Analytics Data Science and Artificial IntelligenceAnalytics Data Science and Artificial Intelligence



With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

Author: Ramesh Sharda

Publisher:

ISBN: 1292341556

Category:

Page: 832

View: 569

For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

The Handbook of Data Science and AIThe Handbook of Data Science and AI



This book will clarify these concepts and provide you with practical knowledge to apply them.

Author: Stefan Papp

Publisher:

ISBN: 1569908869

Category:

Page: 576

View: 502

Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring: - A comprehensive overview of the various fields of application of data science - Case studies from practice to make the described concepts tangible - Practical examples to help you carry out simple data analysis projects The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. Contains these current issues: - Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms. - Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice - Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies - Computer vision: How can we gain insights from images and videos with data science? - Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios. - ML and AI in production: How to turn experimentation into a working data science product? - Presenting your results: Essential presentation techniques for data scientists

Data ScienceData Science



This book will discuss everything that you need to know when it comes to working in the field of data science.

Author: Richard Hurley

Publisher:

ISBN: 1952191238

Category:

Page: 182

View: 673

This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers.

Trends of Data Science and ApplicationsTrends of Data Science and Applications



This book is helpful for the students, practitioners, researchers as well as industry professional. This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021.

Author: Siddharth Swarup Rautaray

Publisher: Springer Nature

ISBN: 9789813368156

Category:

Page: 341

View: 709

This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.

Handbook of Research on Disease Prediction Through Data Analytics and Machine LearningHandbook of Research on Disease Prediction Through Data Analytics and Machine Learning



EXISTING PREDICTION MODELS IN MACHINE LEARNING AND DEEP LEARNING As discussed in precious section, predictive and ... Similarly, data analytics and predictive analytics are different terms and difference is included here as: Data ...

Author: Rani, Geeta

Publisher: IGI Global

ISBN: 9781799827436

Category:

Page: 586

View: 850

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Artificial Intelligence and Data Analytics for Energy Exploration and ProductionArtificial Intelligence and Data Analytics for Energy Exploration and Production



ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in ...

Author: Fred Aminzadeh

Publisher: John Wiley & Sons

ISBN: 9781119879695

Category:

Page: 613

View: 210

ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in exploration and production in the energy industry, covering the most comprehensive and updated new processes, concepts, and practical applications in the field. The book provides an in-depth treatment of the foundations of Artificial Intelligence (AI) Machine Learning, and Data Analytics (DA). It also includes many of AI-DA applications in oil and gas reservoirs exploration, development, and production. The book covers the basic technical details on many tools used in “smart oil fields”. This includes topics such as pattern recognition, neural networks, fuzzy logic, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural language processing, data analytics and next-generation visualization. While theoretical details will be kept to the minimum, these topics are introduced from oil and gas applications viewpoints. In this volume, many case histories from the recent applications of intelligent data to a number of different oil and gas problems are highlighted. The applications cover a wide spectrum of practical problems from exploration to drilling and field development to production optimization, artificial lift, and secondary recovery. Also, the authors demonstrate the effectiveness of intelligent data analysis methods in dealing with many oil and gas problems requiring combining machine and human intelligence as well as dealing with linguistic and imprecise data and rules.

The Era of Artificial Intelligence Machine Learning and Data Science in the Pharmaceutical IndustryThe Era of Artificial Intelligence Machine Learning and Data Science in the Pharmaceutical Industry



Faisal M. Khan AI and Analytics, Data Science and Artificial Intelligence, Biopharma R&D, AstraZeneca, Gaithersburg, MD, United States Sajan Khosla Oncology Data Science, AstraZeneca, Gaithersburg, MD, United States Natalie Kurbatova ...

Author: Stephanie K. Ashenden

Publisher: Academic Press

ISBN: 9780128204498

Category:

Page: 264

View: 641

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide

Predictive AnalyticsPredictive Analytics



Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis ...

Author: Dursun Delen

Publisher: Pearson Business Analytics

ISBN: 0136738516

Category:

Page: 0

View: 823

"Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis Integration with cutting-edge Big Data approaches Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors"--

Data ScienceData Science



Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.

Author: Vijay Kotu

Publisher: Morgan Kaufmann

ISBN: 9780128147627

Category:

Page: 568

View: 725

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Systems for Analytics Data Science Artificial Intelligence Systems for Decision Support Global EditionSystems for Analytics Data Science Artificial Intelligence Systems for Decision Support Global Edition



With six new chapters, the 11th edition marks a major reorganization reflecting a new focus — analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

Author: Ramesh Sharda

Publisher: Pearson Higher Ed

ISBN: 9781292341606

Category:

Page: 833

View: 922

For courses in decision support systems, computerized decision-making tools, and management support systems. Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organizations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganization reflecting a new focus — analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.