Making with data, 

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 52790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.

Автор: Haines Scott
Название: Modern Data Engineering with Apache Spark: A Hands-On Guide for Building Mission-Critical Streaming Applications
ISBN: 1484274512 ISBN-13(EAN): 9781484274514
Издательство: Springer
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Цена: 55890.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow. Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes. Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. What You Will Learn * Simplify data transformation with Spark Pipelines and Spark SQL * Bridge data engineering with machine learning * Architect modular data pipeline applications * Build reusable application components and libraries * Containerize your Spark applications for consistency and reliability * Use Docker and Kubernetes to deploy your Spark applications * Speed up application experimentation using Apache Zeppelin and Docker * Understand serializable structured data and data contracts * Harness effective strategies for optimizing data in your data lakes * Build end-to-end Spark structured streaming applications using Redis and Apache Kafka * Embrace testing for your batch and streaming applications * Deploy and monitor your Spark applications Who This Book Is For Professional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness and use Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 33790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Автор: Currell Graham
Название: Scientific Data Analysis
ISBN: 0198712545 ISBN-13(EAN): 9780198712541
Издательство: Oxford Academ
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Цена: 50680.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Drawing on the author`s extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.

Автор: Guerrero, Hector
Название: Excel data analysis
ISBN: 3030012786 ISBN-13(EAN): 9783030012786
Издательство: Springer
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Цена: 93160.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive and readable introduction to modern business and data analytics.

Автор: Timbers, Tiffany-anne (university Of British Colum
Название: Data science
ISBN: 0367524686 ISBN-13(EAN): 9780367524685
Издательство: Taylor&Francis
Рейтинг:
Цена: 50010.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.

Автор: Bremer Martina, Doerge Rebecca W.
Название: Using R at the Bench: Step-By-Step Data Analytics for Biologists
ISBN: 1621821129 ISBN-13(EAN): 9781621821120
Издательство: Неизвестно
Рейтинг:
Цена: 102690.00 T
Наличие на складе: Невозможна поставка.
Описание: Using R at the Bench: Step-by-Step Data Analytics for Biologists is a convenient bench-side handbook for biologists, designed as a handy reference guide for elementary and intermediate statistical analyses using the free/public software package known as R. The expectations for biologists to
have a more complete understanding of statistics are growing rapidly. New technologies and new areas of science, such as microarrays, next-generation sequencing, and proteomics, have dramatically increased the need for quantitative reasoning among biologists when designing experiments and
interpreting results. Even the most routine informatics tools rely on statistical assumptions and methods that need to be appreciated if the scientific results are to be correct, understood, and exploited fully.

Although the original Statistics at the Bench is still available for sale and has all examples in Excel, this new book uses the same text and examples in R. A new chapter introduces the basics of R: where to download, how to get started, and some basic commands and resources. There is also a new
chapter that explains how to analyze next-generation sequencing data using R (specifically, RNA-Seq). R is powerful statistical software with many specialized packages for biological applications and Using R at the Bench: Step-by-Step Data Analytics for Biologists is an excellent resource for those
biologists who want to learn R. This handbook for working scientists provides a simple refresher for those who have forgotten what they once knew and an overview for those wishing to use more quantitative reasoning in their research. Statistical methods, as well as guidelines for the interpretation
of results, are explained using simple examples. Throughout the book, examples are accompanied by detailed R commands for easy reference.

Автор: Maosong Sun, Xiaojie Wang, Baobao Chang
Название: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
ISBN: 3319690043 ISBN-13(EAN): 9783319690049
Издательство: Springer
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Цена: 35330.00 T
Наличие на складе: Есть
Описание: This book constitutes the proceedings of the 16th China National Conference on Computational Linguistics, CCL 2017, and the 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017, held in Nanjing, China, in October 2017. Minority language information processing.

Автор: Tanwar
Название: Blockchain Technology
ISBN: 9811914877 ISBN-13(EAN): 9789811914874
Издательство: Springer
Рейтинг:
Цена: 74530.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Blockchain is an emerging technology platform for developing decentralized applications and data storage, over and beyond its role as the technology underlying the cryptocurrencies.

Автор: Heeringa
Название: Applied Survey Data Analysis, Second Edition
ISBN: 1498761607 ISBN-13(EAN): 9781498761604
Издательство: Taylor&Francis
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Цена: 91860.00 T
Наличие на складе: Нет в наличии.
Описание: This book provides an overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more examples the analysis of survey data.

Автор: Bessiere
Название: Data Mining and Constraint Programming
ISBN: 3319501364 ISBN-13(EAN): 9783319501369
Издательство: Springer
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Цена: 54040.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.