Book Image

Hands-On Data Science with SQL Server 2017

By : Marek Chmel, Vladimír Mužný
Book Image

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)

Data science next steps

Data science is a prerequisite for numerous other fields of study that closely relate to data that we have available including globally the following:

  • Deep learning
  • Reinforced learning
  • Artificial Intelligence
  • Big-data processing and many others

AI and machine learning are commonly interchangeable, although they are not the same thing and are usually applied differently based on the available data and expected outcomes. In general terms, AI is a larger concept than machine learning, which is trying to mimic cognitive behavior in humans.

A very frequent approach to training computers to think such as humans, or to implement AI, is the usage of neural networks. Same neural networks can be used also for deep learning, where the algorithm is mimicking the human neocortex in the human brain, allowing the algorithm to learn to recognize patterns in digital...