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Key Features Learn how to tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize data Get beyond the theory with real-world projects Expand your numerical programming skills through step-by-step code examples and learn more about the robust features of R and Python Book Description Data's value has grown exponentially in the past decade, with 'Big Data' today being one of... more...

The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to... more...

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework... more...