Packt Analytics for the Internet of Things下载

PIPI_333 2018-08-28 10:05:21

Analytics for the Internet of Things(IoT)
·Defining IoT Analytics and Challenges
·IoT Devices and Networking Protocols
·IoT Analytics for the Cloud
·Creating an AWS Cloud Analytics Environment
·Collecting All That Data - Strategies and Techniques
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相关下载链接：//download.csdn.net/download/bacca1205/10629910?utm_source=bbsseo

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Analytics for the Internet of Things(IoT) ·Defining IoT Analytics and Challenges ·IoT Devices and Networking Protocols ·IoT Analytics for the Cloud ·Creating an AWS Cloud Analytics Environment ·Collecting All That Data - Strategies and Techniques · · ·

Packt.Analytics.for.the.Internet.of.Things.2017

Internet of Things for Architects.pdf, is a very good book on overall security architecture.

This is the era of Big Data and Internet of Things! Big Data implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects. What you will learn Consolidate, clean, and transform your data acquired from various data sources Perform statistical analysis of data to find hidden insights Explore graphical techniques to see what your data looks like Use machine learning techniques to build predictive models Build scalable data products and solutions Start programming using the RADD API Become an expert by improving your data analytical skills

The world generates data at an increasing pace. Consumers, sensors, or scientific experiments emit data points every day. In finance, business, administration and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill. Python started as a general purpose language. Around ten years ago, in 2006, the first version of NumPy was released, which made Python a first class language for numerical computing and laid the foundation for a prospering development, which led to what we today call the PyData ecosystem: A growing set of high-performance libraries to be used in the sciences, finance, business or anywhere else you want to work efficiently with datasets. Python is not only about data analysis. The list of industrial-strength libraries for many general computing tasks is long, which makes working with data in Python even more compelling. Social media and the Internet of Things have resulted in an avalanche of data. The data is powerful but not in its raw form; it needs to be processed and modeled and Python is one of the most robust tools we have out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This course is your guide to get started with Predictive Analytics using Python as the tool. Data visualization is intended to provide information clearly and help the viewer understand them qualitatively. The well-known expression that a picture is worth a thousand words may be rephrased as “a picture tells a story as well as a large collection of words”. Visualization is, therefore, a very precious tool that helps the viewer understand a concept quickly. We are currently faced with a plethora of data containing many insights that hold the key to success in the modern day. It is important to find the data, clean it, and use the right tool to visualize it. This course explains several different ways to visualize data using Python packages, along with very useful examples in many different areas such as numerical computing, financial models, statistical and machine learning, and genetics and networks.

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