Actuarial Notes

DISC DA

Introduction to Data and Analytics (DISC DA) is one of the three Data and Insurance Series Courses (DISCs) the CAS requires for the ACAS. The DISCs are online, self-paced courses run by The Institutes. Each ends in an exam of 75 application-based multiple-choice questions, with 100 minutes to answer them, and the course fee includes one attempt. The course itself contains the learning objectives and all the material needed to meet them; a textbook with the same content, condensed review notes, a course guide of sample questions and answers, and flashcards are sold separately and are optional.

The Institutes gives the topics no exam weights.

Learning Objectives

1. Planning an Insurer Data Modeling Project

  1. Harnessing the Power of Data
  2. Applying Data Quality Principles
  3. Documenting Data
  4. Data Classifications

2. Collecting Data for Insurer Models

  1. Insurer Operational Data
  2. Statistical Plans
  3. External Data Sources

3. Preparing Data for Analysis

  1. Managing Dataframes
  2. Querying Data
  3. Joining Data Tables
  4. Indexes, Null Values, and User-Defined Functions
  5. Extracting Data from Online Sources and Data Marts
  6. Testing Data

4. Working With Different Types of Data

  1. Working with Structured Data
  2. Working with Unstructured Data
  3. Addressing Common Data Issues

5. Analyzing Data With Visualizations

  1. Planning an Effective Data Exploration
  2. Data Exploration Fundamentals
  3. Fundamentals of Exploratory Data Visualizations
  4. Creating Plots

6. Presenting Data Effectively

  1. Keys to Effective Presentation Visualizations
  2. Selecting the Right Presentation Visualization
  3. Maximizing a Presentation Visualization's Effectiveness

7. Understanding Fundamental Modeling Concepts

  1. Basic Data Modeling Techniques
  2. Similarity and Distance in Data Modeling
  3. Predictive Model Training and Evaluation

8. Basic Data Analysis

  1. Traditional Data Analysis
  2. Analyzing Data with Classification Trees
  3. Analyzing Data with Linear Functions
  4. Segmenting Data with Cluster Analysis

9. Preparing Data for Insurance Applications

  1. Preparing Data for Insurance Applications Using SQL
  2. Creating Datasets for Claims Models
  3. Creating Datasets for Underwriting Models

10. Ethical and Societal Considerations of Data Analytics

  1. Using Data Ethically
  2. How Regulations and Professional Codes Affect Data Modeling
  3. Applying Ethics Checklists to Insurance Data Modeling
  4. Societal Impact of Credit-Based Insurance Scores

Source Material

Source Material

  • Introduction to Data and Analytics (The Institutes - 2023)
    • 1–10