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"Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python...
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Hidden from consumers, patient medical data has become a multibillion-dollar worldwide trade between our health-care providers, drug companies, and a complex web of middlemen. This great medical-data bazaar sells copies of our prescriptions, hospital records, insurance claims, blood-test results, and more, stripped of names but still containing identifiers such as year of birth, gender, and doctor. As computing grows ever more sophisticated, patient...
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"Many high-profile applications, like LinkedIn and Netflix, deliver nimble, responsive performance by reacting to user and system events as they occur. In large-scale systems, this requires efficiently monitoring, managing, and reacting to multiple event streams. Tools like Kafka, along with innovative patterns like unified log processing, help create a coherent data processing architecture for event-based applications. "Event streams in action"...
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Data science is an entirely new discipline that encompasses a new era of information, from finding criminals to predicting epidemics. But there's more to it than the vast quantities of information gathered by our computers, smartphones, and credit cards. Carefully compiled by experts in the field, 30-Second Data Science covers the basic statistical principles that drive the algorithms, how data affects us in every way-science, society, business, pleasure-along...
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Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical...
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As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions...
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"Covers how to secure and authenticate datasets and safely communicate with sources; Python programming basics for data science investigations; security concepts, like disk encryption; how to work with data in EML, MBOX, JSON, CSV, and SQL formats; and tricks for using the command-line interface to explore datasets packed with secrets"--
92) Social media analytics: techniques and insights for extracting business value out of social media
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"'Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide...
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"A compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time"--
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Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and...
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Overview: Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and any other activity that leaves a digital trail. Although the abuse of Big Data-surveillance, spying, hacking-has made headlines, it shouldn't overshadow the abundant positive applications of Big Data. In Reality Mining, Nathan Eagle and Kate Greene cut through the hype...
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"Data is collected everywhere these days, in massive quantities. But data alone doesn't do you much good. That's why data analysis -- making sense of the data -- has become a must-have skill in the fields of business, science, and social science. But it's a tough skill to acquire. The concepts are challenging, and too many books and online tutorials treat only parts of the total skillset needed. Now, though, Murach's Python for Data Analysis draws...
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Sociometrics and Human Relationships translates the latest academic research into practical business strategies and techniques as well as actionable insights, providing a wealth of examples for social network analysis and predicting trends. Gloor illustrates how to improve organizational performance by optimizing communication and collaboration through email. Based on Collaborative Innovation Networks courses which have been taught for over a decade...