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Key benefits

Use the features of SQL Server 2017 to implement the data science project life cycle

Leverage the power of R and Python to design and develop efficient data models

find unique insights from your data with powerful techniques for data preprocessing and analysis

Description

SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm.

What you will learn

• Use the popular programming languages,T-SQL, R, and Python, for data science

• Understand your data with queries and introductory statistics

• Create and enhance the datasets for ML

• Visualize and analyze data using basic and advanced graphs

• Explore ML using unsupervised and supervised models

• Deploy models in SQL Server and perform predictions

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Data Science with SQL Server Quick Start Guide

NZ$54.99

Hands-On Data Science with SQL Server 2017

NZ$71.99

NZ$89.99

Total NZ$ 216.97

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Rubén Dec 20, 2018

First of all, I want to explain that I have been given a copy of this book and that Dejan works in the same company I work but that is not conditioning my review. I have also read some other books authored or coauthored by Dejan covering topics like T-SQL programming & DW design.Cons----Maybe my expectations were too high or I'm just used to materials which go deeper on the topics than this book. This book pretends to be an introduction to data science using SQL Server an ML Services and it also pretends to cover all the stages of a data science project, all in a book of fewer than 200 pages, which seems a bit too optimistic.The end half of the book is where the most interesting topics are presented but everything seems a bit "accelerated" and some complex concepts are directly applied without many previous explanations. The number of different packages and libraries used in the examples increases a lot page after page making a bit complicated to follow the code (highly recommended to have side by side the libraries documentation). I also find somehow distracting to be jumping from one language to the other (R vs Python) instead of having the examples in both languages. I guess that some graphics engine/libraries and algorithms are only implemented on R or Python libraries so that jumping is not avoidable.Pros-----I think this book is useful for data professionals which had no previous knowledge of data science and R/Python. The first three chapters, a third of the book, are a basic introduction to T-SQL, R, and Python. Then we find an introduction to the data science project life cycle plus some basic statistical concepts. The second half of the book is the most interesting part from my point of view.In the second half of the book, we will get some insights on some discretization algorithms first and how to explore and measure the associations between variables. The unsupervised & supervised algorithms and methods presented are a good selection and they seem useful for lots of real scenarios. Most of the time you will need a "trial and error" approximation to see which one fits better, generate a better model, gets more accurate predictions, etc. I guess that an experienced data scientist will do a good initial "filtering" intuitively before and he/she will select the ones more promising based on previous experience & knowledge.Maybe the potential readers expect more on some topics but this book succeeded in its main goal; getting into the world of data science with SQL Server and its most used languages and algorithms.

Amazon Verified review

Data Science with SQL Server Quick Start Guide: Integrate SQL Server with data science

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