Data Science for Healthcare Claims: 1 Day Training in Minneapolis, MN
Overview
Group Discounts:
Save 10% when registering 3 or more participants
Save 15% when registering 10 or more participants
About the course:
Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session
Course Overview:
This 1 Day course provides a practical, structured understanding of how data science is applied to healthcare claims. You explore real claims components, data transformations, fraud indicators, trend patterns, KPI interpretation, and forecasting essentials, all explained in a simple, actionable way. The course bridges foundational claims knowledge with intermediate-level analytics, helping you analyze claims with accuracy and clarity. Using clear logic, real examples, and guided activities, you learn how claims data can drive decisions, reduce errors, and support financial and operational insights across payers and providers.
Learning Objectives:
By the end of the course, you will be able to:
- Understand how healthcare claims are structured and processed.
- Prepare, clean, and validate multi-line claims datasets.
- Engineer meaningful features for claims analytics.
- Recognize fraud indicators using rule-based and pattern analysis.
- Interpret trends and forecast claims costs or volumes.
- Evaluate key claims KPIs to support business decisions.
- Build a simple, end-to-end claims analysis workflow.
Target Audience:
This course is ideal for:
- Healthcare analysts & reporting executives
- Claims processing & billing teams
- Payer and TPA operations staff
- Healthcare IT professionals
- Junior data scientists entering the healthcare domain
- Students pursuing healthcare analytics
- Professionals transitioning into health data roles
Why Choose This Course?
This course simplifies complex claims data science concepts into structured, accurate insights that you can apply immediately. The trainer brings deep experience in healthcare analytics, fraud detection, and claims data workflows, ensuring each topic is explained in a clear, practical manner. With a blend of foundation and intermediate-level learning, you gain the confidence to analyze claims intelligently and contribute meaningfully to payer, provider, or analytics teams.
©2025 Mangates Tech Solutions Pvt Ltd. This content is protected by copyright law. Copy or Reproduction without permission is prohibited.
Our Royalty Referral Program
Know a team or professional who could benefit from our workshops? Refer them and earn attractive royalties for every successful registration.
For royalty-related queries, contact orders@mangates.com
Looking to strengthen claims analytics across your organization?
This course can be delivered as a customized in-house program tailored to your claims volume, data formats, coding structures, and workflow challenges. We adapt the modules to your real datasets, enabling teams to work on practical examples and refine accuracy. In-house training helps build consistent analytical skills and improves coordination between claims, billing, and reporting teams.
📧 Contact us today to schedule a customized in-house session: corporate@mangates.com
Good to know
Highlights
- 8 hours
- ages 18+
- In person
- Paid parking
Refund Policy
Location
regus MN, Minneapolis - Spaces North Loop
121 Washington Ave. N 2nd Floor
Ph No +1 469 666 9332 Minneapolis, MN 55401
How do you want to get there?
Module 1: Understanding Healthcare Claims Data
• Structure of claims: header, line items, coding fields • Diagnosis–procedure relationships • Claims life cycle: submission to adjudication • Icebreaker
Module 2: Claims Data Preparation & Cleaning
• Standardizing coding fields (ICD, CPT, NPI, POS) • Identifying anomalies, invalid values, and missingness • Cleaning & validating multi-line claims • Case Study
Module 3: Feature Engineering for Claims Analytics
• Creating utilization, cost, and risk features • Denial-related feature extraction • Identifying high-impact cost drivers • Brainstorm Activity
Frequently asked questions
Organized by
Mangates
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