[[Actuarial Notes Wiki|Wiki]] / **DISC DA (CAS)** ## DISC DA (CAS) The **CAS DISC DA – Introduction to Data and Analytics** is a self-paced online course administered by [[The Institutes]] in partnership with the CAS, assessed via 75 application-based multiple-choice questions in 100 minutes, covering [[Data Management]], [[Data Preparation]], [[Exploratory Data Analysis]], [[Predictive Modeling]], and the ethical use of data in insurance contexts. <div class="callout-cols-2"> > [!answer]- 📅 Exam Windows 2026 > > <div class="highlight-upcoming" data-date-col="0"></div> > > Window | Dates > -|- > Winter | Jan 15 – Mar 15, 2026 > Spring | Apr 15 – Jun 15, 2026 > Summer | Jul 15 – Sep 15, 2026 > Fall | Oct 15 – Dec 15, 2026 > > - [Register](https://web.theinstitutes.org/casualty-actuarial-society) (~$255, includes one exam attempt) > [!answer]- 📄 Download Resources 1 PDF > > - [DISC DA Content Outline (PDF)](https://www.casact.org/sites/default/files/2023-04/DISC_DA_Content_Outline.pdf) > - [CAS DISC Course Page](https://www.casact.org/exams-admissions/exams/acas-exams/cas-data-and-insurance-series-courses) </div> ### Course Topics > [!example]- 1. Planning an Insurer Data Modeling Project > ### Planning an Insurer Data Modeling Project > Introduces the role of data in insurance, principles of [[Data Quality]], documentation practices, and the classification of data types used in insurer modeling projects. > a. Harnessing the Power of Data > b. Applying [[Data Quality]] Principles > c. Documenting Data > d. [[Data Classifications]] > [!example]- 2. Collecting Data for Insurer Models > ### Collecting Data for Insurer Models > Surveys the primary sources of insurer data, including operational systems, statistical plans, and external data providers. > a. Insurer Operational Data > b. [[Statistical Plans]] > c. External Data Sources > [!example]- 3. Preparing Data for Analysis > ### Preparing Data for Analysis > Covers practical data preparation techniques including managing [[Dataframes]], querying, joining tables, handling null values, extracting from online sources and [[Data Marts]], and testing data quality. > 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 > [!example]- 4. Working With Different Types of Data > ### Working With Different Types of Data > Distinguishes between [[Structured Data]] and [[Unstructured Data]] and addresses common data issues encountered in insurance contexts. > a. Working with [[Structured Data]] > b. Working with [[Unstructured Data]] > c. Addressing Common Data Issues > [!example]- 5. Analyzing Data With Visualizations > ### Analyzing Data With Visualizations > Introduces the principles of [[Exploratory Data Analysis]] and the creation of effective data visualizations and plots. > a. Planning an Effective [[Data Exploration]] > b. Data Exploration Fundamentals > c. Fundamentals of [[Exploratory Data Visualizations]] > d. Creating Plots > [!example]- 6. Presenting Data Effectively > ### Presenting Data Effectively > Focuses on selecting and optimizing presentation visualizations to communicate analytical findings clearly and persuasively. > a. Keys to Effective Presentation Visualizations > b. Selecting the Right Presentation Visualization > c. Maximizing a Presentation Visualization's Effectiveness > [!example]- 7. Understanding Fundamental Modeling Concepts > ### Understanding Fundamental Modeling Concepts > Covers foundational concepts in [[Data Modeling]], including similarity and distance measures, and the training and evaluation of [[Predictive Models]]. > a. Basic [[Data Modeling]] Techniques > b. Similarity and Distance in Data Modeling > c. [[Predictive Model]] Training and Evaluation > [!example]- 8. Basic Data Analysis > ### Basic Data Analysis > Surveys traditional data analysis methods alongside machine learning techniques including [[Classification Trees]], [[Linear Regression]], and [[Cluster Analysis]]. > a. Traditional Data Analysis > b. Analyzing Data with [[Classification Trees]] > c. Analyzing Data with [[Linear Functions]] > d. Segmenting Data with [[Cluster Analysis]] > [!example]- 9. Preparing Data for Insurance Applications > ### Preparing Data for Insurance Applications > Applies [[SQL]] and dataset construction techniques specifically to insurance use cases, including claims and underwriting models. > a. Preparing Data for Insurance Applications Using [[SQL]] > b. Creating Datasets for [[Claims Models]] > c. Creating Datasets for [[Underwriting Models]] > [!example]- 10. Ethical and Societal Considerations of Data Analytics > ### Ethical and Societal Considerations of Data Analytics > Examines the ethical responsibilities of data practitioners, the role of regulation and professional codes, ethics checklists, and the societal impact of [[Credit-Based Insurance Scores]]. > 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]]