Data Science Solutions
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Data Science Solutions On Azure
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Author : Julian Soh
language : en
Publisher: Springer Nature
Release Date : 2024-11-18
Data Science Solutions On Azure written by Julian Soh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Computers categories.
This revamped and updated book focuses on the latest in AI technology—Generative AI. It builds on the first edition by moving away from traditional data science into the area of applied AI using the latest breakthroughs in Generative AI. Based on real-world projects, this edition takes a deep look into new concepts and approaches such as Prompt Engineering, testing and grounding of Large Language Models, fine tuning, and implementing new solution architectures such as Retrieval Augmented Generation (RAG). You will learn about new embedded AI technologies in Search, such as Semantic and Vector Search. Written with a view on how to implement Generative AI in software, this book contains examples and sample code. In addition to traditional Data Science experimentation in Azure Machine Learning (AML) that was covered in the first edition, the authors cover new tools such as Azure AI Studio, specifically for testing and experimentation with Generative AI models. What's New in this Book Provides new concepts, tools, and technologies such as Large and Small Language Models, Semantic Kernel, and Automatic Function Calling Takes a deeper dive into using Azure AI Studio for RAG and Prompt Engineering design Includes new and updated case studies for Azure OpenAI Teaches about Copilots, plugins, and agents What You'll Learn Get up to date on the important technical aspects of Large Language Models, based on Azure OpenAI as the reference platform Know about the different types of models: GPT3.5 Turbo, GPT4, GPT4o, Codex, DALL-E, and Small Language Models such as Phi-3 Develop new skills such as Prompt Engineering and fine tuning of Large/Small Language Models Understand and implement new architectures such as RAG and Automatic Function Calling Understand approaches for implementing Generative AI using LangChain and Semantic Kernel See how real-world projects help you identify great candidates for Applied AI projects, including Large/Small Language Models Who This Book Is For Software engineers and architects looking to deploy end-to-end Generative AI solutions on Azure with the latest tools and techniques.
Designing And Building Data Science Solutions
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Author : Jonathan Leslie
language : en
Publisher:
Release Date : 2020-09-10
Designing And Building Data Science Solutions written by Jonathan Leslie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with categories.
Data science, machine learning and artificial intelligence (AI) can have game-changing impacts for businesses, empowering them to increase operational efficiency, improve the quality of their services and understand their customers better. Yet for these benefits to be realised, data science initiatives must be designed and executed in a sensible way. Often these projects, while successful from a scientific standpoint, miss the mark in terms of business impact. Many business leaders are left feeling unsettled, balancing the need for innovation and the adoption of revolutionary technologies with an uncomfortable degree of uncertainty and risk of failure. For the data scientist the situation can be equally unnerving, with uncertainties about how to deliver a successful project when the path is not clear. Yet, these uncertainties and risks -- for the business leader and the data scientist alike -- can be controlled and managed if approached in a sensible manner. Your authors have designed and delivered hundreds of projects across a wide range of industries. We have made many mistakes, and in the process we have learned what works well and where the common pitfalls lie. We wrote this book to share our experiences in hopes that it will help the reader -- whether a data science practitioner or a business leader -- reduce these risks and design projects that have the greatest chance of success. Much of the content in this guide is derived from lessons we have given to our students. Here we have gathered, organised and expanded on those bits of advice to serve as a resource for anyone considering embarking on a data science journey. We share our approach to data science projects, addressing topics such as alignment to business imperatives, project design, project delivery and evaluation of success. Data science can be an exciting, invigorating field, and for the business leader, it can bring about revolutionary changes to an organisation that can come with huge returns on investment and value added. For the data scientist, designing and delivering successful projects is rewarding, stimulating and tremendously gratifying. We hope this guide gives you the confidence to understand the risks and approach your project in a sensible way.
Data Science
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Author : Vijay Kotu
language : en
Publisher: Morgan Kaufmann
Release Date : 2018-11-27
Data Science written by Vijay Kotu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
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
Evaluation Of Novel Approaches To Software Engineering
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Author : Raian Ali
language : en
Publisher: Springer Nature
Release Date : 2021-02-26
Evaluation Of Novel Approaches To Software Engineering written by Raian Ali and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Computers categories.
This book constitutes selected, revised and extended papers of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2020, held in virtual format, in May 2020. The 19 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers included in this book contribute to the understanding of relevant trends of current research on novel approaches to software engineering for the development and maintenance of systems and applications, specically with relation to: model-driven software engineering, requirements engineering, empirical software engineering, service-oriented software engineering, business process management and engineering, knowledge management and engineering, reverse software engineering, software process improvement, software change and configuration management, software metrics, software patterns and refactoring, application integration, software architecture, cloud computing, and formal methods.
Ai Enabled Sustainable Innovations In Education And Business
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Author : Sorayyaei Azar, Ali
language : en
Publisher: IGI Global
Release Date : 2025-04-24
Ai Enabled Sustainable Innovations In Education And Business written by Sorayyaei Azar, Ali and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-24 with Computers categories.
Sustainability for the future is an ever-present concern. Modern innovations in education and business are enhancing resources and technology for improved sustainability-driven solutions. Artificial intelligence (AI), specifically, is transforming education and business by providing real-time data processing systems for decision support and streamlining processes. As a result, educators and business leaders are better able to allocate resources and maximize their impact on students, industries, and customers in addition to sustainability. By fostering efficiency and sustainability in education and business, AI may also increase individual environmental awareness and social responsibility. AI-Enabled Sustainable Innovations in Education and Business discusses technological advancements in digital education and learning, and in various industries, including healthcare, finance, and supply chains. It highlights advanced innovations for environmental, economic, and operational sustainability. Covering topics such as information and communication technology (ICT), state government programs, and automated device management, this book is an excellent resource for business leaders, executives, managers, educators, school administrators, technologists, computer engineers, sustainability advocates, professionals, researchers, scholars, academicians, and more.
Research Handbook On Services Management
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Author : Davis, Mark M.
language : en
Publisher: Edward Elgar Publishing
Release Date : 2022-08-05
Research Handbook On Services Management written by Davis, Mark M. and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-05 with Business & Economics categories.
This comprehensive Research Handbook reflects the latest research breakthroughs and practices in services management. Addressing services management from a broader strategic perspective, it delves into the key issues of analytics and service robots, and their potential impact. Edited by the late Mark M. Davis, it represents an early foray into the new frontier of services management and provides insights into the future of the field.
Ict For Competitive Strategies
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Author : Durgesh Kumar Mishra
language : en
Publisher: CRC Press
Release Date : 2020-05-05
Ict For Competitive Strategies written by Durgesh Kumar Mishra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-05 with Computers categories.
Fourth International Conference on Information and Communication Technology for Competitive Strategies targets state-of-the-art as well as emerging topics pertaining to information and communication technologies (ICTs) and effective strategies for its implementation for engineering and intelligent applications.
Data Science Solutions
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Author : Manav Sehgal
language : en
Publisher:
Release Date : 2017-02-07
Data Science Solutions written by Manav Sehgal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-07 with categories.
The field of data science, big data, machine learning, and artificial intelligence is exciting and complex at the same time. Data science is also rapidly growing with new tools, technologies, algorithms, datasets, and use cases. For a beginner in this field, the learning curve can be fairly daunting. This is where this book helps. The data science solutions book provides a repeatable, robust, and reliable framework to apply the right-fit workflows, strategies, tools, APIs, and domain for your data science projects. This book takes a solutions focused approach to data science. Each chapter meets an end-to-end objective of solving for data science workflow or technology requirements. At the end of each chapter you either complete a data science tools pipeline or write a fully functional coding project meeting your data science workflow requirements. SEVEN STAGES OF DATA SCIENCE SOLUTIONS WORKFLOW Every chapter in this book will go through one or more of these seven stages of data science solutions workflow. STAGE 1: Question. Problem. Solution. Before starting a data science project we must ask relevant questions specific to our project domain and datasets. We may answer or solve these during the course of our project. Think of these questions-solutions as the key requirements for our data science project. Here are some templates that can be used to frame questions for our data science projects. Can we classify an entity based on given features if our data science model is trained on certain number of samples with similar features related to specific classes?Do the samples, in a given dataset, cluster in specific classes based on similar or correlated features?Can our machine learning model recognise and classify new inputs based on prior training on a sample of similar inputs?STAGE 2: Acquire. Search. Create. Catalog.This stage involves data acquisition strategies including searching for datasets on popular data sources or internally within your organisation. We may also create a dataset based on external or internal data sources. The acquire stage may feedback to the question stage, refining our problem and solution definition based on the constraints and characteristics of the acquired datasets. STAGE 3: Wrangle. Prepare. Cleanse.The data wrangle phase prepares and cleanses our datasets for our project goals. This workflow stage starts by importing a dataset, exploring the dataset for its features and available samples, preparing the dataset using appropriate data types and data structures, and optionally cleansing the data set for creating model training and solution testing samples. The wrangle stage may circle back to the acquire stage to identify complementary datasets to combine and complete the existing dataset. STAGE 4: Analyse. Patterns. Explore.The analyse phase explores the given datasets to determine patterns, correlations, classification, and nature of the dataset. This helps determine choice of model algorithms and strategies that may work best on the dataset. The analyse stage may also visualize the dataset to determine such patterns. STAGE 5: Model. Predict. Solve.The model stage uses prediction and solution algorithms to train on a given dataset and apply this training to solve for a given problem. STAGE 6: Visualize. Report. Present.The visualization stage can help data wrangling, analysis, and modeling stages. Data can be visualized using charts and plots suiting the characteristics of the dataset and the desired results.Visualization stage may also provide the inputs for the supply stage.STAGE 7: Supply. Products. Services.Once we are ready to monetize our data science solution or derive further return on investment from our projects, we need to think about distribution and data supply chain. This stage circles back to the acquisition stage. In fact we are acquiring data from someone else's data supply chain.
New Trends In Database And Information Systems
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Author : Panos K. Chrysanthis
language : en
Publisher: Springer Nature
Release Date : 2025-09-21
New Trends In Database And Information Systems written by Panos K. Chrysanthis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-21 with Computers categories.
This book constitutes short papers, Doctoral Consortium and workshop papers which were presented at the 29th European Conference on New Trends in Databases and Information Systems, ADBIS 2025, which took place in Tampere, Finland, during September 23-26, 2025. This CCIS proceedings book contains 14 short papers and 3 demo papers from the main ADBIS 2025 conference. In addition, 24 out of 48 papers submitted to the workshops are included in this book. The papers have been organized in topical sections as follows: Query Optimization; Spatio-Temporal & Graph Data; Data Sharing & Synthesis; Entity Resolution & Integration; Doctoral Consortium School Invited Talks; MADEISD 2025: 7th Workshop on Modern Approaches in Data Engineering and Information System Design; DOING 2025: 6th Workshop on Intelligent Data - From Data to Knowledge;K-GALS 2025: 4th Workshop on Knowledge Graphs Analysis on a Large Scale; CAIMA 2025: 1st Workshop on Cooperative AI Models and Applications; ERGA 2025: 1st Workshop on Entity Resolution and Graph Alignment; FEHDA 2025: 1st Workshop on Fairness Exploration in Heterogeneous Data and Algorithms; and IT4TOCI 2025: 1st Workshop on Information Technology for Tourism and Culture Industries.
Managing Data Science
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Author : Kirill Dubovikov
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-12
Managing Data Science written by Kirill Dubovikov and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Computers categories.
Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.