Big Data Management and Analytics

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【簡介】 With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge. Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape. Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system. Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance. 【目錄】 Contents: Introduction to Big Data Big Data Management and Modeling Big Data Processing Big Data Analytics and Machine Learning Big Data Analytics Through Visualization Taming Big Data with Spark 2.0 Managing Big Data in Cloud Storage Big Data in Healthcare Big Data in Finance Enabling Tools and Technologies for Big Data Analytics References Index

Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud (1版)

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目錄大綱 Table of Contents: 1. Introduction to Computers and Python 2. Introduction to Python Programming 3. Control Statements and Program Development 4. Functions 5. Sequences: Lists and Tuples 6. Dictionaries and Sets 7. Array-Oriented Programming with NumPy 8. Strings: A Deeper Look 9. Files and Exceptions 10. Object-Oriented Programming 11. Computer Science Thinking: Recursion, Searching, Sorting and Big O 12. Natural Language Processing (NLP) 13. Data Mining Twitter 14. IBM Watson and Cognitive Computing 15. Machine Learning: Classification, Regression and Clustering 16. Deep Learning 17. Big Data: Hadoop, Spark, NoSQL and IoT Index 目錄大綱(中文翻譯) 目錄： 1. 電腦和Python簡介 2. Python程式設計簡介 3. 控制語句和程式開發 4. 函式 5. 序列：列表和元組 6. 字典和集合 7. 使用NumPy進行陣列導向程式設計 8. 字串：更深入的觀察 9. 檔案和例外處理 10. 物件導向程式設計 11. 電腦科學思維：遞迴、搜尋、排序和Big O 12. 自然語言處理（NLP） 13. 探勘Twitter資料 14. IBM Watson和認知運算 15. 機器學習：分類、回歸和分群 16. 深度學習 17. 大數據：Hadoop、Spark、NoSQL和物聯網 索引

DEEP LEARNING: FROM BIG DATA TO ARTIFICIAL INTELLIGENCE WITH R

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DESCRIPTION DEEP LEARNING A concise and practical exploration of key topics and applications in data science In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning: From Big Data to Artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find: A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning: From Big Data to Artificial Intelligence with R will also earn a place in the libraries of data science researchers and practicing data scientists.