R is incredibly popular within the data science industry, as reflected by its rapidly growing community. With its rich repository of packages numbering in the thousands, R has a package for virtually any data-related task. In this article, we’ll introduce you to some of the best R packages for data science.
A characteristic of persistent storage — including the relational databases upon which SQL operates — is the ability to create, read, update, and delete information; also known as “CRUD.” The CRUD actions map to these SQL operators:
This article explores SQL Update, the ability to change previously-stored database values.
While a single data point doesn’t have much significance, a collection of data may reveal trends and patterns. Those with the ability to read data can make sense of the past and predict the future. That, in short, is the job of data scientists. Equipped with a diverse set of skills, these professionals find and tell stories hidden in data. In this article, we’ll show you how to learn data science.
Users of relational databases want all the data they need at their fingertips, while the database designers want to responsibly “decompose” the data into the smallest possible and most efficient tables. There’s always a tension between ease and efficiency at scale.
SQL Join satisfyingly bridges this tension, giving you the power to derive tables that contain exactly the data you need.
SQL Join generates a new (temporary or permanent) table from one or more tables by combining columns based on the way the Join is invoked. There’s also a “self-join” variant to groom an unruly table into something more manageable.
Data is the new black gold. Thanks to the rise in data-driven decision making, the demand for data professionals is outpacing the supply — and data architecture is one of the emerging jobs in this field.