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Through practical projects and interesting exercises, learn how to work with data using Python—no prior programming knowledge needed!
Analyze vaccine efficacy, support for gun control, and more in this exercise-filled, beginner-friendly introduction to data science and Python, the leading programming language in the data science industry.
This clear, concise introduction to the data-science discipline is for people with no programming experience. Using Python, a beginner-friendly language popular within the industry, the book presents a basic yet powerful set of tools and methods that allow you to do real work in data science as quickly as possible—everything from answering questions and guiding decision-making under uncertainty, to creating effective data visualizations that have a real impact. Concepts are explained in simple terms, and exercises in each chapter demonstrate the practical purposes of various skill sets. Practical and hands-on, the author’s clever organization of content follows the steps of a data-science project: posing and refining questions, cleaning and validating data, exploratory analysis and identifying relationships between variables, generating predictions, and designing visualizations that tell a compelling story. Upon finishing the book, you’ll be able to execute your own data projects from start to finish!
Learn how to:
Analyze vaccine efficacy, support for gun control, and more in this exercise-filled, beginner-friendly introduction to data science and Python, the leading programming language in the data science industry.
This clear, concise introduction to the data-science discipline is for people with no programming experience. Using Python, a beginner-friendly language popular within the industry, the book presents a basic yet powerful set of tools and methods that allow you to do real work in data science as quickly as possible—everything from answering questions and guiding decision-making under uncertainty, to creating effective data visualizations that have a real impact. Concepts are explained in simple terms, and exercises in each chapter demonstrate the practical purposes of various skill sets. Practical and hands-on, the author’s clever organization of content follows the steps of a data-science project: posing and refining questions, cleaning and validating data, exploratory analysis and identifying relationships between variables, generating predictions, and designing visualizations that tell a compelling story. Upon finishing the book, you’ll be able to execute your own data projects from start to finish!
Learn how to:
- Choose questions, data, and methods that go together
- Find data online or collect it yourself
- Clean and validate data
- Explore datasets, visualizing distributions and relationships between variables
- Model data and generate predictions
- Communicating results effectively
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