Applying Data Science

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Applying Data Science

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Author : Gerhard Svolba
language : en
Publisher: SAS Institute
Release Date : 2017-03-29

Applying Data Science written by Gerhard Svolba and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-29 with Computers categories.

See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.

Applied Data Science

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Author : Martin Braschler
language : en
Publisher: Springer
Release Date : 2019-06-13

Applied Data Science written by Martin Braschler and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-13 with Computers categories.

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Applied Data Science And Machine Learning For Business Optimization 2025

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Author : Manish tripathi, Dr. Anshita Shukla
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Applied Data Science And Machine Learning For Business Optimization 2025 written by Manish tripathi, Dr. Anshita Shukla and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In today’s data-driven world, businesses are increasingly turning to data science and machine learning (ML) to gain a competitive edge, optimize operations, and make informed decisions. The ability to harness large volumes of data and apply advanced analytical techniques is transforming industries, enabling businesses to improve efficiency, reduce costs, and unlock new growth opportunities. As we enter an era where data is one of the most valuable assets, understanding how to apply data science and ML to real-world business problems is becoming an essential skill for professionals across all sectors. “Applied Data Science and Machine Learning for Business Optimization” aims to provide practical insights into how data science and ML can be utilized to optimize business functions and drive strategic decision-making. This book bridges the gap between theory and practice, offering actionable guidance on implementing advanced analytics and machine learning techniques to solve common business challenges. Whether you are a business analyst, data scientist, or decision-maker, this book equips you with the tools, techniques, and real-world examples needed to leverage data science for business success. The core focus of this book is on applying data science and ML to optimize critical areas of business, such as operations, marketing, customer experience, finance, and supply chain management. Each chapter walks through the methodologies used in data analysis, model building, and performance evaluation, providing a hands-on approach that empowers readers to apply these techniques to their own business contexts. From predictive analytics to recommendation systems, natural language processing, and optimization algorithms, the book covers a wide range of ML tools that are instrumental in solving real-world business problems. A major goal of this book is to showcase the power of data-driven decision-making. With the exponential growth of data and computing power, businesses now have unprecedented opportunities to analyze trends, predict future outcomes, and automate decision-making processes. However, it’s crucial to approach these opportunities with a clear understanding of how to integrate data science and ML into the organizational workflow, while ensuring alignment with business goals and strategies. We believe that the application of data science and ML should not be limited to advanced technologists alone. This book is written to demystify these technologies and make them accessible to business professionals, regardless of their technical background. By focusing on practical case studies, real-world examples, and step-by-step instructions, we hope to empower readers to implement data science and ML solutions that drive measurable business outcomes. Ultimately, the journey of business optimization through data science and machine learning is a continual process of learning, adapting, and evolving. As businesses begin to adopt and scale these technologies, they will unlock new capabilities, enhance operational efficiencies, and build a more agile, data-driven organization. “Applied Data Science and Machine Learning for Business Optimization” serves as a foundational resource to help navigate this transformative journey. We hope this book inspires you to harness the power of data science and machine learning in your own organization, unlocking innovative solutions and driving impactful changes in your business. Authors

Apply Data Science

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Author : Thomas Barton
language : en
Publisher:
Release Date : 2023

Apply Data Science written by Thomas Barton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

This book offers an introduction to the topic of data science based on the visual processing of data. It deals with ethical considerations in the digital transformation and presents a process framework for the evaluation of technologies. It also explains special features and findings on the failure of data science projects and presents recommendation systems in consideration of current developments. Machine learning functionality in business analytics tools is compared and the use of a process model for data science is shown. The integration of renewable energies using the example of photovoltaic systems, more efficient use of thermal energy, scientific literature evaluation, customer satisfaction in the automotive industry and a framework for the analysis of vehicle data serve as application examples for the concrete use of data science. The book offers important information that is just as relevant for practitioners as for students and teachers. The Content Introduction to Data Science Systems, tools and methods Applications The target groups IT consultants and management consultants Project managers and project staff Students and teachers of business informatics, computer science and business administration The editors Prof. Dr Thomas Barton is a professor at Worms University of Applied Sciences. His focus is on the development of operational applications, e-business, cloud computing and data science. Prof. Dr Christian Müller is a professor at the Technical University of Wildau. His focus is on operations research, simulation of business processes and internet technologies.

Machine Learning And Knowledge Discovery In Databases Applied Data Science Track

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Author : Albert Bifet
language : en
Publisher: Springer Nature
Release Date : 2024-09-01

Machine Learning And Knowledge Discovery In Databases Applied Data Science Track written by Albert Bifet and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-01 with Computers categories.

This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024. The papers presented in these proceedings are from the following three conference tracks: - Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII. Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII. Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.

Machine Learning And Knowledge Discovery In Databases Applied Data Science Track

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Author : Yuxiao Dong
language : en
Publisher: Springer Nature
Release Date : 2021-02-24

Machine Learning And Knowledge Discovery In Databases Applied Data Science Track written by Yuxiao Dong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-24 with Computers categories.

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Materials Informatics And Catalysts Informatics

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Author : Keisuke Takahashi
language : en
Publisher: Springer Nature
Release Date : 2024-03-30

Materials Informatics And Catalysts Informatics written by Keisuke Takahashi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-30 with Technology & Engineering categories.

This textbook is designed for students and researchers who are interested in materials and catalysts informatics with little to no prior experience in data science or programming languages. Starting with a comprehensive overview of the concept and historical context of materials and catalysts informatics, it serves as a guide for establishing a robust materials informatics environment. This essential resource is designed to teach vital skills and techniques required for conducting informatics-driven research, including the intersection of hardware, software, programming, machine learning within the field of data science and informatics. Readers will explore fundamental programming techniques, with a specific focus on Python, a versatile and widely-used language in the field. The textbook explores various machine learning techniques, equipping learners with the knowledge to harness the power of data science effectively. The textbook provides Python code examples, demonstrating materials informatics applications, and offers a deeper understanding through real-world case studies using materials and catalysts data. This practical exposure ensures readers are fully prepared to embark on their informatics-driven research endeavors upon completing the textbook. Instructors will also find immense value in this resource, as it consolidates the skills and information required for materials informatics into one comprehensive repository. This streamlines the course development process, significantly reducing the time spent on creating course material. Instructors can leverage this solid foundation to craft engaging and informative lecture content, making the teaching process more efficient and effective.

Product Focused Software Process Improvement

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Author : Michael Felderer
language : en
Publisher: Springer
Release Date : 2017-11-10

Product Focused Software Process Improvement written by Michael Felderer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-10 with Computers categories.

This book constitutes the refereed proceedings of the 18th International Conference on Product-Focused Software Process Improvement, PROFES 2017, held in Innsbruck, Austria, in November/December 2017. The 17 revised full papers presented together with 10 short papers, 21 workshop papers. 3 posters and tool demonstrations papers, and 4 tutorials were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on : Agile software Development; Data science and analytics; Software engineering processes and frameworks; Industry relevant qualitative research; User and value centric approaches; Software startups; Serum; Software testing.

Proceedings Of The 2024 International Conference On Applied Economics Management Science And Social Development Aemss 2024

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Author : T. Ramayah
language : en
Publisher: Springer Nature
Release Date : 2024-05-27

Proceedings Of The 2024 International Conference On Applied Economics Management Science And Social Development Aemss 2024 written by T. Ramayah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-27 with Social Science categories.

This is an open access book. 2024 International Conference on Applied Economics, Management Science and Social Development (AEMSS 2024) will be held in Luoyang, China during March 22-24, 2024. The conference mainly focuses on research fields such as applied economics, management science, and social development. The conference aims to provide a platform for experts, scholars, engineering technicians, and technical R&D personnel engaged in the research of applied economics, management science, and social development to share scientific research achievements and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and exploration, and promote cooperation in the industrialization of academic achievements. The conference cordially invites experts, scholars, business professionals, and other relevant personnel from domestic and foreign universities, research institutions, and other relevant personnel to participate and exchange ideas!

Understanding Cybersecurity Management In Fintech

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Author : Gurdip Kaur
language : en
Publisher: Springer Nature
Release Date : 2021-08-04

Understanding Cybersecurity Management In Fintech written by Gurdip Kaur and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-04 with Business & Economics categories.

This book uncovers the idea of understanding cybersecurity management in FinTech. It commences with introducing fundamentals of FinTech and cybersecurity to readers. It emphasizes on the importance of cybersecurity for financial institutions by illustrating recent cyber breaches, attacks, and financial losses. The book delves into understanding cyber threats and adversaries who can exploit those threats. It advances with cybersecurity threat, vulnerability, and risk management in FinTech. The book helps readers understand cyber threat landscape comprising different threat categories that can exploit different types of vulnerabilties identified in FinTech. It puts forward prominent threat modelling strategies by focusing on attackers, assets, and software and addresses the challenges in managing cyber risks in FinTech. The authors discuss detailed cybersecurity policies and strategies that can be used to secure financial institutions and provide recommendations to secure financial institutions from cyber-attacks.