Data Analytics

Radio 4's Book of the WeekA Financial Times Book of the YearShortlisted for the 2020 Financial Times / McKinsey Business Book of the YearLonglisted for the National Book Award 'The story of the original data science hucksters of the 1960s is hilarious,...

Perform fast interactive analytics against different data sources using the Trino high-performance distributed SQL query engine. In the second edition of this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's a...

The missing expert-led manual for the AWS ecosystem - go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data...

Data is constantly increasing; everything from app usage, to sales, to customer surveys generate data in an average business. Out on the streets data is everywhere too, from speed and security cameras, weather monitoring and measuring footfall to name...

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and...

What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-but you still fail. The reason? Bad data semantics. ...

Businesses are gathering data today at exponential rates and yet few people know how to access it meaningfully. If you're a business or IT professional, this short hands-on guide teaches you how to pull and transform data with SQL in significant ways...

A practical guide to making good decisions in a world of missing dataIn the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip...

Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery...

Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you...

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter,...

Most of the high-profile cases of real or perceived unethical activity in data science aren't matters of bad intent. Rather, they occur because the ethics simply aren't thought through well enough. Being ethical takes constant diligence, and in many...

Discover how graph databases can help you manage and query highly connected data. With this practical book, you'll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you...

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization,...

Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features Book DescriptionOrganizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide...

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this...

Your logical, linear guide to the fundamentals of data science programming Data science is exploding-in a good way-with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job...

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark...

A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This...

Get a comprehensive overview on how to set up and design an effective database with MySQL. This thoroughly updated edition covers MySQL's latest version, including its most important aspects. Whether you're deploying an environment, troubleshooting an...

Discover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate...

Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings...

A practical guide to making good decisions in a world of missing dataIn the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip...

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed...

Probability and Statistics for Data Science: Math + R + Data covers "math stat"-distributions, expected value, estimation etc.-but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is...

To lead a data science team, you need to expertly articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. In this practical guide, you'll learn leadership techniques the authors...

Baseball is not the only sport to use "moneyball." American football fans, teams, and gamblers are increasingly using data to gain an edge against the competition. Professional and college teams use data to help select players and identify team needs...

A much-needed guide to implementing new technology in workspaces From experts in the field comes Machine Learning Upgrade: A Data Scientist's Guide to MLOps, LLMs, and ML Infrastructure, a book that provides data scientists and managers with best...

Do your product dashboards look funky? Are your quarterly reports stale? Is the dataset you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you...

Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually...

Visualization is the graphic presentation of data -- portrayals meant to reveal complex information at a glance. Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not...

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data-fake data...

An engaging and essential discussion of generative artificial intelligence In Generative AI: Navigating the Course to the Artificial General Intelligence Future, celebrated author Martin Musiol-founder and CEO of generativeAI.net and GenAI Lead for...

Solve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyondHarness the power of R to build flexible, effective, and...

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While...

Master the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. For demand planners, S&OP managers, supply chain leaders, and data scientists. Demand Forecasting Best Practices...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781088205792Author Gulrez KhanFormat PaperbackPage Count 126Imprint IngramSparkPublisher IngramSparkWeight(grams) 231g

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Key Features Delve into robust AWS tools for...

Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools...

The past two decades have seen an explosion both in the volume of data we use, and our understanding of its management.However, while techniques and technology for manipulating data have advanced rapidly in this time, the concepts around the value of our...