Data Science from Scratch: First Principles with Python

"O'Reilly Media, Inc."
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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.

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 hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
  • Collect, explore, clean, munge, and manipulate data
  • Dive into the fundamentals of machine learning
  • Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
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About the author

Joel Grus is a software engineer at Google. Before that he worked as a data scientist at multiple startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com.

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Additional Information

Publisher
"O'Reilly Media, Inc."
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Published on
Apr 14, 2015
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Pages
330
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ISBN
9781491904398
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Language
English
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Genres
Computers / Data Modeling & Design
Computers / Data Processing
Computers / Data Transmission Systems / General
Computers / Databases / Data Mining
Computers / Databases / General
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Content Protection
This content is DRM free.
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Read Aloud
Available on Android devices
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Eligible for Family Library

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Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions.

But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope.

Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. 

Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. 

But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data.

 Each chapter will cover a different technique in aspreadsheet so you can follow along:

Mathematical optimization, including non-linear programming andgenetic algorithmsClustering via k-means, spherical k-means, and graphmodularityData mining in graphs, such as outlier detectionSupervised AI through logistic regression, ensemble models, andbag-of-words modelsForecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulationMoving from spreadsheets into the R programming language

You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.

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