"The computer world is like an intellectual Wild West, in which you can shoot anyone you wish with your ideas, if you're willing to risk the consequences. " --from Hackers & Painters: Big Ideas from the Computer Age , by Paul Graham We are living in the computer age, in a world increasingly designed and engineered by computer programmers and software designers, by people who call themselves hackers. Who are these people, what motivates them, and why should you care? Consider these facts: Everything around us is turning into computers. Your typewriter is gone, replaced by a computer. Your phone has turned into a computer. So has your camera. Soon your TV will. Your car was not only designed on computers, but has more processing power in it than a room-sized mainframe did in 1970. Letters, encyclopedias, newspapers, and even your local store are being replaced by the Internet. Hackers & Painters: Big Ideas from the Computer Age , by Paul Graham, explains this world and the motivations of the people who occupy it. In clear, thoughtful prose that draws on illuminating historical examples, Graham takes readers on an unflinching exploration into what he calls "an intellectual Wild West." The ideas discussed in this book will have a powerful and lasting impact on how we think, how we work, how we develop technology, and how we live. Topics include the importance of beauty in software design, how to make wealth, heresy and free speech, the programming language renaissance, the open-source movement, digital design, internet startups, and more.

Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-ranging, interdisciplinary field? With this book, you’ll get material from Columbia University’s "Introduction to Data Science" class in an easy-to-follow format.

Each chapter-long lecture features a guest data scientist from a prominent company such as Google, Microsoft, or eBay teaching new algorithms, methods, or models by sharing case studies and actual code they use. You’ll learn what’s involved in the lives of data scientists and be able to use the techniques they present.

Guest lectures focus on topics such as:

Machine learning and data mining algorithms

Statistical models and methods

Prediction vs. description

Exploratory data analysis

Communication and visualization

Data processing

Big data

Programming

Ethics

Asking good questions

If you’re familiar with linear algebra, probability and statistics, and have some programming experience, this book will get you started with data science.

Doing Data Science is collaboration between course instructor Rachel Schutt (also employed by Google) and data science consultant Cathy O’Neil (former quantitative analyst for D.E. Shaw) who attended and blogged about the course.

Think Python is an introduction to Python programming for students with no programming experience. It starts with the most basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters.

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.

Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.

Explore Python’s major built-in object types such as numbers, lists, and dictionaries

Create and process objects with Python statements, and learn Python’s general syntax model

Use functions to avoid code redundancy and package code for reuse

Organize statements, functions, and other tools into larger components with modules

Dive into classes: Python’s object-oriented programming tool for structuring code

Write large programs with Python’s exception-handling model and development tools

Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing

This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas:

Data issues -- The opportunities and ambiguities of the data space are evident in discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself.

The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research.

Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.

The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.