Data Science For Insurance Innovators

Who Should Attend

The target audience is insurance industry leaders (actuarial, sales, marketing, underwriting, product design, claims, technology), industry regulators, and outside innovators interested in applying data science strategies for the industry. 

While this is a standalone course, it can also serve as a direct pathway to an MS in data science or computer science for learners from all undergraduate backgrounds with no prior experience required.

Takeaways

The outcome for this program is to build an insurance industry narrative around data science fundamentals with case study examples and project-based personal “use cases” to apply knowledge and skills, discussion, and explorations.  Learners achieve a fuller understanding of how to leverage emergent data science approaches for competitive advantage in their own industry.  Learners will have the opportunity to understand how to align data science strategies in support of strategic priorities including optimizing pricing and leveraging predictive modeling to:

• Reduce operational costs and expense ratios.

• Minimize claims costs and loss ratios.

• Use data to determine risk

• Increase premium growth by improving retention, selling more to existing customers, and capturing new business.

Description

This program is a condensed version of critical elements from multiple courses in Northeastern’s existing data science curriculum as applied to the insurance industry:

• Foundations of Data Science. 
  Introduces core technologies and methods that provide a foundation of understanding about modern approaches in Data Science as it relates to challenges of the insurance industry. Covers: introductory concepts in probability, statistics, and machine learning; basic data visualization techniques, and identifies case study solutions to common problems.

• Presentation and Visualization of Information.
  Introduces foundational principles, methods, and techniques of visualization to enable the creation of effective information representations suitable for exploration and discovery. Covers: design and evaluation process of visualization creation, visual representations of data, relevant principles of human vision and perception, and basic interactivity principles. Draws examples from industry.
  

• Machine Learning and Data Mining Strategies.
  Introduces common modern data science methods applied to the industry including approaches to predictive modeling, data mining, and machine-learning concepts. Introduces tools and libraries relevant to analyze insurance and insurance adjacent data sets, build predictive models, and evaluate the fit of the models. Applies concepts to common insurance value chain domain areas such as risk
  

• Project-Based Challenges.   
  Provides opportunities throughout the program for learners to apply learnings to a custom challenge modeled after their own business needs.

Program Supported By: