Книги по Big Data/DataScience

Практическое руководство по анализу данных, раскрывающее полный цикл работы с информацией: от сбора и обработки до построения моделей машинного обучения. Рассмотрена работа с инструментами pandas и SQL, методы выявления закономерностей и очистки данных. Описаны различные источники информации, включая текстовые, бинарные и веб-данные. Подробно изложены статистические методы: доверительные интервалы...

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems.In Data Without Labels you’ll learn:Fundamental building blocks and concepts of machine learning and unsupervised learningData cleaning for structured and unstructured data like text and imagesClustering...

If you know how to program, you have the skills to turn data into knowledge. This thoroughly revised edition presents statistical concepts computationally, rather than mathematically, using programs written in Python. Through practical examples and exercises based on real-world datasets, you'll learn the entire process of exploratory data analysis—from wrangling data and generating statistics to i...

Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key...

Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs w...

Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process.In this hands-on guide, author Anthony Sarkis--lead engineer for the Diffgram AI traini...

Рассмотрены современные методы очистки, анализа и визуализации данных в Microsoft Excel. Описаны инструменты Power Query для создания воспроизводимых процессов подготовки данных, средства Power Pivot для построения реляционных моделей и настройки аналитических показателей. Приведены практические примеры использования динамических массивов, функций на базе искусственного интеллекта и интеграции с я...

In a world where data sovereignty, scalability, and AI innovation are at the forefront of enterprise strategy, PostgreSQL is emerging as the key to unlocking transformative business value. This new guide serves as your beacon for navigating the convergence of AI, open source technologies, and intelligent data platforms. Authors Tom Taulli, Benjamin Anderson, and Jozef de Vries offer a strategic an...

Deliver flexible, scalable, and high-performance data storage that's perfect for AI and other modern applications with MongoDB 8.0 and MongoDB Atlas multi-cloud data platform.In MongoDB 8.0 in Action, Third Edition you'll find comprehensive coverage of the latest version of MongoDB 8.0 and the MongoDB Atlas multi-cloud data platform. Learn to utilize MongoDB’s flexible schema design for...

Enables readers to analyze and design systems ― not just read about ITSystems Analysis and Design: An Object-Oriented Approach with UML, Seventh Edition captures the dynamic aspects of the field by keeping students focused on doing SAD while presenting the core set of skills that every systems analyst needs to know today and in the future.The team of expert authors introduces each major technique,...

Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function.Specializ...

Python is doubtless the most versatile programming language.But are you serious enough about becoming proficient in Python?If yes, then you need to become a master in the two essential Python libraries—NumPy and Pandas. You simply can’t overlook this truth.In data science, NumPy and Pandas are by far the most widely used Python libraries. The main features of these libraries are powerful data anal...

In Data Analysis with Python and PySpark you will learn how to:Manage your data as it scales across multiple machinesScale up your data programs with full confidenceRead and write data to and from a variety of sources and formatsDeal with messy data with PySpark’s data manipulation functionalityDiscover new data sets and perform exploratory data analysisBuild automated data pipelines tha...

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.Author...

Computers store and process data with an extra ordinary speed and accuracy. So, it is highly essential that the data is stored efficiently and can be accessed fast. Also, the processing of data should happen in the smallest possible time, but without losing the accuracy.Data structures deal with how the data is organised and held in the memory, when a program processes it. It is important to note...