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 GI industry.

 Contents:

 

ü  What & Why Julia for Data Science

ü  Julia Development Environments

ü  Julia Language: Datatypes, Operators, Data Structures, main programming structures and functions,

ü  Data Engineering using Julia

ü  Julia for EDA & Visualizations

ü  Julia for Probabilities & Simulations

ü  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,

ü  Graph Analysis using Julia

ü  Julia applications in General Insurance Data Science

ü  Hands-on Exercises

 

Participants’ Profile:

This hands-On training is open to General 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:              19.12.2022 - 23.12.2022