At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite Data Science books – and here are the answers.
Understanding the concepts of Data Science starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.
ISL is a fundamental book and popular amongst undergrad and grad students for its clarity and simplicity with explaining concepts. The math required to understand the book is kept to a minimum, making it unique in its format.
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Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
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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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Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more.
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This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience. Determine the appropriate type of graph for your situation.
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In a way, data science has become humanity’s sixth sense. Yet it’s also probably the sense the average person understands the least. So for anyone hoping to learn more, we asked three experts to recommend their favorite data science books from introductory overviews to more advanced content on deep learning, bias in algorithms and more. The resulting reading list includes data science books covering sociological studies of how algorithms impact our daily lives as well as technical machine learning and math textbooks. These data science books include real-life applications and offer approachable explanations and insights about both the positive impacts and risks associated with this field. Whether you’re new to these concepts or someone with a more well-developed technical background, these data science books provide a range of expertise to help you further your knowledge base. Our panel of experts includes Zach Miller, lead data scientist at CreditNinja; Jeff Herman, lead data science
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You've got your basics in order – time to move on to some advanced and specialized concepts. Data Science is evolving every day, these books can help you master it.
In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society.
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