• PMBOK项目管理知识体系指南第六版 最新中文版

    PMI 将项目管理知识体系 (PMBOK) 定义为描述项目管理专业范围内知识的术语。项目管理知识体系包括已被验证并广泛应用的传统做法,以及本专业新近涌现的创新做法。

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  • 数据驱动的管理 - 清华大学出版社

    本书揭示了企业管理和企业信息化的一个崭新的方向:数据驱动型管理,以及它的精髓、秘密和迷人之处。本书适合企业各个层面和职能的管理者,尤其是高层管理人员,信息化从业人员,高等院校管理、金融、计算机、统计等专业的教师和学生

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  • 大数据分析平台技术及IBM解决方案

    大数据简介 – 什么是大数据 – 大数据新技术 – 大数据价值链 IBM大数据分析平台架构 电信运营商大数据应用场景

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  • 创新趋势报告:目标驱动的数据

    It’s an era where “Big,” “Fast” and “Smart” data practices converge with human experience and insight to enable us to identify, understand, solve and inspire action for the challenges we face as a global society. In this Purpose-Driven Data report, the third in our Innovation Trends Report series,

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  • Lean Analytics - Use Data to Build a Better Startup Faster

    Lean Startup helps you structure your progress and identify the riskiest parts of your business, then learn about them quickly so you can adapt. Lean Analytics is used to measure that progress, helping you to ask the most important questions and get clear answers quickly

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  • Data Science & Big Data Analytics

    Data Science & Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data Much has been written about Big Data and the need for advanced analytics within industry, academia, and government. Availability of new data sources and the rise of more complex analytical opportunities have created a need to rethink existing data architectures to enable analytics that take advantage of Big Data. In addition, significant debate exists about what Big Data is and what kinds of skills are required to make best use of it. This chapter explains several key concepts to clarify what is meant by Big Data, why advanced analytics are needed, how Data Science differs from Business Intelligence (BI), and what new roles are needed for the new Big Data ecosystem

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  • CRC Press - Business Analytics for Decision Making

    owards solving—decision problems faced by individuals and organizations of all sorts. These include commercial and non-profit ventures, LLCs, privately held firms, cooperatives, ESOPs, governmental organizations, NGOs, and even quangos. Business analytics is, above all, about “thinking with models and data” of all kinds (e.g., in the case of data, including text data). It is about using them as inputs to deliberative processes that typically are embedded in a rich context of application, which itself provides additional inputs to the decision maker

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  • R for Marketing Research and Analytics

    R for Marketing Research and Analytics is the perfect book for those interested in driving success for their business and for students looking to get an introduction to R. While many books take a purely academic approach, Chapman (Google) and Feit (formerly of GM and the Modellers) know exactly what is needed for practical marketing problem solving. I am an expert R user, yet had never thought about a textbook that provides the soup-to-nuts way that Chapman and Feit do: show how to load a data set, explore it using visualization techniques, analyze it using statistical models, and then demonstrate the business implications. It is a book that I wish I had written

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    2017-09-17
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  • R Quick Syntax Reference

    R is a programming language that provides the user with powerful data and graphical analysis options. R is both flexible and broad. From tasks as simple as adding two numbers to tasks as complex as fitting an ARIMA model, R is capable of crunching the numbers.

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    2017-09-17
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  • Graphing Data with R

    It’s much easier to grasp complex data relationships with a graph than by sc anning numb er s in a spreadsheet. This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many other fields. You’ll learn methods for highlighting important relationships and trends, reducing data to simpler forms, and emphasizing key numbers at a glance. Anyone who wants to analyze data will find something useful here—even if you don’t have a background in mathematics, statistics, or computer programming. If you want to examine data related to your work, this book is the ideal way to start

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    2017-09-17
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