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Documentation and information --- Mass communications --- Computer science --- Information systems --- Information science --- Communication --- Information theory. --- Philosophy. --- Communication theory --- Cybernetics --- Information literacy --- Library science
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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.
Database management. --- Big data. --- Database Management. --- Big Data/Analytics. --- Data sets, Large --- Large data sets --- Data sets --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- SQL (Computer program language) --- Structured Query Language (Computer program language) --- Declarative programming languages --- Query languages (Computer science)
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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.
Statistical science --- Information systems --- database management --- gegevensanalyse
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The database industry is clearly a multi-billion, world-wide, all-encompassing part of the software world. This is part thanks to the standardization of query languages in the form of SQL. And yet, it is well known that SQL has significant shortcomings. Quantifiers in Action: Generalized Quantification in Query, Logical and Natural Languages analyzes one of those shortcomings, the way in which quantification is dealt with in SQL. It is well known that most query languages are simply versions of First Order Logic (FOL). GQs are an extension of the idea of quantifier in FOL. Hence, GQs can be a meaningful extension of the treatment of quantification in query languages. Even though studied within the theoretical community up until now, GQs can be successfully applied, and are a perfect example of a practical theory within databases. This book provides a brief background in logic and introduces the concept of GQs, and then develops a query language based on GQs, called QLGQ. Using QLGQ, the reader explores the efficient implementation of the concept, always a primary consideration in databases. This professional book also includes several extensions, for use with documents employing question and answer techniques. Quantifiers in Action: Generalized Quantification in Query, Logical and Natural Languages is the result of several years of research funded by NSF through a CAREER Award. It is designed for practitioners and researchers that work within the database management field. This volume is also suitable for graduate-level students in computer science.
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Statistical science --- Information systems --- database management --- gegevensanalyse
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