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  • Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python

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Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python

by Sam Lau (Author), Joseph Gonzalez (Author), Deborah Nolan (Author) Format: Paperback

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As an aspiring data scientist, you appreciate why organizations rely on data for important decisions—whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.

Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the "technical/nontechnical" divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas.

  • Refine a question of interest to one that can be studied with data
  • Pursue data collection that may involve text processing, web scraping, etc.
  • Glean valuable insights about data through data cleaning, exploration, and visualization
  • Learn how to use modeling to describe the data
  • Generalize findings beyond the data

Read more
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  1. ISBN-10
    1098113004
  2. ISBN-13
    978-1098113001
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    October 24, 2023
  6. Language
    English
  7. Dimensions
    7 x 1.2 x 9.19 inches
  8. Print length
    594 pages
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From the Publisher

Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python

From the Preface

Data science is exciting work. The ability to draw insights from messy data is valuable for all kinds of decision making across business, medicine, policy, and more. This book, Learning Data Science, aims to prepare readers to do data science. To achieve this, we’ve designed this book with the following special features:

Focus on the fundamentals

Technologies come and go. While we work with specific technologies in this book, our goal is to equip readers with the fundamental building blocks of data science. We do this by revealing how to think about data science problems and challenges, and by covering the fundamentals behind the individual technologies. Our aim is to serve readers even as technologies change.

Cover the entire data science lifecycle

Instead of just focusing on a single topic, like how to work with data tables or how to apply machine learning techniques, we cover the entire data science lifecycle—the process of asking a question, obtaining data, understanding the data, and understanding the world. Working through the entire lifecycle can often be the hardest part of being a data scientist.

Use real data

To be prepared for working on real problems, we consider it essential to learn from examples that use real data, with their warts and all. We chose the datasets presented in this book by carefully picking from actual data analyses that have made an impact, rather than using overly refined or synthetic data.

Apply concepts through case studies

We’ve included extended case studies throughout the book that follow or extend analyses from other data scientists. These case studies show readers how to navigate the data science lifecycle in real settings.

Combine both computational and inferential thinking

On the job, data scientists need to foresee how the decisions they make when writing code and how the size of a dataset might affect statistical analysis. To prepare readers for their future work, Learning Data Science integrates computational and statistical thinking. We also motivate statistical concepts through simulation studies rather than mathematical proofs.

Expected Background Knowledge

We expect readers to be proficient in Python and understand how to use built-in data structures like lists, dictionaries, and sets; import and use functions and classes from other packages; and write functions from scratch. We also use the numpy Python package without introduction but don’t expect readers to have much prior experience using it. Readers will get more from this book if they also know a bit of probability, calculus, and linear algebra, but we aim to explain mathematical ideas intuitively.

Editorial Reviews

Review

This is the book I wish we had when we first came up with the term data scientist to describe what we do. If you're looking to be in data science/ engineering, AI, or machine learning, this is where you need to start.
—DJ Patil, PhD, first US Chief Data Scientist

I helped develop and teach the UC Berkeley data science course based on Learning Data Science. This book provides the foundational skills and concepts needed to solve real-world data science problems.
—Fernando Pérez, Cofounder of Project Jupyter and UC Berkeley Professor

Learning Data Science is a great introduction to the field of data science for beginners and working professionals alike. Read it for the exciting case studies.
—Siddharth Yadav, Freelance Data Scientist

There's not a lot of data science books that focus on exploratory data analysis and how that segues into the real modeling process. This book does just that and should serve anyone wanting a deep-dive in how to explore data.
—Thomas Nield, Consultant/Instructor, Nield Consulting Group/Yawman Flight

Learning Data Science provides a fantastic, comprehensive introduction to the data science lifecycle. It builds a strong foundation in data science principles and techniques, enabling readers to tackle the complex problems we face each day. What truly sets this book apart is the abundance of modern, real-world examples.
—Sona Jeswani, Machine Learning Engineer for Google Search Ads Quality

About the Author

Sam Lau is a PhD candidate at UC San Diego. He designs novel interfaces for learning and teaching data science, and his research has been published in top-tier conferences in human-computer interaction and end-user programming. Sam instructed and helped design flagship data science courses at UC Berkeley. These courses have grown to serve thousands of students every year and their curriculum is used by universities across the world.

Joseph (Joey) Gonzalez is an assistant professor in the EECS department at UC Berkeley and a founding member of the new UC Berkeley RISE Lab. His research interests are at the intersection of machine learning and data systems, including: dynamic deep neural networks for transfer learning, accelerated deep learning for high-resolution computer vision, and software platforms for autonomous vehicles. Joey is also co-founder of Turi Inc. (formerly GraphLab), which was based on his work on the GraphLab and PowerGraph Systems. Turi was recently acquired by Apple Inc.

Deborah (Deb) Nolan is Professor of Statistics and Associate Dean for Undergraduate Studies in the Division of Computing, Data Science, and Society at the University of California, Berkeley, where she holds the Zaffaroni Family Chair in Undergraduate Education. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research. Her pedagogical approach connects research, practice and education, and she is co-author of 4 textbooks: Stat Labs, Teaching Statistics, Data Science in R, and Communicating with Data.

Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ October 24, 2023
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 594 pages
  • ISBN-10 ‏ : ‎ 1098113004
  • ISBN-13 ‏ : ‎ 978-1098113001
  • Item Weight ‏ : ‎ 2.31 pounds
  • Dimensions ‏ : ‎ 7 x 1.2 x 9.19 inches
  • Best Sellers Rank: #1,215,231 in Books (See Top 100 in Books)

About the authors

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  1. Sam Lau

    Sam Lau

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    Sam Lau is an Assistant Teaching Professor in the Halıcıoğlu Data Science Institute at UC San Diego. His research creates novel interfaces for learning and teaching data science, including the popular Pandas Tutor tool which serves over 40,000 people per year. He is the author of Learning Data Science, a data science textbook published by O’Reilly Media in 2023.

    See more on the author's page
  2. Joseph Gonzalez

    Joseph Gonzalez

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    Full content visible, double tap to read brief content.

    Joseph Gonzalez is an Associate Professor of Computer Science at UC Berkeley, the co-director of the RISE and Sky Computing Labs, and a member of the Berkeley AI Research (BAIR) group. He leads the Large Models Systems (LM-Sys) and Gorilla projects which have significantly advanced open research in large language models and their supporting systems. Joseph is also co-founder and VP of product at Aqueduct. His research addresses problems in data systems, large language models, neural network design, compilers and distributed systems for large scale machine learning, natural language processing computer vision, robotics, autonomous driving, and graph analytics. Prior to joining Berkeley, Gonzalez co-founded Turi Inc (formerly GraphLab) based on his thesis work and created the GraphX project (now part of Apache Spark). Gonzalez’s innovative work has earned him significant recognition, including the Okawa Research Grant, the NSF Expedition Award, and the NSF Early CAREER Award.

    See more on the author's page
  3. Deborah Nolan

    Deborah Nolan

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    Full content visible, double tap to read brief content.

    Deborah (Deb) Nolan is Professor Emerita of Statistics and Associate Dean for Students in the College of Computing, Data Science, and Society at the University of California, Berkeley, where she held the Zaffaroni Family Chair in Undergraduate Education. Her pedagogical approach connects research with practice through case studies. She has co-authored six books: Learning Data Science, Communicating with Data, Stat Labs, Teaching Statistics, Data Science in R, and XML and Web Technologies for Data Sciences with R.

    See more on the author's page
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