Course Curriculum

20 Chapters · 38 Lessons

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    Chapter 1: The Foundation of Visual Thinking

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      The Foundation of Visual Thinking

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      The Foundation of Visual Thinking

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    Chapter 2: Setting Up Your Visualization Environment

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      Setting Up Your Visualization Environment

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      Setting Up Your Visualization Environment

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    Chapter 3: Data Handling Essentials

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      Data Handling Essentials

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      Data Handling Essentials

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    Chapter 4: Matplotlib Fundamentals

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      Matplotlib Fundamentals

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      Matplotlib Fundamentals

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    Chapter 5: Customizing Matplotlib Aesthetics

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      Customizing Matplotlib Aesthetics

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      Customizing Matplotlib Aesthetics

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    Chapter 6: Arranging Matplotlib Plots

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      Arranging Matplotlib Plots

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      Arranging Matplotlib Plots

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    Chapter 7: Advanced Matplotlib Techniques

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      Advanced Matplotlib Techniques

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      Advanced Matplotlib Techniques

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    Chapter 8: Preparing Matplotlib for Production

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      Preparing Matplotlib for Production

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      Preparing Matplotlib for Production

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    Chapter 9: Introduction to Seaborn

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      Introduction to Seaborn

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      Introduction to Seaborn

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    Chapter 10: Seaborn for Distribution and Categorical Data

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      Seaborn for Distribution and Categorical Data

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      Seaborn for Distribution and Categorical Data

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    Chapter 11: Seaborn for Relationships and Matrix Plots

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      Seaborn for Relationships and Matrix Plots

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      Seaborn for Relationships and Matrix Plots

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    Chapter 12: Advanced Seaborn Features

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      Advanced Seaborn Features

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      Advanced Seaborn Features

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    Chapter 13: Prompting for Visualization Code

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      Prompting for Visualization Code

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      Prompting for Visualization Code

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    Chapter 14: Refining AI-Generated Visualizations

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      Refining AI-Generated Visualizations

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      Refining AI-Generated Visualizations

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    Chapter 15: Automation with AI Prompts

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      Automation with AI Prompts

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      Automation with AI Prompts

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    Chapter 16: Building Reusable Prompt Templates

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      Building Reusable Prompt Templates

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      Building Reusable Prompt Templates

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    Chapter 17: Data Storytelling and Ethics

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      Data Storytelling and Ethics

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      Data Storytelling and Ethics

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    Chapter 18: The Future of Visualization AI

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      The Future of Visualization AI

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      The Future of Visualization AI

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    Index

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      Index

About the Course

Data visualization turns complex data into insight, alignment, and action—and it is a core skill for modern data, analytics, and engineering roles. Prompting Python Data Visualization shows you how to design clear, trustworthy visuals in Python, while accelerating the entire workflow by translating natural-language intent into working visualization code with AI. You begin with the foundations of visual thinking and data handling, then build strong technical skills using Matplotlib and Seaborn. The book takes you from basic plots to advanced layouts, styling, annotations, time-series analysis, and publication-ready exports, grounded in real-world analytics and reporting use cases. As you progress, you learn prompt engineering techniques to generate charts with AI, refine and debug AI-produced code, and automate repetitive visualization tasks using reusable prompt templates. By the end of the book, you will produce professional visuals for reports, notebooks, and dashboards with speed, consistency, and confidence.

About the Author

Aleksei Aleinikov is a Senior Platform and Cloud Engineer specializing in security-first cloud architecture and scalable systems. He builds reliable Python analytics and visualization workflows, focusing on IAM, observability, and operational rigor, and actively shares hands-on engineering insights with the developer community.