data-science-academy.html

---
layout: default
title: Data Science Academy
permalink: /learn/data-science/
description: Follow a structured data science learning path from foundational concepts through analysis, machine learning, projects, and real-world problem solving.
---

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</style>

<main class="academy-page">

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

      <div class="academy-breadcrumb">
        <a href="{{ '/learn/' | relative_url }}">Learn</a>
        <span> / Data Science Academy</span>
      </div>

      <div class="academy-hero-grid">

        <div>
          <span class="academy-eyebrow">
            DataForSolution Academy
          </span>

          <h1>Data Science Academy</h1>

          <p class="academy-hero-description">
            Develop the complete ability to work with data—from asking the
            right question and preparing information to building models,
            communicating results, and solving real-world problems.
          </p>

          <div class="academy-actions">
            <a
              class="academy-button academy-button-primary"
              href="#roadmap">
              Explore the roadmap
            </a>

            <a
              class="academy-button academy-button-secondary"
              href="{{ '/courses/' | relative_url }}">
              View current courses
            </a>
          </div>
        </div>

        <aside class="academy-summary">
          <h2>Academy overview</h2>

          <ul class="academy-summary-list">
            <li><strong>Level:</strong> Beginner to advanced</li>
            <li><strong>Approach:</strong> Learn, practice, build, explain</li>
            <li><strong>Focus:</strong> Real problems and real data</li>
            <li><strong>Outcome:</strong> Portfolio-ready capability</li>
          </ul>
        </aside>

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

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

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

        <h2>What you will learn to do</h2>

        <p>
          The academy is designed around practical capability rather than
          memorizing isolated tools or definitions.
        </p>
      </div>

      <div class="outcome-grid">

        <article class="outcome-card">
          <h3>Ask better questions</h3>
          <p>
            Translate broad problems into clear, measurable analytical
            questions.
          </p>
        </article>

        <article class="outcome-card">
          <h3>Prepare reliable data</h3>
          <p>
            Inspect, clean, organize, validate, and document datasets before
            analysis.
          </p>
        </article>

        <article class="outcome-card">
          <h3>Build useful analysis</h3>
          <p>
            Use statistics, visualization, programming, and modeling to find
            meaningful patterns.
          </p>
        </article>

        <article class="outcome-card">
          <h3>Explain the meaning</h3>
          <p>
            Communicate findings, uncertainty, limitations, and practical
            implications clearly.
          </p>
        </article>

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

  <section
    id="roadmap"
    class="academy-section academy-section-alt">

    <div class="academy-container">

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

        <h2>Data Science Academy roadmap</h2>

        <p>
          The roadmap progresses from understanding data to completing
          integrated, real-world projects.
        </p>
      </div>

      <div class="roadmap">

        <article class="roadmap-level">
          <div class="level-number">
            Level 1<br>
            Foundations
          </div>

          <div class="level-content">
            <h3>Understanding data and problems</h3>

            <p>
              Learn what data science is, how analytical questions are formed,
              and how data represents real people, events, systems, and
              processes.
            </p>

            <ul class="topic-list">
              <li>Data science workflow</li>
              <li>Problem definition</li>
              <li>Data types</li>
              <li>Measurement</li>
              <li>Data ethics</li>
            </ul>
            <a
  class="level-link"
  href="{{ '/learn/data-science/level-1/' | relative_url }}">
  Begin Level 1 →
</a>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 2<br>
            Statistics
          </div>

          <div class="level-content">
            <h3>Reasoning with uncertainty</h3>

            <p>
              Develop the statistical foundation needed to describe data,
              understand variation, test ideas, and interpret evidence.
            </p>

            <ul class="topic-list">
              <li>Descriptive statistics</li>
              <li>Probability</li>
              <li>Sampling</li>
              <li>Confidence intervals</li>
              <li>Hypothesis testing</li>
            </ul>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 3<br>
            SQL
          </div>

          <div class="level-content">
            <h3>Working with structured data</h3>

            <p>
              Learn to retrieve, join, summarize, filter, and validate data
              stored in relational databases.
            </p>

            <ul class="topic-list">
              <li>SELECT queries</li>
              <li>Filtering</li>
              <li>Aggregation</li>
              <li>Joins</li>
              <li>Data quality checks</li>
            </ul>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 4<br>
            Python
          </div>

          <div class="level-content">
            <h3>Programming for analysis</h3>

            <p>
              Use Python to automate repetitive work, manipulate data, perform
              analysis, and create reproducible workflows.
            </p>

            <ul class="topic-list">
              <li>Python foundations</li>
              <li>NumPy</li>
              <li>Pandas</li>
              <li>Data cleaning</li>
              <li>Reusable analysis</li>
            </ul>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 5<br>
            Visualization
          </div>

          <div class="level-content">
            <h3>Exploring and communicating data</h3>

            <p>
              Build visualizations and dashboards that clarify patterns,
              comparisons, relationships, trends, and uncertainty.
            </p>

            <ul class="topic-list">
              <li>Chart selection</li>
              <li>Exploratory analysis</li>
              <li>Dashboard design</li>
              <li>Data storytelling</li>
              <li>Responsible visualization</li>
            </ul>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 6<br>
            Machine Learning
          </div>

          <div class="level-content">
            <h3>Building predictive models</h3>

            <p>
              Learn how models are trained, evaluated, compared, interpreted,
              and applied without confusing prediction with certainty.
            </p>

            <ul class="topic-list">
              <li>Supervised learning</li>
              <li>Unsupervised learning</li>
              <li>Model evaluation</li>
              <li>Feature engineering</li>
              <li>Interpretability</li>
            </ul>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 7<br>
            Applied Work
          </div>

          <div class="level-content">
            <h3>Solving domain-specific problems</h3>

            <p>
              Apply the complete workflow to areas such as healthcare,
              business, operations, public data, research, and social impact.
            </p>

            <ul class="topic-list">
              <li>Healthcare analytics</li>
              <li>Business analytics</li>
              <li>Research workflows</li>
              <li>Decision support</li>
              <li>Responsible deployment</li>
            </ul>
          </div>
        </article>

        <article class="roadmap-level">
          <div class="level-number">
            Level 8<br>
            Capstone
          </div>

          <div class="level-content">
            <h3>Completing an end-to-end project</h3>

            <p>
              Define a problem, obtain and prepare data, conduct the analysis,
              validate the result, explain limitations, and present the work
              as a professional portfolio project.
            </p>

            <ul class="topic-list">
              <li>Project proposal</li>
              <li>Data documentation</li>
              <li>Analysis and modeling</li>
              <li>Validation</li>
              <li>Final presentation</li>
            </ul>
          </div>
        </article>

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

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

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

        <h2>Learn by solving real problems</h2>

        <p>
          Projects will connect technical skills to decisions, outcomes, and
          meaningful questions.
        </p>
      </div>

      <div class="project-grid">

        <article class="project-card">
          <span class="project-level">Beginner project</span>

          <h3>Explore a public dataset</h3>

          <p>
            Clean and summarize a dataset, identify important patterns, and
            explain what the data can and cannot tell us.
          </p>

          <span class="project-status">Project guide coming soon</span>
        </article>

        <article class="project-card">
          <span class="project-level">Intermediate project</span>

          <h3>Build an analytical dashboard</h3>

          <p>
            Create a dashboard that helps a user understand trends,
            comparisons, performance measures, and areas requiring attention.
          </p>

          <span class="project-status">Project guide coming soon</span>
        </article>

        <article class="project-card">
          <span class="project-level">Advanced project</span>

          <h3>Develop a decision-support model</h3>

          <p>
            Build and evaluate a model, document uncertainty and limitations,
            and explain how it could responsibly support human decisions.
          </p>

          <span class="project-status">Project guide coming soon</span>
        </article>

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

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

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

        <h2>How each level will work</h2>

        <p>
          Every stage will connect knowledge, application, reflection, and
          communication.
        </p>
      </div>

      <div class="method-grid">

        <article class="method-card">
          <div class="method-number">1</div>
          <h3>Learn</h3>
          <p>
            Understand the concept, purpose, assumptions, and practical use.
          </p>
        </article>

        <article class="method-card">
          <div class="method-number">2</div>
          <h3>Practice</h3>
          <p>
            Apply the concept through exercises and focused examples.
          </p>
        </article>

        <article class="method-card">
          <div class="method-number">3</div>
          <h3>Build</h3>
          <p>
            Combine skills in projects based on meaningful problems.
          </p>
        </article>

        <article class="method-card">
          <div class="method-number">4</div>
          <h3>Explain</h3>
          <p>
            Communicate the result, evidence, uncertainty, and limitations.
          </p>
        </article>

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

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

      <h2>Begin with the available learning resources</h2>

      <p>
        The full academy curriculum will be developed progressively. Current
        courses in SQL, machine learning, visualization, and related subjects
        can support your foundation today.
      </p>

      <a
        class="academy-button academy-button-primary"
        href="{{ '/courses/' | relative_url }}"
        style="background:#123d34; color:#ffffff;">
        Browse current courses
      </a>

    </div>
  </section>

</main>