5 Best Data Science Books That You Must Read
List of 5 books based on Data Science. These books provide the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction. Check out the list of the 5 best Data Science Books.
1. Data Science From Scratch: First Principles with Python, Second
Updated for Python 3.6, this second edition of Data Science from scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an Aptitude for Mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist.
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2. Python Data Science Handbook
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
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3. Data Science from Scratch
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
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4. Introducing Data Science
Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it.
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5. Python for Data Science For Dummies
The book covers the Python fundamentals that are necessary to data analysis, including objects, functions, modules, and libraries. The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
