Julia for Probabilities & Simulations
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Julia for Machine Learning: Scikit Learn and
MLJ - (Unsupervised – different types of clustering), Supervised (decision
trees, random forests, basic neural networks, regression trees, etc.), GLM,
Decision Tree, Random Forest, Logistic Regression,
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Graph Analysis using Julia
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Julia applications in General Insurance Data
Science
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Hands-on Exercises
Participants’ Profile:
This hands-On training is open to all -
Insurance industry and other related organizations from within India as well as
from other countries. Officers / Executives / Manager or higher management from
IT / Data Analytics / Data Science, Actuarial, other insurance technical areas,
various functional areas (Marketing / Business Development) of General Insurance
including HI, Re-Insurance organizations, Regulators, Insurance Information
Bureau, Broking companies. Working employees from any other organization
interested in learning ‘Julia for Data Science’. Prior knowledge / skills of
Julia though not necessary, having background of either R or Python or C or C++
or Java will be more advantageous.
Duration:
5 days
Dates:
11.12.2023 – 15.12.2023