Actuarial Notes

CAS DISC DA – Introduction to Data and Analytics

By The Institutes · 2023

Study guide: Exam DISC-DA

The Institutes' online course for CAS DISC DA, and the only source material the DISC DA syllabus names: the course contains the learning objectives and all the material needed to meet them, and ends in the exam. The printed textbook has the same content; review notes, a course guide of sample questions and answers, and flashcards are optional extras.

On the syllabus

  • DISC DA — topics 1–10, the whole course, which the CAS says "contains learning objectives and all the educational material to meet these objectives".

1 Planning an Insurer Data Modeling Project

  • a. Harnessing the Power of Data
  • b. Applying Data Quality Principles
  • c. Documenting Data
  • d. Data Classifications

2 Collecting Data for Insurer Models

  • a. Insurer Operational Data
  • b. Statistical Plans
  • c. External Data Sources

3 Preparing Data for Analysis

  • a. Managing Dataframes
  • b. Querying Data
  • c. Joining Data Tables
  • d. Indexes, Null Values, and User-Defined Functions
  • e. Extracting Data from Online Sources and Data Marts
  • f. Testing Data

4 Working With Different Types of Data

  • a. Working with Structured Data
  • b. Working with Unstructured Data
  • c. Addressing Common Data Issues

5 Analyzing Data With Visualizations

  • a. Planning an Effective Data Exploration
  • b. Data Exploration Fundamentals
  • c. Fundamentals of Exploratory Data Visualizations
  • d. Creating Plots

6 Presenting Data Effectively

  • a. Keys to Effective Presentation Visualizations
  • b. Selecting the Right Presentation Visualization
  • c. Maximizing a Presentation Visualization's Effectiveness

7 Understanding Fundamental Modeling Concepts

  • a. Basic Data Modeling Techniques
  • b. Similarity and Distance in Data Modeling
  • c. Predictive Model Training and Evaluation

8 Basic Data Analysis

  • a. Traditional Data Analysis
  • b. Analyzing Data with Classification Trees
  • c. Analyzing Data with Linear Functions
  • d. Segmenting Data with Cluster Analysis

9 Preparing Data for Insurance Applications

  • a. Preparing Data for Insurance Applications Using SQL
  • b. Creating Datasets for Claims Models
  • c. Creating Datasets for Underwriting Models

10 Ethical and Societal Considerations of Data Analytics

  • a. Using Data Ethically
  • b. How Regulations and Professional Codes Affect Data Modeling
  • c. Applying Ethics Checklists to Insurance Data Modeling
  • d. Societal Impact of Credit-Based Insurance Scores

Sources