Jump-start your career as a data scientist--learn to develop datasets for exploration analysis
and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for
Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset
design skills that data scientists use most. Aspiring data scientists will learn how to how to
construct datasets for exploration analysis and machine learning. You can also discover how
to approach query design and develop SQL code to extract data insights while avoiding common
pitfalls. You may be one of many people who are entering the field of Data Science from a range
of professions and educational backgrounds such as business analytics social science physics
economics and computer science. Like many of them you may have conducted analyses using
spreadsheets as data sources but never retrieved and engineered datasets from a relational
database using SQL which is a programming language designed for managing databases and
extracting data. This guide for data scientists differs from other instructional guides on the
subject. It doesn't cover SQL broadly. Instead you'll learn the subset of SQL skills that data
analysts and data scientists use frequently. You'll also gain practical advice and direction on
how to think about constructing your dataset. * Gain an understanding of relational database
structure query design and SQL syntax * Develop queries to construct datasets for use in
applications like interactive reports and machine learning algorithms * Review strategies and
approaches so you can design analytical datasets * Practice your techniques with the provided
database and SQL code In this book author Renee Teate shares knowledge gained during a 15-year
career working with data in roles ranging from database developer to data analyst to data
scientist. She guides you through SQL code and dataset design concepts from an industry
practitioner's perspective moving your data scientist career forward!
