▶Book Description
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.
▶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
▶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
▶Who This Book Is For
Hands-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.
▶What this book covers
⦁ Chapter 1, Data Science Overview, covers what the term data science means, the need for data science, the difference compared with traditional BI/DWH, and the competencies and knowledge required in order to be a data scientist.
⦁ Chapter 2, SQL Server 2017 as a Data Science Platform, explains the architecture of SQL Server from a data science perspective: in-memory OLTP for data acquisition; integration services as a transformation feature set; reporting services for visualization of input as well as output data; and, probably most importantly of all, T-SQL as a language for data exploration and transformation and machine learning services for making models themselves.
⦁ Chapter 3, Data Sources for Analytics, covers relational databases and NoSQL concepts sideby- side as valuable sources of data with a different approach to use. It also provides an overview of technologies such as HDInsight, Apache Hadoop, and Cosmos DB, and querying against such data sources.
⦁ Chapter 4, Data Transforming and Cleaning with T-SQL, demonstrates T-SQL techniques that are useful for making data consumable and complete for further utilization in data science, along with database architectures that are useful for transform/cleansing tasks.
⦁ Chapter 5, Data Exploration and Statistics with T-SQL, takes a deep dive into T-SQL capabilities, including common grouping and aggregations, framing/windowing, running aggregates, and (if needed) features such as custom CLR aggregates (with performance considerations).
⦁ Chapter 6, Custom Aggregations on SQL Server, explains how to create your own aggregations in order to enhance core T-SQL functionality.
⦁ Chapter 7, Data Visualization, explains the importance of visualizing data to reveal hidden patterns therein, along with examples of reporting services, PowerView, and PowerBI. By way of an alternative, an overview of R/Python visualization features is also provided (as these languages will play a vital role later in the book).
⦁ Chapter 8, Data Transformations with Other Tools, explains how to use integration services, probably R or Python, to transform data into a useful format, replacing missing values, detecting mistakes in datasets, normalization and its purpose, categorization, and finally data denormalization for better analytic purposes using views.
⦁ Chapter 9, Predictive Model Training and Evaluation, concerns a wide set of predictive models (clustering, N-point Bayes machines, recommenders) and their implementations via Machine Learning Studio, R, or Python.
⦁ Chapter 10, Making Predictions, explains how to use models created, evaluated, and scored in previous chapters. We will also learn how to make the model self-learning from the predictions made.
⦁ Chapter 11, Getting It All Together – a Real-World Example, demonstrates how to use certain features to grab, transform, and analyze data for a successful data science case.
⦁ Chapter 12, Next Steps with Data Science and SQL, summarizes the main points of all the preceding chapters and concludes outcomes. The chapter also provides ideas of how to continue working with data science, which trends are probably awaited in the future, and which other technologies will play strong roles in data science.
