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Data and analytics are essential for any business, giving insight into what's working, what can be improved, and what else needs to be done. SAS software helps you make sure you're doing data right, with a host of data management, reporting, and analysis tools. SAS For Dummies teaches you the essentials, helping you navigate this statistical software and turn information into value. In this book, learn how to gather data, create reports, and analyze results. You'll also discover how SAS machine learning and AI can help deliver decisions based on data.

Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast.

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each.

Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.

Microsoft Power Platform for Dummies covers the essentials you need to know to get started with Microsoft Power Platform, the suite of business intelligence applications designed to make your enterprise work smarter and more efficiently.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.