Data Science for Insurance

The Certificate Programme in Data Science for Insurance is designed to empower learners with analytical and predictive skills to improve decision-making in the insurance sector. The insurance industry is increasingly adopting big data, AI, and machine learning to enhance underwriting, claims management, fraud detection, and customer personalization. This course blends insurance domain knowledge with advanced data science techniques to prepare participants for modern insurance roles.

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Not Enrolled

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About the course

What will you learn

Fundamentals of insurance operations and regulatory framework.
Data sources in insurance: customer, claims, actuarial, IoT, telematics.
Data cleaning, preparation, and feature engineering for insurance datasets.
Risk modeling and premium pricing with predictive analytics.
Fraud detection models using machine learning.
Customer segmentation and policy recommendation engines.
Forecasting claims and loss ratios.
Dashboard creation and reporting for insurance analytics.

Who is this for

Students aspiring for careers in insurance analytics and actuarial science.
Insurance professionals upgrading to data-driven roles.
Data analysts and scientists seeking domain specialization in BFSI.
Risk managers, claims executives, and underwriters.
Professionals in InsurTech startups.

Highlights

Learn data-driven decision-making in underwriting, pricing, and claims.

Apply machine learning models for risk assessment and fraud detection.

Hands-on experience with datasets from health, life, and general insurance.

Training on Python/R, SQL, and visualization tools.

Case studies on customer behavior, policy recommendations, and retention.

Industry-recognized certification.

Eligibility

The eligibility criteria for this course is successful completion of high school (10+2) or an equivalent qualification.

Pre-Requisites:

Basic knowledge of insurance operations is preferred. Familiarity with Excel; programming knowledge (Python/R) is beneficial but not mandatory.

Syllabus:

Module 1: Introduction to Insurance and Data Science
Module 2: Data Collection and Preparation in Insurance
Module 3: Risk Modeling and Underwriting Analytics
Module 4: Fraud Detection and Claims Analytics
Module 5: Customer Analytics in Insurance
Module 6: Machine Learning Applications in Insurance
Module 7: Visualization and Dashboarding
Module 8: Regulatory and Ethical Aspects
Module 9: Capstone Project

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About this course

Level

Beginner

Duration:

30 Hours

Downloadable Files:

Features

Comprehensive insurance + data science integration.
Real-world case studies from life, health, and general insurance.
Guidance from insurance experts and data scientists.
Flexible learning with live projects and simulations.
Practical training on analytics tools (Python, Power BI, Tableau).
Career mentorship and placement support.

Mentorship

Live Sessions

Job Roles

Insurance Data Scientist
Risk & Underwriting Analyst
Claims Analytics Specialist
Fraud Detection Analyst
Actuarial Data Analyst
Customer Insights & Retention Analyst
Product Pricing Analyst
InsurTech Consultant