data-science-module-2.html

---
layout: default
title: Types and Structures of Data
permalink: /learn/data-science/level-1/module-2/
description: Learn how qualitative, quantitative, structured, semi-structured, and unstructured data differ and why data type affects analysis.
---

<style>
  .lesson-page {
    background: #f7faf9;
    color: #18312a;
  }

  .lesson-container {
    max-width: 920px;
    margin: 0 auto;
  }

  .lesson-hero {
    padding: 78px 24px 68px;
    background: linear-gradient(
      135deg,
      #071f1a 0%,
      #0c3329 55%,
      #145040 100%
    );
    color: #ffffff;
  }

  .lesson-breadcrumb {
    margin-bottom: 28px;
    font-size: 0.94rem;
    color: #dcefe8;
  }

  .lesson-breadcrumb a {
    color: #bde8d9;
    text-decoration: none;
  }

  .lesson-breadcrumb a:hover {
    text-decoration: underline;
  }

  .lesson-eyebrow {
    display: inline-block;
    margin-bottom: 14px;
    font-size: 0.8rem;
    font-weight: 700;
    letter-spacing: 0.12em;
    text-transform: uppercase;
    color: #bde8d9;
  }

  .lesson-hero h1 {
    margin: 0 0 18px;
    font-size: clamp(2.5rem, 6vw, 4.5rem);
    line-height: 1.08;
  }

  .lesson-hero p {
    max-width: 760px;
    margin: 0;
    font-size: 1.15rem;
    line-height: 1.75;
    color: #e4f3ee;
  }

  .lesson-section {
    padding: 64px 24px;
  }

  .lesson-section-alt {
    background: #eaf3f0;
  }

  .lesson-overview-section {
    background: #ffffff;
  }

  .lesson-heading {
    margin-bottom: 24px;
  }

  .lesson-label {
    display: inline-block;
    margin-bottom: 10px;
    font-size: 0.8rem;
    font-weight: 700;
    letter-spacing: 0.1em;
    text-transform: uppercase;
    color: #28755f;
  }

  .lesson-heading h2 {
    margin: 0 0 14px;
    font-size: clamp(1.8rem, 4vw, 2.8rem);
    color: #102c25;
  }

  .lesson-heading p,
  .lesson-content p {
    line-height: 1.8;
    color: #556863;
  }

  .lesson-content p {
    margin: 0 0 22px;
    font-size: 1.04rem;
  }

  .lesson-overview-grid {
    display: grid;
    grid-template-columns: repeat(2, minmax(0, 1fr));
    gap: 20px;
  }

  .lesson-overview-card {
    padding: 26px;
    border: 1px solid #dce8e3;
    border-radius: 14px;
    background: #f8fbfa;
    color: #18312a;
  }

  .overview-card-label {
    display: inline-block;
    margin-bottom: 10px;
    font-size: 0.76rem;
    font-weight: 700;
    letter-spacing: 0.09em;
    text-transform: uppercase;
    color: #28755f;
  }

  .lesson-overview-card h3 {
    margin: 0 0 10px;
    color: #102c25;
  }

  .lesson-overview-card p {
    margin: 0;
    line-height: 1.65;
    color: #556863;
  }

  .concept-box {
    margin: 28px 0;
    padding: 28px;
    border-left: 5px solid #28755f;
    border-radius: 10px;
    background: #ffffff;
    box-shadow: 0 7px 20px rgba(15, 53, 43, 0.05);
  }

  .concept-box h3 {
    margin: 0 0 10px;
    color: #123d34;
  }

  .concept-box p {
    margin: 0;
  }

  .type-grid {
    display: grid;
    grid-template-columns: repeat(2, minmax(0, 1fr));
    gap: 22px;
    margin-top: 28px;
  }

  .type-card {
    padding: 26px;
    background: #ffffff;
    border: 1px solid #dce8e3;
    border-radius: 14px;
  }

  .type-card h3 {
    margin: 0 0 10px;
    color: #123d34;
  }

  .type-card p {
    margin: 0 0 14px;
    line-height: 1.65;
    color: #5b6b66;
  }

  .example-list {
    margin: 0;
    padding-left: 20px;
  }

  .example-list li {
    margin-bottom: 8px;
    line-height: 1.55;
    color: #556863;
  }

  .structure-list {
    display: grid;
    gap: 18px;
    margin-top: 28px;
  }

  .structure-item {
    padding: 24px;
    background: #ffffff;
    border-radius: 12px;
    border: 1px solid #dce8e3;
  }

  .structure-item h3 {
    margin: 0 0 9px;
    color: #102c25;
  }

  .structure-item p {
    margin: 0;
    line-height: 1.7;
    color: #556863;
  }

  .data-table-wrapper {
    overflow-x: auto;
    margin-top: 28px;
  }

  .data-table {
    width: 100%;
    min-width: 650px;
    border-collapse: collapse;
    background: #ffffff;
  }

  .data-table th,
  .data-table td {
    padding: 15px;
    border: 1px solid #d8e5e0;
    text-align: left;
  }

  .data-table th {
    background: #123d34;
    color: #ffffff;
  }

  .data-table td {
    color: #4f625c;
  }

  .practice-box {
    padding: 30px;
    border-radius: 15px;
    background: #123d34;
    color: #ffffff;
  }

  .practice-box h2 {
    margin: 0 0 14px;
    color: #ffffff;
  }

  .practice-box p {
    color: #e4f3ee;
  }

  .practice-box ol {
    margin: 20px 0 0;
    padding-left: 22px;
  }

  .practice-box li {
    margin-bottom: 12px;
    line-height: 1.65;
    color: #f1faf6;
  }

  .build-box {
    padding: 30px;
    border: 1px solid #cfe0da;
    border-radius: 15px;
    background: #ffffff;
  }

  .build-box h2 {
    margin: 0 0 14px;
    color: #102c25;
  }

  .build-box p,
  .build-box li {
    line-height: 1.7;
    color: #556863;
  }

  .reflection-list {
    margin: 22px 0 0;
    padding-left: 22px;
  }

  .reflection-list li {
    margin-bottom: 14px;
    line-height: 1.7;
    color: #556863;
  }

  .lesson-navigation {
    display: flex;
    justify-content: space-between;
    gap: 16px;
    margin-top: 34px;
    flex-wrap: wrap;
  }

  .lesson-button {
    display: inline-block;
    padding: 13px 20px;
    border-radius: 8px;
    background: #123d34;
    color: #ffffff;
    font-weight: 700;
    text-decoration: none;
  }

  .lesson-button:hover {
    background: #0b2d25;
    color: #ffffff;
  }

  .lesson-button-secondary {
    background: #dfece8;
    color: #123d34;
  }

  .lesson-button-secondary:hover {
    background: #d1e3dd;
    color: #123d34;
  }

  @media (max-width: 760px) {
    .lesson-overview-grid,
    .type-grid {
      grid-template-columns: 1fr;
    }

    .lesson-hero {
      padding: 64px 20px 55px;
    }

    .lesson-section {
      padding: 50px 20px;
    }
  }
</style>

<main class="lesson-page">

  <section class="lesson-hero">
    <div class="lesson-container">

      <div class="lesson-breadcrumb">
        <a href="{{ '/learn/' | relative_url }}">Learn</a>
        <span> / </span>

        <a href="{{ '/learn/data-science/' | relative_url }}">
          Data Science Academy
        </a>

        <span> / </span>

        <a href="{{ '/learn/data-science/level-1/' | relative_url }}">
          Level 1
        </a>

        <span> / Module 2</span>
      </div>

      <span class="lesson-eyebrow">
        Data Science Academy · Level 1 · Module 2
      </span>

      <h1>Types and Structures of Data</h1>

      <p>
        Data can represent categories, quantities, text, images, measurements,
        documents, signals, or events. Understanding its type and structure
        determines how it should be stored, analyzed, and interpreted.
      </p>

    </div>
  </section>

  <section class="lesson-section lesson-overview-section">
    <div class="lesson-container">

      <div class="lesson-heading">
        <span class="lesson-label">Lesson overview</span>

        <h2>What you will learn and create</h2>

        <p>
          This lesson introduces the major ways data is classified and
          organized.
        </p>
      </div>

      <div class="lesson-overview-grid">

        <article class="lesson-overview-card">
          <span class="overview-card-label">Distinguish</span>
          <h3>Qualitative and quantitative data</h3>
          <p>
            Recognize whether a variable describes a category or measures a
            numerical quantity.
          </p>
        </article>

        <article class="lesson-overview-card">
          <span class="overview-card-label">Recognize</span>
          <h3>Data structure</h3>
          <p>
            Identify structured, semi-structured, and unstructured
            information.
          </p>
        </article>

        <article class="lesson-overview-card">
          <span class="overview-card-label">Understand</span>
          <h3>Measurement scales</h3>
          <p>
            Explain how nominal, ordinal, interval, and ratio variables
            differ.
          </p>
        </article>

        <article class="lesson-overview-card">
          <span class="overview-card-label">Build</span>
          <h3>A data classification guide</h3>
          <p>
            Classify variables from a real-world dataset by type, structure,
            and measurement scale.
          </p>
        </article>

      </div>

    </div>
  </section>

  <section class="lesson-section">
    <div class="lesson-container lesson-content">

      <div class="lesson-heading">
        <span class="lesson-label">Learn</span>
        <h2>Variables carry different kinds of meaning</h2>
      </div>

      <p>
        A variable is a characteristic recorded for an observation. Age,
        diagnosis, temperature, product category, travel time, and customer
        comments are all variables, but they do not represent information in
        the same way.
      </p>

      <p>
        The type of a variable affects which summaries, visualizations, and
        analytical methods are appropriate. A category such as blood type
        should not be treated like a numerical measurement such as body
        temperature.
      </p>

      <div class="concept-box">
        <h3>Core idea</h3>

        <p>
          Before analyzing a variable, determine what it means—not merely how
          it appears in a file.
        </p>
      </div>

    </div>
  </section>

  <section class="lesson-section lesson-section-alt">
    <div class="lesson-container">

      <div class="lesson-heading">
        <span class="lesson-label">Meaning</span>
        <h2>Qualitative and quantitative data</h2>
      </div>

      <div class="type-grid">

        <article class="type-card">
          <h3>Qualitative data</h3>

          <p>
            Qualitative data represents qualities, names, groups, labels, or
            categories.
          </p>

          <ul class="example-list">
            <li>Blood type</li>
            <li>Department name</li>
            <li>Product category</li>
            <li>Diagnosis</li>
            <li>Customer feedback</li>
          </ul>
        </article>

        <article class="type-card">
          <h3>Quantitative data</h3>

          <p>
            Quantitative data represents amounts, counts, measurements, or
            numerical values.
          </p>

          <ul class="example-list">
            <li>Age</li>
            <li>Weight</li>
            <li>Temperature</li>
            <li>Number of purchases</li>
            <li>Travel time</li>
          </ul>
        </article>

      </div>

    </div>
  </section>

  <section class="lesson-section">
    <div class="lesson-container lesson-content">

      <div class="lesson-heading">
        <span class="lesson-label">Storage and organization</span>
        <h2>Structured, semi-structured, and unstructured data</h2>
      </div>

      <div class="structure-list">

        <article class="structure-item">
          <h3>Structured data</h3>

          <p>
            Information organized into clearly defined rows, columns, fields,
            and data types. Examples include spreadsheets, relational
            databases, and transaction tables.
          </p>
        </article>

        <article class="structure-item">
          <h3>Semi-structured data</h3>

          <p>
            Information that does not follow a traditional table but contains
            labels, keys, tags, or markers that provide organization. Examples
            include JSON, XML, emails, and system logs.
          </p>
        </article>

        <article class="structure-item">
          <h3>Unstructured data</h3>

          <p>
            Information without a fixed row-and-column format. Examples
            include clinical notes, documents, photographs, medical images,
            audio recordings, and videos.
          </p>
        </article>

      </div>

    </div>
  </section>

  <section class="lesson-section lesson-section-alt">
    <div class="lesson-container">

      <div class="lesson-heading">
        <span class="lesson-label">Measurement</span>
        <h2>Four common measurement scales</h2>

        <p>
          Measurement scales describe the meaning and relationships contained
          within recorded values.
        </p>
      </div>

      <div class="data-table-wrapper">
        <table class="data-table">
          <thead>
            <tr>
              <th>Scale</th>
              <th>Meaning</th>
              <th>Example</th>
              <th>Important property</th>
            </tr>
          </thead>

          <tbody>
            <tr>
              <td>Nominal</td>
              <td>Names or categories</td>
              <td>Blood type</td>
              <td>No natural order</td>
            </tr>

            <tr>
              <td>Ordinal</td>
              <td>Ordered categories</td>
              <td>Pain: mild, moderate, severe</td>
              <td>Order exists, distance is uncertain</td>
            </tr>

            <tr>
              <td>Interval</td>
              <td>Equal numerical intervals</td>
              <td>Temperature in Celsius</td>
              <td>Zero does not mean absence</td>
            </tr>

            <tr>
              <td>Ratio</td>
              <td>Equal intervals with a true zero</td>
              <td>Weight or duration</td>
              <td>Ratios are meaningful</td>
            </tr>
          </tbody>
        </table>
      </div>

    </div>
  </section>

  <section class="lesson-section">
    <div class="lesson-container">

      <div class="practice-box">
        <h2>Practice: Classify each variable</h2>

        <p>
          For each variable below, identify whether it is qualitative or
          quantitative and determine its likely measurement scale.
        </p>

        <ol>
          <li>Patient identification number</li>
          <li>Age in years</li>
          <li>Satisfaction rating: poor, fair, good, excellent</li>
          <li>Appointment date</li>
          <li>Diagnosis category</li>
          <li>Travel distance in miles</li>
          <li>Written clinical note</li>
        </ol>
      </div>

    </div>
  </section>

  <section class="lesson-section lesson-section-alt">
    <div class="lesson-container">

      <div class="build-box">
        <span class="lesson-label">Build</span>

        <h2>Create a data classification guide</h2>

        <p>
          Use the small dataset you created in Module 1 or choose another
          everyday dataset. Create a table with these columns:
        </p>

        <div class="data-table-wrapper">
          <table class="data-table">
            <thead>
              <tr>
                <th>Variable</th>
                <th>Meaning</th>
                <th>Qualitative or quantitative</th>
                <th>Measurement scale</th>
                <th>Structure or format</th>
              </tr>
            </thead>

            <tbody>
              <tr>
                <td>Date</td>
                <td>When the event occurred</td>
                <td>Quantitative or temporal</td>
                <td>Interval</td>
                <td>Structured</td>
              </tr>

              <tr>
                <td>Event type</td>
                <td>Category of event</td>
                <td>Qualitative</td>
                <td>Nominal</td>
                <td>Structured</td>
              </tr>

              <tr>
                <td>Duration</td>
                <td>Length of the event</td>
                <td>Quantitative</td>
                <td>Ratio</td>
                <td>Structured</td>
              </tr>
            </tbody>
          </table>
        </div>

        <p>
          Add at least five variables. For each one, explain why you selected
          that classification.
        </p>
      </div>

    </div>
  </section>

  <section class="lesson-section">
    <div class="lesson-container lesson-content">

      <div class="lesson-heading">
        <span class="lesson-label">Reflect</span>
        <h2>Questions to deepen your understanding</h2>
      </div>

      <ul class="reflection-list">
        <li>
          Can a number function as a label rather than a measurement?
        </li>

        <li>
          Could the same variable be stored differently in two systems?
        </li>

        <li>
          What analytical mistakes could occur when an ordinal variable is
          treated as a precise numerical measurement?
        </li>

        <li>
          Why is unstructured data often more difficult to analyze?
        </li>
      </ul>

    </div>
  </section>

  <section class="lesson-section lesson-section-alt">
    <div class="lesson-container lesson-content">

      <div class="lesson-heading">
        <span class="lesson-label">Apply</span>
        <h2>Why data type matters in real work</h2>
      </div>

      <p>
        Healthcare systems contain numerical measurements, diagnosis
        categories, medical images, clinical notes, timestamps, and signals.
        Business systems may contain transaction tables, customer comments,
        emails, product images, and activity logs.
      </p>

      <p>
        Each type requires different methods for storage, preparation,
        visualization, and analysis. Correctly identifying data type is
        therefore one of the first responsibilities in any data science
        project.
      </p>

      <div class="lesson-navigation">

        <a
          class="lesson-button lesson-button-secondary"
          href="{{ '/learn/data-science/level-1/module-1/' | relative_url }}">
          ← Previous: What Is Data?
        </a>

        <a
          class="lesson-button"
          href="{{ '/learn/data-science/level-1/' | relative_url }}">
          Return to Level 1 →
        </a>

      </div>

    </div>
  </section>

</main>