Instructor Guide Overview

Purpose of This Guide

This instructor guide accompanies Data Visualization with Python: See the Data --- Perception Science, Design Thinking, and the Complete Python Visualization Stack. It provides course planning resources, teaching notes, discussion prompts, assessment materials, and strategies for addressing common student difficulties. The guide assumes you have read the textbook and are familiar with its structure, progressive climate project, and pedagogical approach.

Who the Book Is For

The textbook targets three overlapping audiences: data analysts who can wrangle data with pandas but produce poor charts, programmers adding visualization to their skill set, and business analysts transitioning from Excel to Python. All readers are expected to have Python 3.10+ proficiency, pandas fundamentals, basic statistics knowledge, and comfort with Jupyter notebooks.

As an instructor, your students will likely cluster in one of these profiles. Tailor your emphasis accordingly: analysts benefit most from the design and perception chapters (Parts I--II), programmers benefit from explicit design constraints and critique exercises, and Excel converts benefit from direct comparisons between spreadsheet workflows and Python equivalents.

The Progressive Climate Project

A global climate dataset --- temperature anomalies, atmospheric CO2 concentrations, and sea-level measurements spanning 1870--2024 --- threads through every chapter. Students begin with raw, ugly exploratory charts in Chapter 10 and end with a polished Streamlit dashboard and PDF report in Chapter 34. This project is the backbone of the course: it gives students a consistent reference point and shows how each technique transforms the same data from adequate to excellent.

Assign the progressive project milestones as they arise naturally in the chapter sequence. By the capstone, students will have a portfolio of 15+ versions of the same visualization, each demonstrating a different technique or principle. This progression is one of the most powerful teaching tools in the book.

The Eight-Step Chapter Structure

Every chapter follows a consistent eight-step structure that supports active learning:

  1. Opening vignette --- A motivating scenario grounding the chapter in a real-world problem.
  2. Learning objectives --- Explicit Bloom's-taxonomy-aligned outcomes.
  3. Main content --- Conceptual foundations interleaved with code examples and design critiques.
  4. Key takeaways --- Concise summary of core ideas.
  5. Exercises --- Graduated from recall to creative application.
  6. Quiz --- Self-assessment questions.
  7. Case studies --- Two in-depth worked examples per chapter.
  8. Further reading --- Curated pointers to primary sources.

Use the opening vignette as a class warm-up discussion. Assign exercises selectively based on your time constraints. The case studies work well as in-class walkthroughs or take-home analysis assignments. Quizzes can be used as low-stakes formative assessments at the start of the following class.

Assessment Philosophy

This course emphasizes creation over memorization. Students should be assessed primarily on their ability to produce effective visualizations and articulate why their design choices work, using perception science vocabulary. Recommended assessment weighting:

  • Weekly exercises and quizzes (20%): Low-stakes practice drawn from the chapter exercises and quizzes. Grade for completion and effort rather than perfection.
  • Progressive climate project milestones (25%): Periodic check-ins on the climate project, graded against the rubrics in the Additional Assessments section.
  • Design critiques (15%): Students analyze and critique real-world visualizations, applying principles from Parts I--II. These can be peer-reviewed.
  • Midterm (15%): Covers Chapters 1--15 (perception, design, and matplotlib foundations). See the Additional Assessments section.
  • Capstone project (25%): A complete data-to-dashboard pipeline on a dataset of the student's choosing. See the rubric in Additional Assessments.

Avoid assessing students purely on code correctness. A chart that runs without errors but violates perception principles, uses misleading scales, or lacks proper annotation should score lower than a chart with minor code issues but strong design reasoning.

Adapting to Your Context

Three syllabi are provided:

  • 15-week semester: Full coverage of all 35 chapters. Best for a dedicated data visualization course.
  • 10-week quarter: Essential chapters only. Skips specialized and advanced topics. Best for a quarter system or a module within a broader data science course.
  • Self-paced: For independent learners or flipped classrooms. Includes milestones and self-assessment checkpoints.

Each syllabus identifies which chapters to cover, which to assign as reading, and which to skip. All three paths ensure students complete the progressive climate project.

Using the Companion Materials

The chapter-by-chapter teaching notes provide time allocation, common student struggles, suggested demos, and teaching tips for every chapter. The discussion guides offer ready-to-use prompts for each chapter. The common struggles document addresses recurring difficulties across the entire course with concrete intervention strategies. Use these materials to prepare your sessions and anticipate where students will need extra support.