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Understanding Complex Datasets
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Book Synopsis Understanding Complex Datasets by : David Skillicorn
Download or read book Understanding Complex Datasets written by David Skillicorn and published by CRC Press. This book was released on 2007-05-17 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book
Book Synopsis Mining of Massive Datasets by : Jure Leskovec
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Book Synopsis Algorithms and Data Structures for Massive Datasets by : Dzejla Medjedovic
Download or read book Algorithms and Data Structures for Massive Datasets written by Dzejla Medjedovic and published by Simon and Schuster. This book was released on 2022-08-16 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting
Book Synopsis R for Data Science by : Hadley Wickham
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Book Synopsis Handbook of Statistical Analysis and Data Mining Applications by : Ken Yale
Download or read book Handbook of Statistical Analysis and Data Mining Applications written by Ken Yale and published by Elsevier. This book was released on 2017-11-09 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
Book Synopsis Automated Data Analytics by : Soraya Sedkaoui
Download or read book Automated Data Analytics written by Soraya Sedkaoui and published by John Wiley & Sons. This book was released on 2024-11-13 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models. Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons. This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress.
Book Synopsis Information Systems Security and Privacy by : Paolo Mori
Download or read book Information Systems Security and Privacy written by Paolo Mori and published by Springer. This book was released on 2019-07-04 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 4th International Conference on Information Systems Security and Privacy, ICISSP 2018, held in Funchal - Madeira, Portugal, in January 2018. The 15 full papers presented were carefully reviewed and selected from a total of 71 submissions. They are dealing with topics such as data and software security; privacy and confidentiality; mobile systems security; biometric authentication; information systems security and privacy; authentication, privacy and security models; data mining and knowledge discovery; phishing; security architecture and design analysis; security testing; vulnerability analysis and countermeasures; web applications and services.
Book Synopsis Automating Data Quality Monitoring by : Jeremy Stanley
Download or read book Automating Data Quality Monitoring written by Jeremy Stanley and published by "O'Reilly Media, Inc.". This book was released on 2024-01-09 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term
Book Synopsis Machine Learning and Data Mining in Pattern Recognition by : Petra Perner
Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer. This book was released on 2015-06-30 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015. The 41 full papers presented were carefully reviewed and selected from 123 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
Book Synopsis Writing for Impact: How to Craft Powerful Scientific Articles by : Balasubramanian Thiagarajan
Download or read book Writing for Impact: How to Craft Powerful Scientific Articles written by Balasubramanian Thiagarajan and published by Otolaryngology online. This book was released on 2023-02-02 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current day researchers are at cross roads. The entire publishing culture and scenario has undergone a sea change. Author is now confronted with newer tools that aid their article generation and publication. The arrival of digital era has made a quantum difference to the scientific publication scenario. The sheer number of publications and the publishers have multiplied rather drastically. The present day author hence should have the following skill set in order to publish good scientific papers. 1. Adept at handling word processors. 2. Comfortable with browser usage and search engine usage. 3. Should be familiar with LATEX language and allied tools. 4. Digital publishing brings with it a certain amount of automation and hence the user should be comfortable with the auto publishing process. This book is intended for those aspiring scientific writers, which ever format of writing they choose to showcase their wares. Separate chapters have been included to hand hold the budding author throughout their writing journey. Some of the chapters that would be of immense use this book has included: 1. Thesis writing 2. Dissertation writing 3. Scholarly writing 4. Preparing project reports for grants Chapters have been organised in such a way that each one of them can be read in isolation. Hope this book would be of help to budding authors and researchers
Book Synopsis Foundations of Data Science by : Dr. M. Muthamizh Selvam
Download or read book Foundations of Data Science written by Dr. M. Muthamizh Selvam and published by RK Publication. This book was released on 2024-09-05 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Data Science is a comprehensive guide that introduces key concepts and techniques essential for understanding and analyzing data in the modern world. Foundational topics like statistics, probability, linear algebra, and machine learning, offering practical insights and applications in real-world data science. With a focus on both theory and implementation, it is designed for students and professionals seeking to build a solid grounding in data science principles and develop skills in data-driven problem-solving, analysis, and predictive modeling across diverse domains.
Book Synopsis Quantum Trading: Harnessing the Power of Data and Strategy by : Anurag Basu Anu
Download or read book Quantum Trading: Harnessing the Power of Data and Strategy written by Anurag Basu Anu and published by Ocleno. This book was released on 2024-06-19 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the future of trading with "Quantum Trading: Innovations in Financial Markets." Delve into the cutting-edge intersection of quantum computing and finance, where advanced algorithms and quantum-inspired strategies redefine how traders navigate today’s dynamic markets. From quantum machine learning to algorithmic risk management, explore the transformative potential of quantum technologies in optimizing portfolios, predicting market trends, and managing risks with unprecedented precision. Discover the ethical implications, regulatory challenges, and practical applications shaping the future of quantum trading. Whether you're a seasoned trader, financial analyst, or curious innovator, this book offers a comprehensive guide to harnessing quantum computing for strategic advantage in global financial ecosystems.
Book Synopsis Machine Learning Essentials and Applications by : Mrs. N. Jayasri
Download or read book Machine Learning Essentials and Applications written by Mrs. N. Jayasri and published by RK Publication. This book was released on 2024-07-27 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Essentials and Applications a comprehensive of machine learning's core principles, methodologies, and real-world applications. This book is designed for both beginners and professionals, covering essential topics like supervised and unsupervised learning, neural networks, and deep learning. With clear explanations and practical examples, it connects theory to practice, showcasing machine learning’s impact across industries such as healthcare, finance, and technology. Ideal for readers seeking foundational knowledge and insights into the transformative potential of machine learning in various fields.
Download or read book Quantum Computing written by N.B. Singh and published by N.B. Singh. This book was released on with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Quantum Computing" is a comprehensive and accessible exploration of one of the most exciting and rapidly evolving fields in modern science. Written with both beginners and advanced enthusiasts in mind, this book offers a captivating journey through the world of quantum computing without the need for complex mathematical formulas. With 50 engaging chapters covering a wide range of topics, readers will discover the fascinating principles behind quantum mechanics and how they are harnessed to revolutionize computing, cryptography, telecommunications, and even our understanding of consciousness and the universe itself. From the basics of qubits and superposition to advanced applications like quantum cryptography and artificial intelligence, this book presents complex concepts in a clear and easy-to-understand manner, making it suitable for readers of all backgrounds. Whether you're a curious novice or a seasoned quantum enthusiast, "Quantum Computing" offers a captivating glimpse into the extraordinary possibilities of the quantum world.
Book Synopsis The Beginner’s Guide to Data Science by : Jason Brownlee
Download or read book The Beginner’s Guide to Data Science written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2024-03-27 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s data-driven world, businesses and industries constantly seek insights to drive innovation, enhance decision-making, and stay ahead of the curve. Data science is not just a skill but a superpower that empowers you to extract meaningful patterns and knowledge from raw data, unlocking limitless opportunities. The theme of data science is to tell a story from data. There are many tools to help you build a narrative, but you should be focused on something other than the tool since the end is more important than the means. If you are a beginner, how should you embark on data science? You can learn many models, read many examples, and eventually gain the right mindset to handle a data science project. You can also learn the data science mindset first and then learn models that fit the picture when needed. The Beginner’s Guide to Data Science is your gateway to learn the data science mindset from examples. This ebook is written in the engaging and approachable style you are familiar with from Machine Learning Mastery. Discover exactly how to start and what the thought process is in dealing with a data science project.
Book Synopsis Intelligent Computing: An Introduction to Artificial Intelligence Book by : Dr. Shivamurthaiah M
Download or read book Intelligent Computing: An Introduction to Artificial Intelligence Book written by Dr. Shivamurthaiah M and published by Shineeks Publishers. This book was released on 2023-10-20 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book named ‘Intelligent Computing: An Introduction to Artificial Intelligence.’ the authors try to give detailed information on various aspects of Intelligent computing. This book consists of seven chapters from Introduction to AI to the Future of AI. The first chapter consists of the Introduction, history importance, and impact of intelligent computing in various fields. The Second chapter gives information about the Foundations of Artificial Intelligence which is cognitive science and its relation to AI. It also explains the Key concepts of Machine learning, Neural networks, Natural language processing and followed by concepts of Robotics. The third chapter explains Intelligent Computing Techniques named Supervised learning: Linear regression, Logistic regression, Support vector machines, Unsupervised learning: Clustering algorithms, Dimensionality reduction, Association rule mining, Deep learning: Neural network architectures, Convolutional neural networks, Recurrent neural networks: Generative adversarial networks, Reinforcement learning, Markov decision processes, Q-learning, Deep reinforcement learning. The fourth chapter consists of information about Applications of Intelligent Computing. Natural language processing applications: Sentiment analysis, Speech recognition, Machine translation, Computer vision applications like Object detection and recognition, Image classification, Facial recognition, Robotics applications Like Autonomous Vehicles, Industrial Automation, human robots, Healthcare applications, Disease diagnosis, Medical Image Analysis & Drug discovery. The fifth chapter consists of topics on the Ethical and Social prospective of the Implications of Intelligent Computing covers the Limitations & strengths of AI algorithms, Privacy and security concerns, Automation and its impact on job displacement also about governance and regulations on AI by the government. The sixth Chapter contains Future Directions and Challenges in Intelligent Computing Advances like interpretability of AI systems, Human-AI collaboration and augmentation, and Addressing ethical and societal challenges. The last chapter gives a conclusion about the topic: key points of AI, its Potential impact in the future & required Encouragement for further exploration of AI and intelligent computing. This book gives detailed enough information for the reader to enhance their knowledge of Intelligent Computing and AI.
Book Synopsis Data Science with Python by : Robert Johnson
Download or read book Data Science with Python written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-26 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Data Science with Python: Unlocking the Power of Pandas and Numpy" is an essential guide for beginners and professionals alike, striving to master the art of data analysis using Python's robust ecosystem. This book delves into the foundational aspects of data science, providing readers with a comprehensive understanding of how to harness Python's capabilities for data manipulation and exploration. By covering key libraries such as Pandas and Numpy, it equips readers with the skills necessary to perform high-performance numerical computations and sophisticated data analysis tasks. Structured to ensure a seamless learning experience, this book introduces essential Python programming concepts and progressively advances to more complex topics in data cleaning, preprocessing, and visualization. Each chapter is crafted to build upon the last, ensuring a coherent progression and a deepening of knowledge. With a series of practical projects, readers will gain hands-on experience in real-world data science applications, learning how to develop predictive models and deploy solutions effectively. Through this approach, the book bridges the gap between theoretical understanding and practical application, empowering readers to unlock the full potential of data science in today's data-driven landscape.
