Effective Data Science InfrastructureEffective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you'll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You'll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python.
The author is donating proceeds from this book to charities that support women and underrepresented groups in data science.
Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from ...
Data-Oriented ProgrammingData-Oriented Programming is a one-of-a-kind guide that introduces the data-oriented paradigm. This groundbreaking approach represents data with generic immutable data structures. It simplifies state management, eases concurrency, and does away with the common problems you'll find in object-oriented code. The book presents powerful new ideas through conversations, code snippets, and diagrams that help you quickly grok what's great about DOP. Best of all, the paradigm is language-agnostic - you'll learn to write DOP code that can be implemented in JavaScript, Ruby, Python, Clojure, and also in traditional OO languages like Java or C#.
Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose fun ...
Exploring Graphs with ElixirData is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data!
Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distribute ...
Jira 8 Essentials, 6th EditionThis new and improved sixth edition comes with the latest Jira 8.21 Data Center offerings, with enhanced features such as clustering, advanced roadmaps, custom field optimization, and tools to track and manage tasks for your projects. This comprehensive guide to Jira 8.20.x LTS version provides updated content on project tracking, issue and field management, workflows, Jira Service Management, and security.
The book begins by showing you how to plan and set up a new Jira instance from scratch before getting you acquainted with key features such as emails, workflows, and business processes. You'll also get to grips with Jira's data hierarchy and design and work with projects. Since Jira is used for issue management, this book will help you understand the different issues that can arise in your projects. As you advance, you'll create new screens from scratch and customize them to suit your requirements. Workflows, business processes, and guides on setting up incoming and outgoing mail ...
Power Platform and Dynamics 365 CE for Absolute BeginnersThis is your complete guide to less-code and no-code theories, along with practical application of Microsoft Power Apps and Dynamics 365 CE/CRM Apps.
The book covers topics including the configurations, customizations, and enhancements in Microsoft Power Apps and Dynamics 365 CE/CRM Apps. You will start by learning Microsoft Dataverse concepts followed by Microsoft Canvas Apps, model-driven apps, and PowerApps Portals. You will understand how to work with Power Virtual Agent, Power BI, and Power Automate, and how to use AI in Power Apps. The book provides important integration concepts for Power Apps, Dynamics 365 CE/CRM Apps, and Microsoft Azure. You will know how to customize Dynamics 365 CE/CRM Apps and Power Apps using OOTB capabilities.
After reading this book, you will understand how Microsoft Power Apps and Dynamics 365 CE/CRM Apps can be used, configured, and customized for your business needs using customer data. You will be able to increase efficiency in customer data m ...
Python for Geospatial Data AnalysisIn spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions.
Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. ...
Data Quality Engineering in Financial ServicesData quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines. ...
Learning Tableau 2022, 5th EditionLearning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features.
This new edition is updated with Tableau's latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau.
After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data.
You'll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources wit ...
Data Quality FundamentalsDo your product dashboards look funky? Are your quarterly reports stale? Is the data set 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 answered yes to these questions, this book is for you.
Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. ...
Snowflake: The Definitive GuideSnowflake's ability to eliminate data silos and run workloads from a single platform creates opportunities to democratize data analytics, allowing users at all levels within an organization to make data-driven decisions. Whether you're an IT professional working in data warehousing or data science, a business analyst or technical manager, or an aspiring data professional wanting to get more hands-on experience with the Snowflake platform, this book is for you.
You'll learn how Snowflake users can build modern integrated data applications and develop new revenue streams based on data. Using hands-on SQL examples, you'll also discover how the Snowflake Data Cloud helps you accelerate data science by avoiding replatforming or migrating data unnecessarily. ...