Data Analytics for IT Networks

Data Analytics for IT Networks
Author: John Garrett
Publisher: Cisco Press
Total Pages: 745
Release: 2018-10-24
Genre: Computers
ISBN: 0135183448

Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data

Data Science and Network Engineering

Data Science and Network Engineering
Author: Suyel Namasudra
Publisher: Springer Nature
Total Pages: 525
Release: 2023-11-02
Genre: Technology & Engineering
ISBN: 9819967554

This book includes research papers presented at the International Conference on Data Science and Network Engineering (ICDSNE 2023) organized by the Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India, during July 21–22, 2023. It includes research works from researchers, academicians, business executives, and industry professionals for solving real-life problems by using the advancements and applications of data science and network engineering. This book covers many advanced topics, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), computer networks, blockchain, security and privacy, Internet of things (IoT), cloud computing, big data, supply chain management, and many more. Different sections of this book are highly beneficial for the researchers, who are working in the field of data science and network engineering.

Network Models for Data Science

Network Models for Data Science
Author: Alan Julian Izenman
Publisher: Cambridge University Press
Total Pages: 501
Release: 2022-12-31
Genre: Mathematics
ISBN: 1108835767

This is the first book to describe modern methods for analyzing complex networks arising from a wide range of disciplines.

Data-Driven Science and Engineering

Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
Total Pages: 615
Release: 2022-05-05
Genre: Computers
ISBN: 1009098489

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Neural Networks for Applied Sciences and Engineering

Neural Networks for Applied Sciences and Engineering
Author: Sandhya Samarasinghe
Publisher: CRC Press
Total Pages: 596
Release: 2016-04-19
Genre: Computers
ISBN: 1420013068

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in

An Engineering Approach to Computer Networking

An Engineering Approach to Computer Networking
Author: Srinivasan Keshav
Publisher: Addison-Wesley Professional
Total Pages: 702
Release: 1997
Genre: Computers
ISBN:

Taking a unique "engineering" approach that will help readers gain a grasp of not just how but also why networks work the way they do, this book includes the very latest network technology--including the first practical treatment of Asynchronous Transfer Mode (ATM). The CD-ROM contains an invaluable network simulator.

Foundations of Data Science

Foundations of Data Science
Author: Avrim Blum
Publisher: Cambridge University Press
Total Pages: 433
Release: 2020-01-23
Genre: Computers
ISBN: 1108617360

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

A First Course in Network Science

A First Course in Network Science
Author: Filippo Menczer
Publisher: Cambridge University Press
Total Pages: 275
Release: 2020-01-30
Genre: Science
ISBN: 1108579612

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

Artificial Intelligence and Data Science Engineering

Artificial Intelligence and Data Science Engineering
Author: Dr.Ravi Kumar Saidala
Publisher: Leilani Katie Publication
Total Pages: 185
Release: 2024-07-13
Genre: Computers
ISBN: 936348419X

Dr.Ravi Kumar Saidala, Associate Professor, Department of Computer Science and Engineering (Data Science), CMR University, Bangalore, Karnataka, India. Dr.D.Usha Rani, Associate Professor, Department of Computer Science and Applications, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. Ms.Indu.B, Assistant Professor, Department of Computer Science Engineering, Dayananda Sagar Academy of Technology and Management (DSATM), Bangalore, Karnataka, India. Dr.Shanthala.P.T, Assistant Professor, Department of Computer Science Engineering, PES University, Bangalore, Karnataka, India.

Quantum State Transfer and Network Engineering

Quantum State Transfer and Network Engineering
Author: Georgios M. Nikolopoulos
Publisher: Springer Science & Business Media
Total Pages: 258
Release: 2013-10-05
Genre: Computers
ISBN: 3642399371

Faithful communication is a necessary precondition for large-scale quantum information processing and networking, irrespective of the physical platform. Thus, the problems of quantum-state transfer and quantum-network engineering have attracted enormous interest over the last years, and constitute one of the most active areas of research in quantum information processing. The present volume introduces the reader to fundamental concepts and various aspects of this exciting research area, including links to other related areas and problems. The implementation of state-transfer schemes and the engineering of quantum networks are discussed in the framework of various quantum optical and condensed matter systems, emphasizing the interdisciplinary character of the research area. Each chapter is a review of theoretical or experimental achievements on a particular topic, written by leading scientists in the field. The volume aims at both newcomers as well as experienced researchers.