Sean Murphy,Abhijit Dasgupta,Benjamin Bengfort,Tony Ojeda

Practical Data Science Cookbook

As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.
Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples in the two most popular programming languages for data analysis—R and Python.
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    seaandsimplicityhar citeretfor 3 år siden
    Data is almost never in the needed form for the desired analysis.
    Sergey Gavrichenkohar citeretfor 4 år siden
    Practicing data scientists require a great number and diversity of tools to get the job done. Data practitioners scrape, clean, visualize, model, and perform a million different tasks with a wide array of tools. If you ask most people working with data, you will learn that the foremost component in this toolset is the language used to perform the analysis and modeling of the data. Identifying the best programming language for a particular task is akin to asking which world religion is correct, just with slightly less bloodshed.
    Sergey Gavrichenkohar citeretfor 4 år siden
    contemporary data science projects

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