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跟金融大数据专家学Python神器—Anaconda，快速成为数据科学高手Key Features本书示例丰富，讲解细致作者不仅在金融领域有着深厚的积累，还有着丰富的教学经验Book DescriptionAnaconda是一个强大的开源数据科学平台，它将很多好的工具整合在一起，极大地简化了使用者的工作流程，并能够帮助使用者解决一系列数据科学难题。 本书旨在通过一系列示例，引导读者在编码和图表中了解Anaconda的强大之处。本书包括12章，结合R、Python、Octave和Julia等4种编程语言，从平台的安装和配置开始，循序渐进地引导读者掌握数据集的获取、数据可视化、统计建模、管理包、Anaconda的优化、无监督学习、监督学习、数据预测分析、云、分布式计算等内容。 本书示例丰富，讲解细致，作者不仅在金融领域有着深厚的积累，还有着丰富的教学经验。对于那些有兴趣了解金融领域数据科学的读者，以及普通的数据分析师或数据科学从业者，本书都是一个不错的选择。在阅读本书之前，我们希望读者具备R或Python的基本编程知识，以及线性代数相关的基本知识。What you will learnAnaconda平台的安装和配置数据集的获取数据可视化统计建模管理包Anaconda的优化无监督学习监督学习数据预测分析云分布式计算Who this book is for数据分析师、数据科学从业者以及金融领域从业者，想通过Python神器Anaconda解决常见数据科学难题的读者。

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, AnacondaKey Features-Use Anaconda to find solutions for clustering, classification, and linear regression-Analyze your data efficiently with the most powerful data science stack-Use the Anaconda cloud to store, share, and discover projects and librariesBook DescriptionAnaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You'll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You'll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you're accustomed to all this, you'll start with operations in data science such as cleaning, sorting, and data classification. You'll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you'll learn how to visualize data using the packages available for Julia, Python, and R.What you will learnPerform cleaning, sorting, classification, clustering, regression, and dataset modeling using AnacondaUse the package manager conda and discover, install, and use functionally efficient and scalable packagesGet comfortable with heterogeneous data exploration using multiple languages within a projectPerform distributed computing and use Anaconda Accelerate to optimize computational powersDiscover and share packages, notebooks, and environments, and use shared project drives on Anaconda CloudTackle advanced data prediction problemsWho this book is forHands-On Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It's also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.

Globally, major network operators have incorporated network convergence into their strategy to grow service revenues and reduce capital and operating costs. Conv- gence occurs in applications (integrated messaging, voice over IP), in network control (portable numbers, SIP, mobile IP), in the transport layer, as well as in the access network. This convergence of networks means that various types of traffic flows, which have been carried by separate specialized networks, now share the resources of a single core IP-based network. In the access, the trends are towards fixed/wireless convergence as well as convergence of various wireless access technologies. Network convergence will be successful only if the quality of the individual s- vices is maintained in the new network environment without undue increased costs. The quality of service delivery is critically dependent on how network performance and availability, as experienced by the traffic flows, are managed. Managing traffic performance is a critical enabler for success. Reaching the desired performance levels requires adapting processes such as network planning, resource engineering, and network monitoring to the converged network milieu.