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.
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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.
Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee.
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Any experienced professional hoping to break into data needs this book because it strikes the right balance between strategic advice and pragmatic tips you can start applying today.
This book gives experienced professionals not just the tactical tips they need to break into a Data Science career, but something even more important - the confidence to make the leap! - Nick Singh, Author of Ac…
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Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.
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WMDs, or Weapons of Math Destruction, are mathematical algorithms that supposedly take human traits and quantify them, resulting in damaging effects and the perpetuation of bias against certain groups of people.
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Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career.
The first book of its kind, The Kaggle Book assembles in o…
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Probability is simply how likely something is to happen. Whenever we're unsure about the outcome of an event, we can talk about the probabilities of certain outcomes—how likely they are. The analysis of events governed by probability is called statistics.
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These books are not required for you to learn Data Science, but they are highly recommended for you to deepen your knowledge.
Here's what to expect in Data Science for Dummies: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value. Includes coverage of big data frameworks and applications like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL.
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R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling. R is an environment for statistical analysis. R has various statistical and graphical capabilities.
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You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them.
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse…
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In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.
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Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use t…
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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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This list is curated by MentorCruise and can include Amazon affiliate links. Have any other suggestions? Add here.
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