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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。

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

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

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,

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

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

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

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

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.

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

Today’s world is knowledge based. In the earliest days, knowledge was gathered through observation. Later, knowledge not only was gathered through observation, but also confirmed by actually doing and then extended by experimenting further. Knowledge thus gathered was applied to practical fields and extended by analogy to other fields. Today, knowledge is gathered and applied by analyzing, or deep-diving, into the data accumulated through various computer applications, web sites, and more. The advent of computers complemented the knowledge of statistics, mathematics, and programming. The enormous storage and extended computing capabilities of the cloud, especially, have ensured that knowledge can be quickly derived from huge amounts of data and also can be used for further preventive or productive purposes.

This book is about the R programming language. Maybe more important, this book is for you. These days, R is an impressively robust language for solving problems that lend themselves to statistical programming methods. There is a large community of users and developers of this language, and together we are able to accomplish things that were not possible before we virtually met. Of course, to leverage this collective knowledge, we have to start somewhere. Chapters 1 through 5 focus on gaining familiarity with the R language itself. If you have prior experience in programming, these chapters will be very easy for you. If you have no prior programming experience, that is perfectly fine. We build from the ground up, and let us suggest you spend some thoughtful time here. Thinking like a programmer has some very great advantages. It is a skill we would want you to have, and this book is, after all, for you. Chapters 6 through 10 focus on what might be termed elementary statistical methods in R. We did not have the space to introduce those methods in their entirety—we are supposing some knowledge of statistics. An introductory or elementary course for nonmajors would be more than enough. If you are already familiar with programming and statistics, we suggest you travel through these chapters only briefly. With Chapter 11, we break into the last part of the book. For someone with both a fair grasp of traditional statistics and some programming experience, this may well be a good place to start. For our readers who read through from the first pages, this is where it starts to get very exciting. From bootstrapping to logistic regression to data visualization to high-performance computing, these last chapters have hands-on examples that work through some much applied and very interesting examples. One final note: While we wrote this text from Chapter 1 to Chapter 19 in order, the chapters are fairly independent of each other. Don't be shy about skipping to the chapter you're most interested in learning. We show all our code, and you may well be able to modify what we have to work with what you have. Happy reading

R has become one of the most popular programming languages in an era where data science is increasingly prevalent. As R and data science have become more mainstream, there is a growing number of R users without dedicated training in statistical computing or data science, and thus a growing demand for books and resources to bridge the gap between applied users who may have only an introductory background in statistics or programming and advanced and sophisticated data analytics. This book focuses on how to use advanced programming in R to speed up everyday tasks in data analysis and data science. This book is also unique in its coverage of how to set up R in the cloud and generate dynamic reports for analyses that are regularly repeated, such as monthly analysis of company sales or quarterly analysis of student grades, enrollment, and dropout numbers in schools with projections for future enrollment rates. Chapters 1 through 6 focus on more advanced programming techniques than the Apress offering of Beginning R . Chapters 7 – 10 develop powerful data management measures including the exciting and (comparatively) new data.table . From here, we delve into the modern (and slightly edgy) world of cloud computing with R. From the ground up, we walk you through getting R started on an Amazon cloud in chapters 11 – 14 . Finally, Chapter 15 provides you with solid techniques in dynamic documents and reports.

Dynamic SQL - Applications, Performance, and Security Rapid response and exibility in the face of changing business requirements

Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets. This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind. xii | Preface The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: NumPy for manipulation of homogeneous arraybased data, Pandas for manipulation of heterogeneous and labeled data, SciPy for common scientific computing tasks, Matplotlib for publication-quality visualizations, IPython for interactive execution and sharing of code, Scikit-Learn for machine learning, and many more tools that will be mentioned in the following pages.

It has been my experience as a .NET trainer and lead programmer that most people do not have trouble picking up the syntax of the C# language. What perplexes and frustrates many people are the higher-level concepts of object-oriented programming methodology and design. To compound the problem, most introductory programming books and training classes skim over these concepts or, worse, don’t cover them at all. It is my hope that this book fills this void. My goals in writing this book are twofold. My first goal is to provide you with the information you need to understand the fundamentals of programming in C#. More importantly, my second goal is to present you with the information required to master the higher-level concepts of object-oriented programming methodology and design.

Beginning Data Science in R - Data Analysis, Visualization, and Modelling for the Data Scientist Welcome to Introduction to Data Science with R. This book was written as a set of lecture notes for two classes I teach, Data Science: Visualization and Analysis and Data Science: Software Development and Testing. The book is written to fit the structure of these classes, where each class consists of seven weeks of lectures and project work. This means that there are 14 chapters with the core material, where the first seven focus on data analysis and the last seven on developing reusable software for data science

The C# programming language is a modern, object-oriented language created by Microsoft for the .NET Framework. C# (pronounced “see sharp”) builds upon some of the best features of the major programming languages. It combines the power of C++ with the simplicity of Visual Basic and also borrows much from Java. This results in a language that is easy to learn and use, robust against errors and that enables rapid application development. All this is achieved without sacrificing much of the power or speed, when compared to C++

data structures, keywords, strings, variables, and more.

Text Analytics with Python A Practical Real-World Approach to Gaining Actionable Insights from your Data

Python Data Analytics - Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language

Data-drivenness is about building tools, abilities, and, most cru‐ cially, a culture that acts on data. This chapter will outline what sets data-driven organizations apart. I start with some initial prerequi‐ sites about data collection and access. I then contrast reporting and alerting versus analyses in some detail because it is such an impor‐ tant distinction. There are many different types of forward-looking analysis, varying in degrees of sophistication. Thus, I spend some time going over those types, describing them in terms of “levels of analytics” and “analytics maturity,” in particular, discussing the hall‐ marks of an analytically mature organization. What does that look like?

Getting Started with SQL：A Hands-On Approach for Beginners

Beginning Power BI - A Practical Guide to Self-Service Data Analytics with Excel 2016 and Power BI Desktop

Excel is a powerful and versatile spreadsheet program that can be used for both business and personal needs. It has amazing capabilities that you can use to make any type of data you record more streamlined and productive.

Microsoft Project 2016 Step by Step 入门到精通 Welcome! This Step by Step book has been designed so you can read it from the beginning to learn about Microsoft Project 2016 and then build your skills as you learn to perform increasingly specialized procedures. Or, if you prefer, you can jump in wherever you need ready guidance for performing tasks. The how-to steps are delivered crisply and concisely—just the facts. You’ll also find informative, full-color graphics that support the instructional content.

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