Книги по Big Data/DataScience

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...

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney...

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those...

The easy way to get started coding and analyzing data in the R programming languageStatistical Analysis with R Essentials For Dummies is your reference to all the core concepts about R―the widely used, open-source programming language and data analysis tool. This no-nonsense book gets right to the point, eliminating review material, wordy explanations, and fluff. Understand all you need to kn...

Python is one of the most popular programming languages in the world, used for everything from shell scripts to web development to data science. As a result, Python is a great language to learn, but you don't need to learn "everything" to get started, just how to use it efficiently to solve real problems. In Learn Enough Python to Be Dangerous, renowned instructor Michael Hartl teaches the sp...

Алгоритмы — это сердце программирования. От их правильного выбора зависит, будет ли программа работать мгновенно или заставит вас ждать вечность. Но как разобраться во всем этом, если вы только в начале пути? Эта яркая книга делает изучение алгоритмов и структур данных простым и увлекательным. Благодаря полноцветным иллюстрациям, схемам и наглядным примерам сложные концепции становятся понятными д...

This book gives readers a hands-on understanding of Excel-assisted statistical techniques to take effective business decisions. It showcases applications of the tools and techniques of statistics for analysing business data from the domain of business statistics.The volume provides an exhaustive introduction to the application of statistics in solving business problems and implementing data analyt...

As an aspiring data scientist, you appreciate why organizations rely on data for important decisions—whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of col...