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Data Science IT Books

Data Pipelines Pocket Reference

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today’s modern data stack.

You’ll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions.

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Data Science IT Books

Data Architecture & Engineering

Data Engineering, DataOps, Data Science, Machine Learning, and AI are considered specialty occupations. On a daily basis, both engineers and data scientists in these categories, work on different frameworks and techniques to support their company’s data strategy.

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Data Science Software Architecture

Architecture for High-Throughput Low-Latency Big Data Pipeline on Cloud

Scalable and efficient data pipelines are as important for the success of analytics, data science, and machine learning as reliable supply lines are for winning a war.

For deploying big-data analytics, data science, and machine learning (ML) applications in the real world, analytics-tuning and model-training is only around 25% of the work. Approximately 50% of the effort goes into making data ready for analytics and ML. The remaining 25% effort goes into making insights and model inferences easily consumable at scale. The big data pipeline puts it all together. It is the railroad on which heavy and marvelous wagons of ML run. Long-term success depends on getting the data pipeline right.

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Blockchain Data Science IT Books

Big Data and Blockchain for Service Operations Management

This book aims to provide the necessary background to work with big data blockchain by introducing some novel applications in service operations for both academics and interested practitioners, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book intends to cover theory, research, development, and applications of big data and blockchain, as embedded in the fields of mathematics, engineering, computer science, physics, economics, business, management, and life sciences, to help service operations management.

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Data Science IT Books

Machine Learning Fundamentals: A Concise Introduction

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

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Blockchain Data Science Innovation

Blockchain and Deep Learning Future Trends and Enabling Technologies

This book introduces to blockchain and deep learning and explores and illustrates the current and new trends that integrate them. The pace and speeds for connectivity are certain on the ascend.

Blockchain and deep learning are twin technologies that are integral to integrity and relevance of network contents. Since they are data-driven technologies, rapidly growing interests exist to incorporate them in efficient and secure data sharing and analysis applications. Blockchain and deep learning are sentinel contemporary research technologies.

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Data Science Software Architecture

Emerging Architectures for Modern Data Infrastructure

The growth of the data infrastructure industry has continued unabated since we published a set of reference architectures in late 2020. Nearly all key industry metrics hit record highs during the past year, and new product categories appeared faster than most data teams could reasonably keep track. Even the benchmark wars and billboard battles returned.

To help data teams stay on top of the changes happening in the industry, we’re publishing in this post an updated set of data infrastructure architectures. They show the current best-in-class stack across both analytic and operational systems, as gathered from numerous operators we spoke with over the last year. Each architectural blueprint includes a summary of what’s changed since the prior version.

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Data Science Innovation

The Data Portfolio Guidebook

You’ve made the decision to pursue a career in data, learned the skills needed for the job and began applying, but you’re still not getting a job. Maybe you’re not even getting an interview! You’ve spent hours learning Excel, BI tools, SQL, maybe even Python and you know you’re qualified. If only you could show […]


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