Elements of Data Science

Informationen zum Autor Allen Downey is a Staff Producer at Brilliant and Professor Emeritus at Olin College, where he taught Modeling and Simulation and other classes related to software and data science. He is the author of several textbooks, including Think Python , Think Bayes , and Elements of Data Science . Previously, Downey taught at Wellesley College and Colby College. He received his Ph.D. in computer science from the University of California/Berkeley in 1997. His undergraduate and master's degrees are from the Civil Engineering department at MIT. He is the author of Probably Overthinking It , a blog about data science and Bayesian statistics. Klappentext 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:  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 Zusammenfassung 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:  Choose questions, data, and methods that go together Find data online or collect it yourself Clean and validate data

Product details

• Authors: Allen B Downey, Allen B. Downey
• Publisher: No Starch Press
• Languages: English
• Product format: Paperback / Softback
• Release: 03.10.2023, delayed
• EAN: 9781718502901
• ISBN: 978-1-71850-290-1
• No. of pages: 304
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT