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 books – and here are the answers.
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Understanding the concepts of Data 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.
Weapons of Math Destruction is a 2016 American book about the societal impact of algorithms, written by Cathy O'Neil. It explores how some big data algorithms are increasingly used in ways that reinforce preexisting inequality.
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The best-selling author of Naked Economics defies the odds with a book about statistics that you’ll welcome and enjoy. Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” ...
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Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. ...
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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. ...
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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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A statistical national treasure' Jeremy Vine, BBC Radio 2'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? ...
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These books are not required for you to learn Data, but they are highly recommended for you to deepen your knowledge.
Business intelligence (BI) used to be so simple in theory anyway. Integrate and copy data from your transactional systems into a specialized relational database, apply BI reporting and query tools and add business users. Job done. No longer. ...
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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. ...
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When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.
Throughout the …
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The Signal and the Noise: Why So Many Predictions Fail – but Some Don't is a 2012 book by Nate Silver detailing the art of using probability and statistics as applied to real-world circumstances.
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If you’re involved with a startup, analytics help you find your way to the right product and market before the money runs out. But with a flood of information available, where do you start? ...
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A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large.Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? ...
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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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