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Hands-On Data Science with SQL Server 2017

Hands-On Data Science with SQL Server 2017

By : Marek Chmel , Vladimír Mužný
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Hands-On Data Science with SQL Server 2017

Hands-On Data Science with SQL Server 2017

By: Marek Chmel , Vladimír Mužný

Overview of this book

SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features. Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples. By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs.
Table of Contents (14 chapters)
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Transforming data

Data transformations refer to the infinite list of changes made on the data to reach its desired format. A lot of transformations can be done through simple expressions in queries, but there are also many challenges that are more complicated than these simple expressions. In this section, we will learn how to read data fully or incrementally, how to deduplicate data, and how to do data quality checks.

The first kind of transformation is often to recognize which data was loaded previously, if any. In the first section of this topic, we will learn how to load data fully or incrementally.

Full data load

Full data load means that every dataset extracted from the data source is fully loaded into the landing or...

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