Find, explore, and extract big data to transform into actionable insights
Key Features
Perform end-to-end data analysis-from exploration to visualization
Real-world examples, tasks, and interview queries to be a proficient data scientist
Understand how SQL is used for big data processing using HiveQL and SparkSQL
Book DescriptionSQL 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.
What you will learn
Understand what data science is and how SQL Server is used for big data processing
Analyze incoming data with SQL queries and visualizations
Create, train, and evaluate predictive models
Make predictions using trained models and establish regular retraining courses
Incorporate data source querying into SQL Server
Enhance built-in T-SQL capabilities using SQLCLR
Visualize data with Reporting Services, Power View, and Power BI
Transform data with R, Python, and Azure
Who this book is forHands-On Data Science with SQL Server 2017 is intended for data scientists, data analysts, and big data professionals who want to master their skills learning SQL and its applications. This book will be helpful even for beginners who want to build their career as data science professionals using the power of SQL Server 2017. Basic familiarity with SQL language will aid with understanding the concepts covered in this book.
Sprache
Verlagsort
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 27 mm
Gewicht
ISBN-13
978-1-78899-634-1 (9781788996341)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Marek Chmel is an IT consultant and trainer with more than 10 years' experience. He is a frequent speaker, focusing on Microsoft SQL Server, Azure, and security topics. Marek writes for Microsoft's TechnetCZSK blog and has been an MVP: Data Platform since 2012. He has earned numerous certifications, including MCSE: Data Management and Analytics, EC Council Certified Ethical Hacker, and several eLearnSecurity certifications. Marek earned his MSc (business and informatics) degree from Nottingham Trent University. He started his career as a trainer for Microsoft server courses. Later, he joined AT&T, as a principal database administrator specializing in MSSQL Server, Data Platform, and Machine Learning. Vladimir Muzny has been a freelance IT consultant, developer, and Microsoft data platform trainer since 2000. He is also a frequent speaker on local events in Czech Republic and Slovakia. His most favorite topics are not only MS SQL Server, but also data integration, data science or NoSQL topics. During his career, Vladimir has earned certifications such as MCSE: Data Management and Analytics, MVP: Data Platform and MCT. Nowadays, Vladimir is a data science enthusiast and works on data migration/integration projects also with output to machine learning models.
Table of Contents
Data Science Overview
SQL Server 2017 as a Data Science Platform
Data Sources for Analytics
Data Transforming and Cleaning with T-SQL
Data Exploration and Statistics with T-SQL
Custom Aggregations on SQL Server
Data Visualization
Data Transformations with Other Tools
Predictive Model Training and Evaluation
Making Predictions
Getting It All Together - A Real-World Example
Next Steps with Data Science and SQL
