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Data Analysis with Python
Data Analysis with Python

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you're likely to meet in today. The first of these is an image recognition application with TensorFlow - embracing the importance today of AI in your data analysis. The secon ...
Data Science with Python and Dask
Data Science with Python and Dask

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework ...
Oracle High Availability, Disaster Recovery, and Cloud Services
Oracle High Availability, Disaster Recovery, and Cloud Services

Work with Oracle database's high-availability and disaster-management technologies. This book covers all the Oracle high-availability technologies in one place and also discusses how you configure them in engineered systems and cloud services. You will see that when you say your database is healthy, it is not limited to whether the database is performing well on day-to-day operations; rather it should also be robust and free from disasters. As a result, your database will be capable of handling unforeseen incidents and recovering from disaster with very minimal or zero downtime. Oracle High Availability, Disaster Recovery, and Cloud Services explores all the high-availability features of Oracle database, how to configure them, and best practices. After you have read this book you will have mastered database high-availability concepts such as RAC, Data Guard, OEM 13c, and engineered systems (Oracle Exadata x6/x7 and Oracle Database Appliance). Master the best practices and feat ...
Mining Social Media
Mining Social Media

Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you're a professional journalist, an academic researcher, or a citizen investigator, you'll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: Write Python scripts and use APIs to gather data from the social web; Download data archives and dig through them for insights; Inspect HTML downloaded from websites for useful content; Format, aggregate, sort, and filter your collected data using Google Sheets; Create data visualizations to illustrate your discoveries; Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library; Apply what you've learned to research topics on you ...
PolyBase Revealed
PolyBase Revealed

Harness the power of PolyBase data virtualization software to make data from a variety of sources easily accessible through SQL queries while using the T-SQL skills you already know and have mastered. PolyBase Revealed shows you how to use the PolyBase feature of SQL Server 2019 to integrate SQL Server with Azure Blob Storage, Apache Hadoop, other SQL Server instances, Oracle, Cosmos DB, Apache Spark, and more. You will learn how PolyBase can help you reduce storage and other costs by avoiding the need for ETL processes that duplicate data in order to make it accessible from one source. PolyBase makes SQL Server into that one source, and T-SQL is your golden ticket. The book also covers PolyBase scale-out clusters, allowing you to distribute PolyBase queries among several SQL Server instances, thus improving performance. With great flexibility comes great complexity, and this book shows you where to look when queries fail, complete with coverage of internals, troubleshooting tech ...
Practical Data Science with R, 2nd Edition
Practical Data Science with R, 2nd Edition

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. Practical Data Science with R, 2nd Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you'll face on the job, this friendly guide is comfort ...
Pandas 1.x Cookbook, 2nd Edition
Pandas 1.x Cookbook, 2nd Edition

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. ...
T-SQL Window Functions, 2nd Edition
T-SQL Window Functions, 2nd Edition

Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You'll find a detailed section on optimization, plus an extensive collection of business solutions - including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: Use window functions to improve queries you previously built with predicates; Master essential SQL windowing concepts, and efficiently design window functions; Effectively utilize partitioning, ordering, and framing; Gain practical in-depth insight into window aggregate, ranking, offset, and statist ...
Fundamentals of Data Visualization
Fundamentals of Data Visualization

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value; Understand the importance of redundant coding to ensure you provide key information in multiple ways; Use the book's visualizations directory, a graphica ...
Data Science from Scratch, 2nd Edition
Data Science from Scratch, 2nd Edition

To really learn data science, you should not only master the tools - data science libraries, frameworks, modules, and toolkits - but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data. Get a crash course in Python; Learn the basics of linear algebra, statistics, and probability - and how and when they're used in data science; Collect, explore, clean, munge, and manipulate data; Dive into the fundamentals of machine ...
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