这本书比韦斯.麦金尼的《Python for Data Analysis》更适合入门学习，强烈推荐。

matlab导入excel代码可靠性分析 这是MATLAB代码的集合， 系统地将基于csv的事件日志导入标准格式 分析基准指标，以纵向跟踪在役舰队的绩效。 根据时间段和感兴趣的系统选择，导出为用户友好的Excel格式。

Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) ISBN-10 书号: 1138744387 ISBN-13 书号: 9781138744387 Edition 版本: 1 出版日期: 2018-04-04 pages 页数: 418 Chapter 1 Preliminaries and Overview Guozhu Dong and Huan Liu Part I Feature Engineering for Various Data Types Chapter 2 Feature Engineering for Text Data Chase Geigle, Qiaozhu Mei, and ChengXiang Zhai Chapter 3 Feature Extraction and Learning for Visual Data Parag S. Chandakkar, Ragav Venkatesan, and Baoxin Li Chapter 4 Feature-Based Time-Series Analysis Ben D. Fulcher Chapter 5 Feature Engineering for Data Streams Yao Ma, Jiliang Tang, and Charu Aggarwal Chapter 6 Feature Generation and Feature Engineering for Sequences Guozhu Dong, Lei Duan, Jyrki Nummenmaa, and Peng Zhang Chapter 7 Feature Generation for Graphs and NetworksYuan Yao, Hanghang Tong, Feng Xu, and Jian Lu Part lI General Feature Engineering Techniques Chapter 8 Feature Selection and Evaluation Yun Li and Tao Li Chapter 9 Automating Feature Engineering in Supervised Learning Udayan Khurana Chapter 10 Pattern-Based Feature Generation Yunzhe Jia, James Bailey, Ramamohanarao Kotagiri, and Christopher Leckie Chapter 11 Deep Learning for Feature Representation Suhang Wang and Huan Liu Part ll Feature Engineering in Special Applications Chapter 12 Feature Engineering for Social Bot Detection Onur Varol, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini Chapter 13 Feature Generation and Engineering for Software Analytics Xin Xia and David Lo Chapter 14 Feature Engineering for Twitter-Based Applications Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein S. Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan Index

电力需求预测：机器学习模型预测Sunyani和Nationwide的未来电力需求

大数据是当今全世界都在谈论的词。 数据量日复一日地从千字节增加到 Zettabytes，数据可能是连续或频繁的实时流的速度，以及来自不同来源的不同格式（结构化、非结构化和非结构化）的各种数据。半结构化）。 所有这些类型的数据都将被合并、存储、处理和分析以备将来的结果。 大数据分析因其降低成本、更快和更好的决策而广受欢迎。 由于其特定功能，它被用于医疗保健、教育、制造、银行、保险、运输、媒体和娱乐等众多应用中。医疗保健领域的数据正在Swift增长，预计近年来会显着增加。 在当今的数字世界中，必须将数据数字化。 为了通过最小化成本来提高医疗质量，必须有效地处理和分析不同类型的健康数据，如电子健康记录、基因组、行为和公共卫生，以应对新的挑战。 出于这个原因，医疗领域被考虑用于大数据分析。 本文介绍了用于处理医疗保健记录的预测性、规范性、描述性和诊断性分析类型。 要执行所有这些操作，Hadoop 是最佳选择。 Hadoop 是 Hadoop 分布式文件系统 (HDFS) 和 MapReduce 的组合。 Hadoop以其存储容量大、处理速度快、成本低、使用集群在分布式环境中工作的模型简单高效而广为人知。 因此，了解 Hadoop 的技术细节变得至关重要。 这一事实激发了深入探索 Hadoop 及其组件的灵感。 MapReduce 结果有助于预测流行病、治愈疾病、提高生活质量并防止死亡。

This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies. . Read more... Abstract: This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You’ll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you’ve learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You’ll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

预测钻石价格 竞争的目的是根据钻石的特征（克拉，重量，颜色，切工...）预测钻石的价格。 这是为Ironhack Data Analytics训练营的学生创建的学术竞赛。

Big Data Analytics Tools and Technology for Effective Planning 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

本书描述了如何使用Spark架构进行大数据分析，包含了大规模数据处理、机器学习、图分析、高速数据流处理。