SQL for Data Scientists: A Beginner's Guide to Creating Analytical Datasets

Are you an experienced developer interested in expanding your skill set to include database management?
Not certain how to get ready for the future that data will drive?
This book focuses on Structured queries (SQL) and the creation of datasets, two of the most important abilities for data scientists. Training data scientists will acquire the knowledge necessary to generate datasets for exploration, analytics, and machine learning. You can also understand how to approach question design and write SQL statements to extract data and insights while avoiding typical problems.
This information is provided to you:

Introduction to Data Science

SQL for Data Science

Exploratory data analysis

Data and sampling distributions

Statistical experiments and significant tasting

Regression and prediction

Classifications

Statistical machine learning

Unsupervised learning

You may be unique among the several people entering the area of Data Science from various professions or education backgrounds, such as business intelligence, social science, physics, finance, and computer science. If this designates your condition, you are not alone. You may have performed studies by using a spreadsheet as a data source, just like many of them have, but you have probably never retrieved and created datasets from a database system using SQL, which is a computer language built for managing databases or extracting data.
In contrast to previous instructional resources on the topic, this guide is tailored specifically for data scientists. It does not comprehensively cover SQL. Instead, you will become proficient in the SQL abilities that data scientists and analysts most frequently utilize. You will also receive direction and practical advice on "how to think about generating your dataset," which you will provide.