data-science-level-1.html

---
layout: default
title: Data Science Level 1
permalink: /learn/data-science/level-1/
description: Begin the Data Science Academy by learning how data represents real-world events, how analytical problems are defined, and how responsible data science begins.
---

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<main class="curriculum-page">

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

      <div class="curriculum-breadcrumb">
        <a href="{{ '/learn/' | relative_url }}">Learn</a>
        <span> / </span>
        <a href="{{ '/learn/data-science/' | relative_url }}">
          Data Science Academy
        </a>
        <span> / Level 1</span>
      </div>

      <span class="curriculum-eyebrow">
        Data Science Academy · Level 1
      </span>

      <h1>Understanding Data and Problems</h1>

      <p>
        Before learning advanced tools, models, or programming, a data
        scientist must first understand what data represents, how problems
        are defined, what evidence can support, and where responsible
        analysis begins.
      </p>

    </div>
  </section>

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

      <div class="section-heading">
        <span class="section-label">Learning outcomes</span>

        <h2>What you should understand by the end</h2>

        <p>
          Level 1 develops the reasoning foundation required for every later
          stage of the Data Science Academy.
        </p>
      </div>

      <div class="outcomes-grid">

        <article class="outcome-card">
          <h3>Recognize what data represents</h3>
          <p>
            Understand that data is a recorded representation of real people,
            events, measurements, systems, or processes.
          </p>
        </article>

        <article class="outcome-card">
          <h3>Define analytical problems</h3>
          <p>
            Convert broad concerns into clear questions that data may be able
            to investigate.
          </p>
        </article>

        <article class="outcome-card">
          <h3>Evaluate evidence carefully</h3>
          <p>
            Distinguish observations, assumptions, interpretations, and
            conclusions.
          </p>
        </article>

        <article class="outcome-card">
          <h3>Think responsibly</h3>
          <p>
            Consider privacy, fairness, context, limitations, and possible
            harm before analysis begins.
          </p>
        </article>

      </div>
    </div>
  </section>

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

      <div class="section-heading">
        <span class="section-label">Curriculum</span>

        <h2>Level 1 modules</h2>

        <p>
          These six modules build the conceptual foundation for statistics,
          SQL, Python, visualization, and machine learning.
        </p>
      </div>

      <div class="module-list">

        <article class="module-card">
          <div class="module-number">
            Module 1
          </div>

          <div class="module-content">
            <h3>What Is Data?</h3>

            <p>
              Explore how observations become recorded information and why
              data is never the same thing as the full reality it represents.
            </p>

            <ul class="module-topics">
              <li>Observations</li>
              <li>Measurements</li>
              <li>Records</li>
              <li>Variables</li>
              <li>Representation</li>
            </ul>

            <a
  class="module-link"
  href="{{ '/learn/data-science/level-1/module-1/' | relative_url }}">
  Start Module 1 →
</a>
          </div>
        </article>

        <article class="module-card">
          <div class="module-number">
            Module 2
          </div>

          <div class="module-content">
            <h3>Types and Structures of Data</h3>

            <p>
              Learn how data differs by meaning, format, scale, source, and
              structure—and why those differences affect analysis.
            </p>

            <ul class="module-topics">
              <li>Qualitative data</li>
              <li>Quantitative data</li>
              <li>Structured data</li>
              <li>Unstructured data</li>
              <li>Measurement scales</li>
            </ul>

           <a
  class="module-link"
  href="{{ '/learn/data-science/level-1/module-2/' | relative_url }}">
  Start Module 2 →
</a>
          </div>
        </article>

        <article class="module-card">
          <div class="module-number">
            Module 3
          </div>

          <div class="module-content">
            <h3>The Data Science Process</h3>

            <p>
              Understand the complete workflow from identifying a problem to
              collecting data, analyzing evidence, communicating results, and
              evaluating impact.
            </p>

            <ul class="module-topics">
              <li>Problem definition</li>
              <li>Data collection</li>
              <li>Preparation</li>
              <li>Analysis</li>
              <li>Communication</li>
            </ul>

            <span class="module-status">Lesson planned</span>
          </div>
        </article>

        <article class="module-card">
          <div class="module-number">
            Module 4
          </div>

          <div class="module-content">
            <h3>Asking Good Analytical Questions</h3>

            <p>
              Learn how to turn vague concerns into specific, measurable,
              answerable, and useful questions.
            </p>

            <ul class="module-topics">
              <li>Broad problems</li>
              <li>Research questions</li>
              <li>Measurable outcomes</li>
              <li>Scope</li>
              <li>Decision context</li>
            </ul>

            <span class="module-status">Lesson planned</span>
          </div>
        </article>

        <article class="module-card">
          <div class="module-number">
            Module 5
          </div>

          <div class="module-content">
            <h3>Data Quality, Bias, and Ethics</h3>

            <p>
              Examine how missing information, poor measurement, biased
              samples, privacy concerns, and unfair assumptions can affect
              conclusions.
            </p>

            <ul class="module-topics">
              <li>Data quality</li>
              <li>Missing data</li>
              <li>Bias</li>
              <li>Privacy</li>
              <li>Fairness</li>
            </ul>

            <span class="module-status">Lesson planned</span>
          </div>
        </article>

        <article class="module-card">
          <div class="module-number">
            Module 6
          </div>

          <div class="module-content">
            <h3>Thinking Like a Data Scientist</h3>

            <p>
              Develop the habit of questioning assumptions, checking evidence,
              considering alternatives, recognizing uncertainty, and
              explaining limitations.
            </p>

            <ul class="module-topics">
              <li>Curiosity</li>
              <li>Skepticism</li>
              <li>Evidence</li>
              <li>Uncertainty</li>
              <li>Communication</li>
            </ul>

            <span class="module-status">Lesson planned</span>
          </div>
        </article>

      </div>
    </div>
  </section>

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

      <div class="section-heading">
        <span class="section-label">Learning framework</span>

        <h2>How every module will work</h2>

        <p>
          Each module will follow the same five-part structure so that
          understanding leads to practical capability.
        </p>
      </div>

      <div class="framework-grid">

        <article class="framework-card">
          <div class="framework-number">1</div>
          <h3>Learn</h3>
          <p>
            Understand the concept, purpose, assumptions, and common mistakes.
          </p>
        </article>

        <article class="framework-card">
          <div class="framework-number">2</div>
          <h3>Practice</h3>
          <p>
            Strengthen understanding through focused questions and examples.
          </p>
        </article>

        <article class="framework-card">
          <div class="framework-number">3</div>
          <h3>Build</h3>
          <p>
            Apply the module through a meaningful analytical task.
          </p>
        </article>

        <article class="framework-card">
          <div class="framework-number">4</div>
          <h3>Reflect</h3>
          <p>
            Examine assumptions, limitations, uncertainty, and alternatives.
          </p>
        </article>

        <article class="framework-card">
          <div class="framework-number">5</div>
          <h3>Apply</h3>
          <p>
            Connect the concept to healthcare, business, research, or society.
          </p>
        </article>

      </div>
    </div>
  </section>

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

      <div class="section-heading">
        <span class="section-label">Level completion</span>

        <h2>Practice, project, and reflection</h2>

        <p>
          Learners will not complete Level 1 by reading alone.
        </p>
      </div>

      <div class="assessment-grid">

        <article class="assessment-card">
          <span class="assessment-label">Practice</span>

          <h3>Question classification exercise</h3>

          <p>
            Review real-world questions and determine whether they are
            descriptive, comparative, predictive, causal, or not yet suitable
            for data analysis.
          </p>
        </article>

        <article class="assessment-card">
          <span class="assessment-label">Project</span>

          <h3>Turn a real problem into a data plan</h3>

          <p>
            Select a practical problem, define the decision that matters,
            identify the data needed, and document possible limitations,
            biases, and ethical concerns.
          </p>
        </article>

        <article class="assessment-card">
          <span class="assessment-label">Reflection</span>

          <h3>What can the data not tell us?</h3>

          <p>
            Explain the difference between what is observed, what is inferred,
            and what remains unknown.
          </p>
        </article>

      </div>
    </div>
  </section>

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

      <div class="completion-box">
        <h2>Level 1 completion standard</h2>

        <p>
          A learner is ready to continue when they can clearly define a
          problem, explain what the available data represents, identify
          important limitations, and describe how an analysis could support a
          real decision without overstating what the evidence proves.
        </p>
      </div>

    </div>
  </section>

  <section class="curriculum-cta">
    <div class="curriculum-cta-content">

      <h2>The curriculum is ready. The first lesson comes next.</h2>

      <p>
        Module 1 will begin by exploring a foundational question:
        What is data, and how does recorded information relate to reality?
      </p>

      <a
        class="curriculum-button"
        href="{{ '/learn/data-science/' | relative_url }}">
        Return to Data Science Academy
      </a>

    </div>
  </section>

</main>