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| bits pilani m tech data science: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio® platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data. |
| bits pilani m tech data science: Deep Learning for Smart Healthcare K. Murugeswari, B. Sundaravadivazhagan, S Poonkuntran, Thendral Puyalnithi, 2024-05-15 Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data. Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process. Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes. |
| bits pilani m tech data science: Handbook of Research on Pattern Engineering System Development for Big Data Analytics Tiwari, Vivek, Thakur, Ramjeevan Singh, Tiwari, Basant, Gupta, Shailendra, 2018-04-20 Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. The Handbook of Research on Pattern Engineering System Development for Big Data Analytics is a critical scholarly resource that examines the incorporation of pattern management in business technologies as well as decision making and prediction process through the use of data management and analysis. Featuring coverage on a broad range of topics such as business intelligence, feature extraction, and data collection, this publication is geared towards professionals, academicians, practitioners, and researchers seeking current research on the development of pattern management systems for business applications. |
| bits pilani m tech data science: Enhancing Future Skills and Entrepreneurship Kuldip Singh Sangwan, Christoph Herrmann, 2020-07-27 This open access book presents the proceedings of the 3rd Indo-German Conference on Sustainability in Engineering held at Birla Institute of Technology and Science, Pilani, India, on September 16–17, 2019. Intended to foster the synergies between research and education, the conference is one of the joint activities of the BITS Pilani and TU Braunschweig conducted under the auspices of Indo-German Center for Sustainable Manufacturing, established in 2009. The book is divided into three sections: engineering, education and entrepreneurship, covering a range of topics, such as renewable energy forecasting, design & simulation, Industry 4.0, and soft & intelligent sensors for energy efficiency. It also includes case studies on lean and green manufacturing, and life cycle analysis of ceramic products, as well as papers on teaching/learning methods based on the use of learning factories to improve students’problem-solving and personal skills. Moreover, the book discusses high-tech ideas to help the large number of unemployed engineering graduates looking for jobs become tech entrepreneurs. Given its broad scope, it will appeal to academics and industry professionals alike. |
| bits pilani m tech data science: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-17 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| bits pilani m tech data science: Fundamentals of Analytics Engineering Dumky De Wilde, Fanny Kassapian, Jovan Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira, 2024-03-29 Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use cases Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing. |
| bits pilani m tech data science: Machine Learning, Optimization, and Data Science Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton, 2022-02-01 This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications. |
| bits pilani m tech data science: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics Patil, Bhushan, Vohra, Manisha, 2020-10-23 Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies. |
| bits pilani m tech data science: Advanced Applications of NLP and Deep Learning in Social Media Data Abd El-Latif, Ahmed A., Wani, Mudasir Ahmad, El-Affendi, Mohammed A., 2023-06-05 Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for researchers and academicians with numerous research opportunities. This ample amount of data needs advanced machine learning, deep learning, and intelligent tools and techniques to receive, process, and interpret the information to resolve real-life challenges and improve the online social lives of people. Advanced Applications of NLP and Deep Learning in Social Media Data bridges the gap between natural language processing (NLP), advanced machine learning, deep learning, and online social media. It hopes to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. Covering topics such as machine learning-based prediction, emotion recognition, and high-dimensional text clustering, this premier reference source is an essential resource for OSN service providers, psychiatrists, psychologists, clinicians, sociologists, students and educators of higher education, librarians, researchers, and academicians. |
| bits pilani m tech data science: Soft Computing in Data Science Michael W. Berry, Azlinah Hj. Mohamed, Bee Wah Yap, 2016-09-17 This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics. |
| bits pilani m tech data science: Hands-on Go Programming Sachchidanand Singh, Prithvipal Singh, 2021-03-05 An easy-to-understand guide that helps you get familiar with the basics and advanced concepts in Golang Ê KEY FEATURESÊÊ _ Everything you need to know on how to use Go programming. _ Illustrated Examples on Go Functions, Control Flows, and Arrays. _ Deep Dive into Slices, Maps, Structs, Error Handling and Concurrency in Golang. DESCRIPTION Hands-on Go Programming is designed to get you up and running as fast as possible with Go. You will not just learn the basics but get introduced to how to use advanced features of Golang. The book begins with the basic concepts of Data types, Constants, Variables, Operators, Reassignment, and Redeclaration. Moving ahead, we explore and learn the use of Functions, Control flows, Arrays, Slices, Maps, and Structs using some great examples and illustrations. We then get to know about Methods in Golang. Furthermore, we learn about complex aspects of Golang such as Interfaces,Pointers, Concurrency and Error Handling. By the end, you will be familiar with both the basics and advanced concepts of Go and start developing critical programs working using this language. Ê WHAT YOU WILL LEARNÊ _ Learn Golang syntaxes, control structures and Error Handling in-depth. _ Learn to declare, create and modify Slices, Maps and Struct in Go. _ Build your own concurrent programs with Goroutines and Channels. _ Deep Dive into Error handling in Golang. WHO THIS BOOK IS FORÊ Anyone who knows basic programming can use this book to upskill themselves in Golang. This book is also for Engineering students, IT/Software professionals, and existing Go programmers. Architects and Developers working in Cloud, Networking, and DevOps can use this book to learn Go programming and apply the knowledge gained to design and build solutions in their respective domains. Ê TABLE OF CONTENTS 1. Chapter 1 Introduction 2. Chapter 2 Functions 3. Chapter 3 Control Flows 4. Chapter 4 Arrays 5. Chapter 5 Slices 6. Chapter 6 Maps 7. Chapter 7 Structs 8. Chapter 8 Methods 9. Chapter 9 Interfaces 10. Chapter 10 PointersÊ 11. Chapter 11 ConcurrencyÊ 12. Chapter 12 Error Handling |
| bits pilani m tech data science: Neural Networks for Natural Language Processing S., Sumathi, M., Janani, 2019-11-29 Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing. |
| bits pilani m tech data science: Accelerating Discoveries in Data Science and Artificial Intelligence I Frank M. Lin, Ashokkumar Patel, Nishtha Kesswani, Bosubabu Sambana, 2024 Zusammenfassung: The Volume 1 book on Accelerating Discoveries in Data Science and Artificial Intelligence (Proceedings of ICDSAI 2023), that was held on April 24-25, 2023 by CSUSB USA, the International Association of Academicians (IAASSE), and the Lendi Institute of Engineering and Technology, Vizianagaram, India is intended to be used as a reference book for researchers and practitioners in the disciplines of AI and data science. The book introduces key topics and algorithms and explains how these contribute to healthcare, manufacturing, law, finance, retail, real estate, accounting, digital marketing, and various other fields. The book is primarily meant for academics, researchers, and engineers who want to employ data science techniques and AI applications to address real-world issues. Besides that, businesses and technology creators will also find it appealing to use in industry |
| bits pilani m tech data science: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| bits pilani m tech data science: Artificial Intelligence and the Fourth Industrial Revolution Utpal Chakraborty, Amit Banerjee, Jayanta Kumar Saha, Niloy Sarkar, Chinmay Chakraborty, 2022-05-26 This book presents the overall technology spectrum in artificial intelligence (AI) and the Fourth Industrial Revolution, which is set to revolutionize the world. It discusses their various aspects and related case studies from industry, academics, administration, law, finance, and accounting as well as educational technology. The contributors, who are experts in their respective fields and from industry and academia, focus on a gesture-recognition prototype for specially abled people; jurisprudential approach to AI and legal reasoning; automated chatbot for autism spectrum disorder using AI assistance; Big Data analytics and Internet of Things (IoT); role of AI in advancement of drug discovery; development, opportunities, and challenges of the Fourth Industrial Revolution; legal, ethical, and policy implications of AI; Internet of Health Things for smart healthcare and digital wellbeing; machine learning and computer vision; computer vision-based system for automation and industrial applications; AI-IoT in home-based healthcare; and AI in super-precision human brain and spine surgery. Buttressed with comprehensive theoretical, methodological, well-established, and validated empirical examples, the book covers the interests of a broad audience from basic science to engineering and technology experts and learners. It will be greatly helpful for CEOs, entrepreneurs, academic leaders, researchers, and students of engineering, biomedicine, and master’s programs in science as well as the vast workforce and students with technical or non-technical backgrounds. It also serves common public interest by presenting new methods to improve the quality of life in general, with a better integration into society. |
| bits pilani m tech data science: Data Management, Analytics and Innovation Neha Sharma, Amol Goje, Amlan Chakrabarti, Alfred M. Bruckstein, 2023-05-28 This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. The volume is a collection of peer reviewed research papers presented at Seventh International Conference on Data Management, Analytics and Innovation (ICDMAI 2023), held during 20 – 22 January, 2023 in Pune, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| bits pilani m tech data science: Data Science and Analytics Usha Batra, Nihar Ranjan Roy, Brajendra Panda, 2020-05-27 This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics. |
| bits pilani m tech data science: Design Solutions for Improving Website Quality and Effectiveness Sreedhar, G., 2016-01-07 As the Internet has evolved to become an integral part of modern society, the need for better quality assurance practices in web engineering has heightened. Adherence to and improvement of current standards ensures that overall web usability and accessibility are at optimum efficiency. Design Solutions for Improving Website Quality and Effectiveness is an authoritative reference source for the latest breakthroughs, techniques, and research-based solutions for the overall improvement of the web designing process. Featuring relevant coverage on the analytics, metrics, usage, and security aspects of web environments, this publication is ideally designed for reference use by engineers, researchers, graduate students, and web designers interested in the enhancement of various types of websites. |
| bits pilani m tech data science: Supply Chain Engineering A. Ravi Ravindran, Donald P. Warsing, Jr., Paul M. Griffin, 2023-08-30 This new edition textbook continues down the path that the first edition, winner of the 2013 IISE/Joint Publishers Book-of-the-Year Award, successfully carved out. The textbook targets engineering students and emphasizes the use of operations research models and solution methods important in the design, control, operation, and management of global supply chains. Completely updated, Supply Chain Engineering: Models and Applications, Second Edition stresses quantitative models and methods, highlights global supplier selection and vendor risk management techniques, and discusses the use of multiple criteria decision-making models in supply chain management. The new edition includes chapters on health and humanitarian supply chains, including disaster management and logistics modeling, and on warehousing and distribution. Disruptions to global supply chains due to the COVID-19 pandemic are discussed throughout the book. Industry and government strategies to make the global supply chains resilient are also presented. Thirty four case studies have been included to illustrate various supply chain models and methods. Exercises are included at the end of each chapter, and a solutions manual and PowerPoint slides are available for qualified textbook adoptions. The new edition continues to target upper-level undergraduate and graduate students in engineering, as well as MBA students in operations management, logistics, and supply chain management programs that emphasize quantitative analysis. It is also useful as a reference for technical professionals and researchers in industrial engineering, supply chain management, procurement, logistics and health administration. |
| bits pilani m tech data science: AIoT and Big Data Analytics for Smart Healthcare Applications Shreyas Suresh Rao, Steven Lawrence Fernandes, Chandra Singh, Rathishchandra R. Gatti, Harisha A., Rohanchandra R. Gatty, 2023-12-26 AIoT (Artificial Intelligence of Things) and Big Data Analytics are catalyzing a healthcare revolution. This book is an accessible volume that summarizes the information available. In this book, researchers explore how AIoT and Big Data can seamlessly integrate into healthcare, enhancing medical services and devices while adhering to established protocols. The book demonstrates the crucial role of these technologies during healthcare crises like the COVID-19 pandemic. It presents novel solutions and computational techniques powered by AIoT, Machine Learning, and Deep Learning, providing a new frontier in healthcare problem-solving. Key Features: Real-Life Illustrations: Real-world examples showcase AIoT and Big Data in action, highlighting their impact in healthcare. Comprehensive Exploration: The book offers a thorough examination of AIoT, Big Data, and their harmonious synergy within the healthcare landscape. Visual Aids: Complex concepts become approachable through diagrams, flowcharts, and infographics, making technical processes and system designs more digestible. Ethical Insights: Delving into the ethical dimensions of AIoT and Big Data, it addresses concerns like data bias, patient consent, and transparency in healthcare. Forward-Looking Discourse: The book engages with emerging trends, potential innovations, and the future direction of AIoT and Big Data, making it a compass for healthcare transformation. Researchers, whether from academia, industry, or research and development organizations, interested in AIoT, Big Data, artificial intelligence, and healthcare optimization, will find this book informative. It also serves as an update for tech enthusiasts who want to explore the future of healthcare powered by AI and data. |
| bits pilani m tech data science: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| bits pilani m tech data science: Applications of Artificial Intelligence in Engineering Xiao-Zhi Gao, Rajesh Kumar, Sumit Srivastava, Bhanu Pratap Soni, 2021-05-10 This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence. |
| bits pilani m tech data science: Handling Priority Inversion in Time-Constrained Distributed Databases Shanker, Udai, Pandey, Sarvesh, 2020-02-14 In the computer science industry, high levels of performance remain the focal point in software engineering. This quest has made current systems exceedingly complex, as practitioners strive to discover novel approaches to increase the capabilities of modern computer structures. A prevalent area of research in recent years is scalable transaction processing and its usage in large databases and cloud computing. Despite its popularity, there remains a need for significant research in the understanding of scalability and its performance within distributed databases. Handling Priority Inversion in Time-Constrained Distributed Databases provides emerging research exploring the theoretical and practical aspects of database transaction processing frameworks and improving their performance using modern technologies and algorithms. Featuring coverage on a broad range of topics such as consistency mechanisms, real-time systems, and replica management, this book is ideally designed for IT professionals, computing specialists, developers, researchers, data engineers, executives, academics, and students seeking research on current trends and developments in distributed computing and databases. |
| bits pilani m tech data science: Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) Rajiv Misra, Rudrapatna K. Shyamasundar, Amrita Chaturvedi, Rana Omer, 2021-09-29 This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions. |
| bits pilani m tech data science: Confluence of AI, Machine, and Deep Learning in Cyber Forensics Misra, Sanjay, Arumugam, Chamundeswari, Jaganathan, Suresh, S., Saraswathi, 2020-12-18 Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication. |
| bits pilani m tech data science: Comet for Data Science Angelica Lo Duca, Gideon Mendels, 2022-08-26 Gain the key knowledge and skills required to manage data science projects using Comet Key Features • Discover techniques to build, monitor, and optimize your data science projects • Move from prototyping to production using Comet and DevOps tools • Get to grips with the Comet experimentation platform Book Description This book provides concepts and practical use cases which can be used to quickly build, monitor, and optimize data science projects. Using Comet, you will learn how to manage almost every step of the data science process from data collection through to creating, deploying, and monitoring a machine learning model. The book starts by explaining the features of Comet, along with exploratory data analysis and model evaluation in Comet. You'll see how Comet gives you the freedom to choose from a selection of programming languages, depending on which is best suited to your needs. Next, you will focus on workspaces, projects, experiments, and models. You will also learn how to build a narrative from your data, using the features provided by Comet. Later, you will review the basic concepts behind DevOps and how to extend the GitLab DevOps platform with Comet, further enhancing your ability to deploy your data science projects. Finally, you will cover various use cases of Comet in machine learning, NLP, deep learning, and time series analysis, gaining hands-on experience with some of the most interesting and valuable data science techniques available. By the end of this book, you will be able to confidently build data science pipelines according to bespoke specifications and manage them through Comet. What you will learn • Prepare for your project with the right data • Understand the purposes of different machine learning algorithms • Get up and running with Comet to manage and monitor your pipelines • Understand how Comet works and how to get the most out of it • See how you can use Comet for machine learning • Discover how to integrate Comet with GitLab • Work with Comet for NLP, deep learning, and time series analysis Who this book is for This book is for anyone who has programming experience, and wants to learn how to manage and optimize a complete data science lifecycle using Comet and other DevOps platforms. Although an understanding of basic data science concepts and programming concepts is needed, no prior knowledge of Comet and DevOps is required. |
| bits pilani m tech data science: Handbook of Research on AI-Equipped IoT Applications in High-Tech Agriculture Khang, Alex, 2023-08-02 The agriculture industry is facing significant challenges in meeting the increasing demand for food while also ensuring sustainable development. Traditional agricultural methods are not equipped to meet the demands of the modern world. To overcome these challenges, Advanced Technologies and AI-Equipped IoT Applications in High-Tech Agriculture provides an in-depth analysis of the opportunities and challenges for AI-powered management tools and IoT-equipped techniques for the high-tech agricultural ecosystem. The Handbook of Research on AI-Equipped IoT Applications in High-Tech Agriculture explores advanced methodologies, models, techniques, technologies, and applications along with the concepts of real-time supporting systems to help agricultural producers adjust plans or schedules for taking care of their farms. Additionally, it discusses the role of IoT technologies and AI applications in agricultural ecosystems and their potential to improve product quality and market competitiveness. The book includes discussions on the application of blockchain, biotechnology, drones, robotics, data analytics, and visualization in high-tech agriculture. It is an essential reference for anyone interested in the future of high-tech agriculture, including agricultural analysts, investment analysts, scholars, researchers, academics, professionals, engineers, and students. |
| bits pilani m tech data science: Advances in Data-driven Computing and Intelligent Systems Swagatam Das, Snehanshu Saha, Carlos A. Coello Coello, Jagdish Chand Bansal, 2023-06-21 The volume is a collection of best selected research papers presented at International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022) held at BITS Pilani, K K Birla Goa Campus, Goa, India during 23 – 25 September 2022. It includes state-of-the art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book will be useful for academicians, research scholars, and industry persons. |
| bits pilani m tech data science: Handbook of Research on Big Data Storage and Visualization Techniques Segall, Richard S., Cook, Jeffrey S., 2018-01-05 The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject. |
| bits pilani m tech data science: Practical Biochemistry Pamela Jha, 2024-08-12 Practical Biochemistry provides both foundational knowledge and advanced insights into biochemistry, including the basic compounds, and laboratory methods. The book is designed for students and academic professionals seeking a comprehensive understanding of the practical aspects of the subject. The book is systematically divided into five sections, each dedicated to a specific category of macromolecules and related biochemical techniques: 1) Carbohydrates, 2) Proteins, 3) Nucleic acids, 4) Lipids, 5) Supplementary Techniques and Safety Data Sheet (SDS). Each chapter within these sections is structured to provide a thorough understanding of the aim, principles, procedures, and practical applications of biochemical techniques. Key features: · Comprehensive Information: meticulously organized and structured chapters provide a thorough and methodical approach to learning · Additional Learning Tools: ‘Did You Know’ segments and ‘Viva Voice’ questions enrich the learning experience by offering interesting facts and stimulating critical thinking · Practical Focus: Step-by-step guides aid readers in understanding and applying the techniques in the lab · Safety and Accuracy: teaches how to conduct safe and accurate experiments with precautions · Accessible Language: simple and lucid language helps beginners to understand complex biochemical concepts |
| bits pilani m tech data science: Computational Intelligence in Data Science Mieczyslaw Lech Owoc, |
| bits pilani m tech data science: Big Data Analytics with Hadoop 3 Sridhar Alla, 2018-05-31 Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3 Key Features Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink Exploit big data using Hadoop 3 with real-world examples Book Description Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly. What you will learn Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples Integrate Hadoop with R and Python for more efficient big data processing Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics Set up a Hadoop cluster on AWS cloud Perform big data analytics on AWS using Elastic Map Reduce Who this book is for Big Data Analytics with Hadoop 3 is for you if you are looking to build high-performance analytics solutions for your enterprise or business using Hadoop 3’s powerful features, or you’re new to big data analytics. A basic understanding of the Java programming language is required. |
| bits pilani m tech data science: Defect Prediction in Software Development & Maintainence Rudra Kumar, KSN Prasad, Annaluri Sreenivasa Rao, 2018-04-11 This book is a collection of taxonomy and review of contemporary model in the field of software development and maintenance. This book is basically the result of our passion toward the research of application of software engineering concepts. This work is derived from the need for accurate fault estimation in goals of quality programming and minimal maintenance overheads. State of art technologies have been discussed with respective experimental investigations and analysis. This work started out as a survey and then evolved according to our interest and proclivity into a work that emphasizes the aspects of software development. This book is intended to explain how the defect predictions are used to improve the quality of software development for easy analysis in a very simple way. It contains research that is useful to research scholars, engineers, and computing researchers. |
| bits pilani m tech data science: Handbook of Research on Foundations and Applications of Intelligent Business Analytics Sun, Zhaohao, Wu, Zhiyou, 2022-03-11 Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students. |
| bits pilani m tech data science: Advanced Classification Techniques for Healthcare Analysis Chakraborty, Chinmay, 2019-02-22 Medical and information communication technology professionals are working to develop robust classification techniques, especially in healthcare data/image analysis, to ensure quick diagnoses and treatments to patients. Without fast and immediate access to healthcare databases and information, medical professionals’ success rates and treatment options become limited and fall to disastrous levels. Advanced Classification Techniques for Healthcare Analysis provides emerging insight into classification techniques in delivering quality, accurate, and affordable healthcare, while also discussing the impact health data has on medical treatments. Featuring coverage on a broad range of topics such as early diagnosis, brain-computer interface, metaheuristic algorithms, clustering techniques, learning schemes, and mobile telemedicine, this book is ideal for medical professionals, healthcare administrators, engineers, researchers, academicians, and technology developers seeking current research on furthering information and communication technology that improves patient care. |
| bits pilani m tech data science: Perspective and Strategies on Newage Education and Creative Learning Shibani Khanra Jha, |
| bits pilani m tech data science: Advances in Computational Modeling and Simulation Rallapalli Srinivas, Rajesh Kumar, Mainak Dutta, 2022-02-15 The book presents select proceedings of Global meet on ‘Computational Modelling and Simulation, Recent Innovations, Challenges and Perspectives, 2020. This book covers leading-edge technologies from different domains such as computation in optimization and control, multiscale and multiphysics modeling and computation analysis, environmental modeling, modeling approaches to enterprise systems and services, finite element analysis, dependability and security, high-performance computation/cloud computing applications, computational biology and chemistry and computational mechanics. The primary goal of this book is to strengthen pre-eminence in computational modeling and simulation by catalyzing the transformative use of innovative developments in a wide range of disciplines to achieve lasting societal impact. The book discusses on how to perform simulation of large complex dynamic systems in an efficient manner using advanced computational analysis. The inter-disciplinary nature of the book would be a valuable reference for academicians and research scientists, industrialists interested in modelling and simulation driven by computational technology. |
| bits pilani m tech data science: Women and Educational Development Mukta Gupta, 2000 |
| bits pilani m tech data science: Data Science for Marketing Analytics Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali, 2021-09-07 Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily. |
| bits pilani m tech data science: ICT Systems and Sustainability Milan Tuba, Shyam Akashe, Amit Joshi, 2020-12-14 This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 5th International Conference on ICT for Sustainable Development (ICT4SD 2020), held in Goa, India, on 23–24 July 2020. The conference provided a valuable forum for cutting-edge research discussions among pioneering researchers, scientists, industrial engineers, and students from all around the world. Bringing together experts from different countries, the book explores a range of central issues from an international perspective. |
Birla Institute of Technology And Science, Pilani (BITS Pilani)
BITS Pilani is an all-India institute for higher education with the primary aim of training young men and women to be able and eager to create and put into action ideas, methods, techniques, and …
Bit - Wikipedia
In information theory, one bit is the information entropy of a random binary variable that is 0 or 1 with equal probability, [3] or the information that is gained when the value of such a variable …
Bits and Bytes
At the smallest scale in the computer, information is stored as bits and bytes. In this section, we'll learn how bits and bytes encode information. Everything in a computer is 0's and 1's. The bit …
What is a bit? Bits and bytes explained - IONOS
Dec 8, 2022 · Knowing about bits is essential for understanding how much storage your hard drive has or how fast your DSL connection is. So what is a bit and how is it different from a byte? A …
What is bit (binary digit) in computing? - TechTarget
Jun 6, 2025 · What is bit (binary digit) in computing? A bit (binary digit) is the smallest unit of data that a computer can process and store. It can have only one of two values: 0 or 1. Bits are …
What are Bits and Bytes?
Bits and bytes are the smallest units of data in a computer. A bit is a single binary digit, with a value of either 0 or 1. A byte is a group of 8 bits.
How Bits and Bytes Work | HowStuffWorks
Bytes and bits are the starting point of the computer world. Find out about the Base-2 system, 8-bit bytes, the ASCII character set, byte prefixes and binary math.
What is Bit? - GeeksforGeeks
Nov 12, 2023 · Bits stand for Binary Digit. Where binary means two things or two elements. Digit means a symbol which represents a number. So, bit consists two symbols in form of numbers …
What Are Bits? Binary Data Explained (2025) - BroadbandSearch
Learn what bits are and their importance in digital systems. Discover the different types of binary bits, and data bits, and how binary data processing works.
Bit Definition - What is a bit in data storage? - TechTerms.com
Apr 20, 2013 · Bit Definition - What is a bit in data storage? A bit (short for "binary digit") is the smallest unit of measurement used to quantify computer data. It contains a single binary value …
Birla Institute of Technology And Science, Pilani (BITS Pilani)
BITS Pilani is an all-India institute for higher education with the primary aim of training young men and women to be able and eager to create and put into action ideas, methods, techniques, …
Bit - Wikipedia
In information theory, one bit is the information entropy of a random binary variable that is 0 or 1 with equal probability, [3] or the information that is gained when the value of such a variable …
Bits and Bytes
At the smallest scale in the computer, information is stored as bits and bytes. In this section, we'll learn how bits and bytes encode information. Everything in a computer is 0's and 1's. The bit …
What is a bit? Bits and bytes explained - IONOS
Dec 8, 2022 · Knowing about bits is essential for understanding how much storage your hard drive has or how fast your DSL connection is. So what is a bit and how is it different from a byte? A …
What is bit (binary digit) in computing? - TechTarget
Jun 6, 2025 · What is bit (binary digit) in computing? A bit (binary digit) is the smallest unit of data that a computer can process and store. It can have only one of two values: 0 or 1. Bits are …
What are Bits and Bytes?
Bits and bytes are the smallest units of data in a computer. A bit is a single binary digit, with a value of either 0 or 1. A byte is a group of 8 bits.
How Bits and Bytes Work | HowStuffWorks
Bytes and bits are the starting point of the computer world. Find out about the Base-2 system, 8-bit bytes, the ASCII character set, byte prefixes and binary math.
What is Bit? - GeeksforGeeks
Nov 12, 2023 · Bits stand for Binary Digit. Where binary means two things or two elements. Digit means a symbol which represents a number. So, bit consists two symbols in form of numbers …
What Are Bits? Binary Data Explained (2025) - BroadbandSearch
Learn what bits are and their importance in digital systems. Discover the different types of binary bits, and data bits, and how binary data processing works.
Bit Definition - What is a bit in data storage? - TechTerms.com
Apr 20, 2013 · Bit Definition - What is a bit in data storage? A bit (short for "binary digit") is the smallest unit of measurement used to quantify computer data. It contains a single binary value …
