Data Analytics Practical Guide to Leveraging the Power of Algorithms,Data Science,Data Mining,Statistics,Big Data,and Predictive Analysis to Improve Business,Work,and Life

呼叫分析工具是一个服务器程序，用于监控 Avaya VDN、ACD 寻线组和分机对象，它从监控事件中提取有用信息，并为报告、墙板集成和呼叫日志分析等应用程序输出呼叫和座席记录。 呼叫分析工具是一个 CTI 报告和实时监控引擎，它是呼叫中心环境中未安装 CMS 时的理想工具。

亚马逊上的Analytics（分析）评论 数据分析考试最终项目，。 由 ， ， 。 探索，情感分析，主题分析（LDA）和VueJS Web应用程序，公开受过训练的模型。 （网络演示部署） 勘探 网络演示 跑 设置一个Python虚拟环境并安装所需的软件包 cd scripts python3 -m venv . source bin/activate pip3 install -r requirements.txt python3 -m spacy download en （可选）安装ipynb内核以使用venv软件包 pip3 install --user ipykernel

重要通知 此公共存储库是只读的，不再维护。 SAP Analytics Cloud API演示应用程序 该存储库包含博客文章《 SAP Analytics Cloud API：入门指南》的随附源代码（可在或）。 我们举例说明了第三方应用程序如何通过使用授权协议访问SAC内容。 该演示应用程序是在编程的，并使用了。 项目结构 analytics-cloud-apis-oauth-client-sample docs 上市 CSS img js-客户端程序 rsrc config.js-客户端程序的配置，即租户配置（租户URL和ID），演示参数（演示案例/过滤器/变量） 服务器配置config.properties-服务器程序的OAuth配置（客户端ID，机密，令牌和授权URL，重定向URI） 执照 注意 自述文件 package.json 视图-html文件 server-a

Big Data Science & Analytics: A Hands-On Approach By 作者: Arshdeep Bahga – Vijay Madisetti ISBN-10 书号: 0996025537 ISBN-13 书号: 9780996025539 Edition 版本: 1 出版日期: 2016-04-15 pages 页数: (542 ) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, processing frameworks for batch and real-time analytics, serving databases, web and visualization frameworks. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks as used in the proposed design methodology. We chose Python as the primary programming language for this book. Other languages, besides Python, may also be easily used within the Big Data stack described in this book. We describe tools and frameworks for Data Acquisition including Publish-subscribe messaging frameworks such as Apache Kafka and Amazon Kinesis, Source-Sink connectors such as Apache Flume, Database Connectors such as Apache Sqoop, Messaging Queues such as RabbitMQ, ZeroMQ, RestMQ, Amazon SQS and custom REST-based connectors and WebSocket-based connectors. The reader is introduced to Hadoop Distributed File System (HDFS) and HBase non-relational database. The batch analysis chapter provides an in-depth study of frameworks such as Hadoop-MapReduce, Pig, Oozie, Spark and Solr. The real-time analysis chapter focuses on Apache Storm and Spark Streaming frameworks. In the chapter on interactive querying, we describe with the help of examples, the use of frameworks and services such as Spark SQL, Hive, Amazon Redshift and Google BigQuery. The chapter on serving databases and web frameworks provide an introduction to popular relational and non-relational databases (such as MySQL, Amazon DynamoDB, Cassandra, and MongoDB) and the Django Python web framework. Part III focuses advanced topics on big data including analytics algorithms and data visualization tools. The chapter on analytics algorithms introduces the reader to machine learning algorithms for clustering, classification, regression and recommendation systems, with examples using the Spark MLlib and H2O frameworks. The chapter on data visualization describes examples of creating various types of visualizations using frameworks such as Lightning, pygal and Seaborn.

百夫长统计(Centcount Analytics)是一款功能强大的开源网站统计分析软件。采用PHP + MySQL + Redis开发而成，可以方便地部署在自己的服务器上，100%独享数据。数据精准是该统计系统的最大特点，我们尽最大可能收集用户浏览轨迹，从而为网站管理者提供极为精确的统计数据，发掘潜在价值。

预期寿命 过去已经对影响预期寿命的几个因素进行了研究。 以前从未考虑过使用某些功能根据国家状况（发展/发达，GDP，百分比支出），​​生活方式（BMI，酒精，教育，资源收入构成），疾病（艾滋病毒/艾滋病）预测所有国家/地区预期寿命的准确性艾滋病，白喉等） 数据集已从收集。 我已经在R上完成了这个项目，并且在Tableau上创建了不同类型的有意义的可视化。 清理数据，可视化数据，缩放比例的特征，进行统计分析，创建相关矩阵，检查变量之间如何正/负相关以及它们之间的相关性如何，为每个特征创建简单的（一个变量）回归模型并比较p值使用多变量线性回归来检查冗余预测变量，使用vif来量化共线性度，检查条件，这些清理后的数据集是否适合线性回归模型，生成多元回归模型，同时使用AIC和向后消除预测最准确模型的方法以及未来的预测方法-该项目的一部分

#Network Analytics and Recommendation System for GitHub Hosted Projects and Developers 基于对 GitHub 上托管项目和 GitHub 上活跃开发者的协作网络分析的推荐系统。 项目模块 该项目有两个模块： * Recommendation Engine for Similar Projects * Recommendation Engine for Similar Developers ##推荐引擎概述 数据来自 。 然后将数据加载到 。 SQL 查询用于提取网络图数据，如节点和边。 对于项目协作网络，每个项目都是图中的一个节点，并且根据两个节点之间共同开发人员的贡献来计算任意两个节点之间的边缘及其权重。 类似地，在开发人员协作网络的情况下，节点是单独的开发人员，任何两个节点之间的边是通过考虑两个

Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications By 作者: Ashish Kumar – Joseph Babcock ISBN-10 书号: 1788992369 ISBN-13 书号: 9781788992367 Release 出版日期: 2017-12-27 pages 页数: (660 ) $99.99 Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Social Media and the Internet of Things have resulted in an avalanche of data. 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 out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You’ll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: Learning Predictive Analytics with Python Mastering Predictive Analytics with Python What You Will Learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis

客户分析：机器学习有关客户细分和群体预测的案例研究