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Books > Computing & IT > Applications of computing > Databases
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SQL for Data Science - Data Cleaning, Wrangling and Analytics with Relational Databases (Paperback, 1st ed. 2020)
Loot Price: R1,522
Discovery Miles 15 220
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SQL for Data Science - Data Cleaning, Wrangling and Analytics with Relational Databases (Paperback, 1st ed. 2020)
Series: Data-Centric Systems and Applications
Expected to ship within 18 - 22 working days
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This textbook explains SQL within the context of data science and
introduces the different parts of SQL as they are needed for the
tasks usually carried out during data analysis. Using the framework
of the data life cycle, it focuses on the steps that are very often
given the short shift in traditional textbooks, like data loading,
cleaning and pre-processing. The book is organized as follows.
Chapter 1 describes the data life cycle, i.e. the sequence of
stages from data acquisition to archiving, that data goes through
as it is prepared and then actually analyzed, together with the
different activities that take place at each stage. Chapter 2 gets
into databases proper, explaining how relational databases organize
data. Non-traditional data, like XML and text, are also covered.
Chapter 3 introduces SQL queries, but unlike traditional textbooks,
queries and their parts are described around typical data analysis
tasks like data exploration, cleaning and transformation. Chapter 4
introduces some basic techniques for data analysis and shows how
SQL can be used for some simple analyses without too much
complication. Chapter 5 introduces additional SQL constructs that
are important in a variety of situations and thus completes the
coverage of SQL queries. Lastly, chapter 6 briefly explains how to
use SQL from within R and from within Python programs. It focuses
on how these languages can interact with a database, and how what
has been learned about SQL can be leveraged to make life easier
when using R or Python. All chapters contain a lot of examples and
exercises on the way, and readers are encouraged to install the two
open-source database systems (MySQL and Postgres) that are used
throughout the book in order to practice and work on the exercises,
because simply reading the book is much less useful than actually
using it. This book is for anyone interested in data science and/or
databases. It just demands a bit of computer fluency, but no
specific background on databases or data analysis. All concepts are
introduced intuitively and with a minimum of specialized jargon.
After going through this book, readers should be able to profitably
learn more about data mining, machine learning, and database
management from more advanced textbooks and courses.
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