SQL for Data Science

Start Date: 11/17/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/sql-for-data-science

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Course Syllabus

In this module, you will be able to define SQL and discuss how SQL differs from other computer languages. You will be able to compare and contrast the roles of a database administrator and a data scientist, and explain the differences between one-to-one, one-to-many, and many-to-many relationships with databases. You will be able to use the SELECT statement and talk about some basic syntax rules. You will be able to add comments in your code and synthesize its importance.

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Course Introduction

As data collection has increased exponentially, so has the need for people skilled at using and inte

Course Tag

Data Science Data Analysis Sqlite SQL

Related Wiki Topic

Article Example
Azure SQL Data Warehouse SQL Data Warehouse integrates with data tools such as Azure Machine Learning for advanced analytics, Azure Data Factory for data orchestration, Azure Stream Analytics for real-time analytics, and Power BI for data visualization. Standard query tools like SQL Management Studio (SSMS), SQLCMD, and SQL Server Data Tools can also be used to execute queries.
SQL/MED The SQL/MED, or "Management of External Data", extension to the SQL standard is defined by ISO/IEC 9075-9:2008 (originally defined for SQL:2003). SQL/MED provides extensions to SQL that define foreign-data wrappers and datalink types to allow SQL to manage external data. External data is data that is accessible to, but not managed by, an SQL-based DBMS. This standard can be used in the development of federated database systems.
Data science The term "data science" (originally used interchangeably with "datalogy") has existed for over thirty years and was used initially as a substitute for computer science by Peter Naur in 1960. In 1974, Naur published "Concise Survey of Computer Methods", which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.
Data science In 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched, and the first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science". In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics. 2013 the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg establishing the European Association for Data Science (EuADS) in August 2015. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK.
Data science he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists.
SQL Originally based upon relational algebra and tuple relational calculus, SQL consists of a data definition language, data manipulation language, and data control language. The scope of SQL includes data insert, query, update and delete, schema creation and modification, and data access control. Although SQL is often described as, and to a great extent is, a declarative language (4GL), it also includes procedural elements.
Microsoft SQL Server SQL Native Client is the native client side data access library for Microsoft SQL Server, version 2005 onwards. It natively implements support for the SQL Server features including the Tabular Data Stream implementation, support for mirrored SQL Server databases, full support for all data types supported by SQL Server, asynchronous operations, query notifications, encryption support, as well as receiving multiple result sets in a single database session. SQL Native Client is used under the hood by SQL Server plug-ins for other data access technologies, including ADO or OLE DB. The SQL Native Client can also be directly used, bypassing the generic data access layers.
Data science In April 2002, the International Council for Science: Committee on Data for Science and Technology (CODATA) started the "Data Science Journal", a publication focused on issues such as the description of data systems, their publication on the internet, applications and legal issues. Shortly thereafter, in January 2003, Columbia University began publishing "The Journal of Data Science", which provided a platform for all data workers to present their views and exchange ideas. The journal was largely devoted to the application of statistical methods and quantitative research. In 2005, The National Science Board published "Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century" defining data scientists as "the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection" whose primary activity is to "conduct creative inquiry and analysis."
Azure SQL Data Warehouse By combining MPP architecture and Azure storage capabilities, SQL Data Warehouse can:
Data science Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
Azure SQL Data Warehouse Azure SQL Data Warehouse is a cloud-based data warehouse-as-a-service hosted within Microsoft’s Azure platform. It has a massively parallel processing (MPP) shared nothing architecture capable of distributing query computation over a set of compute nodes running Azure SQL Database and uses Azure Storage Blobs as the underlying data storage.
Azure SQL Data Warehouse Allocation of resources to SQL Data Warehouse is measured in Data Warehouse Units (DWUs). DWUs are a measure of underlying resources like CPU, memory, IOPS, which are allocated to your SQL Data Warehouse. Increasing the number of DWUs increases resources and performance. Specifically, DWUs help ensure that:
Azure SQL Data Warehouse Azure SQL Data Warehouse, is an elastic cloud data warehousing service that allows pausing and scaling on demand. It creates a SQL-based view across all your data which enables business insights. Pricing in January 2017 is approximately 1/10th of the cost of traditional appliance solutions.
Data science In 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to incorporate "advances in computing with data" in his article "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics," which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / Revue Internationale de Statistique. In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.
Microsoft SQL Server Master Data Services Microsoft SQL Server Master Data Services is a Master Data Management (MDM) product from Microsoft that ships as a part of the Microsoft SQL Server relational database management system. Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) enables your organization to discover and define non-transactional lists of data, and compile maintainable, reliable master lists. Master Data Services first shipped with Microsoft SQL Server 2008 R2. Microsoft SQL Server 2016 includes many enhancements to Master Data Services, such as improved performance and security, and the ability to clear transaction logs, create custom indexes, share entity data between different models, and support for many-to-many relationships. For more information, see What's New in Master Data Services (MDS)
SQL SQL ( or , Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS).
Data science Although use of the term "data science" has exploded in business environments, many academics and journalists see no distinction between data science and statistics. Writing in Forbes, Gil Press argues that data science is a buzzword without a clear definition and has simply replaced “business analytics” in contexts such as graduate degree programs. In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, “I think data-scientist is a sexed up term for a statistician...Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”
Data science In 1996, members of the International Federation of Classification Societies (IFCS) met in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference ("Data Science, classification, and related methods"), after the term was introduced in a roundtable discussion by Chikio Hayashi.
SQL The Data Manipulation Language (DML) is the subset of SQL used to add, update and delete data:
Data science Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD).