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You may know SQL basics, but are you taking advantage of its
expressive power? This second edition applies a highly practical
approach to Structured Query Language (SQL) so you can create and
manipulate large stores of data. Based on real-world examples, this
updated cookbook provides a framework to help you construct
solutions and executable examples in several flavors of SQL,
including Oracle, DB2, SQL Server, MySQL, and PostgreSQL. SQL
programmers, analysts, data scientists, database administrators,
and even relatively casual SQL users will find SQL Cookbook to be a
valuable problem-solving guide for everyday issues. No other
resource offers recipes in this unique format to help you tackle
nagging day-to-day conundrums with SQL. The second edition
includes: Fully revised recipes that recognize the greater adoption
of window functions in SQL implementations Additional recipes that
reflect the widespread adoption of common table expressions (CTEs)
for more readable, easier-to-implement solutions New recipes to
make SQL more useful for people who aren't database experts,
including data scientists Expanded solutions for working with
numbers and strings Up-to-date SQL recipes throughout the book to
guide you through the basics
At first glance, the skills required to work in the data science
field appear to be self-explanatory. Do not be fooled. Impactful
data science demands an interdisciplinary knowledge of business
philosophy, project management, salesmanship, presentation, and
more. In Managing Your Data Science Projects, author Robert de
Graaf explores important concepts that are frequently overlooked in
much of the instructional literature that is available to data
scientists new to the field. If your completed models are to be
used and maintained most effectively, you must be able to present
and sell them within your organization in a compelling way. The
value of data science within an organization cannot be overstated.
Thus, it is vital that strategies and communication between teams
are dexterously managed. Three main ways that data science strategy
is used in a company is to research its customers, assess risk
analytics, and log operational measurements. These all require
different managerial instincts, backgrounds, and experiences, and
de Graaf cogently breaks down the unique reasons behind each. They
must align seamlessly to eventually be adopted as dynamic models.
Data science is a relatively new discipline, and as such, internal
processes for it are not as well-developed within an operational
business as others. With Managing Your Data Science Projects, you
will learn how to create products that solve important problems for
your customers and ensure that the initial success is sustained
throughout the product's intended life. Your users will trust you
and your models, and most importantly, you will be a more
well-rounded and effectual data scientist throughout your career.
Who This Book Is For Early-career data scientists, managers of data
scientists, and those interested in entering the field of data
science
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