Date article was published: June 28, 2019
This article stresses the importance of knowing SQL for data scientists. SQL helps data scientists with an understanding of relational databases and is one of the most important languages in data science.
## [1] "Easy to Learn and Use" "Understanding your Dataset"
## [3] "Integrates with Scripting Languages" "Manage huge volumes of data"
## [5] "A Gateway to Data Science Jobs"
When trying to find a job in data science, one would wonder what the most important skills are. A foundation in Python and R sure, but a language that connects data and databases together is important. That language is SQL. SQL uses simple structure and English words for its commands and functions. SQL integrates with Python and R and it can also work with huge amounts of data. The ability to store, update, access control, and manipulate datasets are highly marketable skills. In conclusion, SQL is a must know language for data scientists.
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SQL stands for “Structured Query Language” and it is a programming language that is used to create, maintain, and retrieve relational databases. It is critical in accessing, updating, inserting, manipulating and modifying data. It was started in the 1970s.
## [1] "computer software" "health" "manufacturing"
## [4] "transport" "banking"
SQL can be used in many other database management systems. This is beneficial because it allows flexibility.
## [1] "MySQL" "Microsoft SQL Server" "Oracle Database"
## [4] "dBase"
I agree that SQL is an important skill for a data scientist to know since it develops understanding of relational databases. Since SQL can be integrated with Python and R, it is an important tool to have. This means that when someone is working in Python or R, they will be able to interact with the database in the environment. Being able to work with huge datasets is a marketable skill for any company since data is the new gold. I found it useful that the article showed how important SQL is to data scientists. This will make me want to build a strong familiarity with SQL. Basically, it showed how knowing SQL can give you an edge.
orange_plot <- ggplot(Orange, aes(x=age, y=circumference, color = Tree)
) +
geom_point()
print(orange_plot + ggtitle("Relationship Between Circumference of Orange Trees and Age"))
header <- "Relationship between Chicken Weights and Feed"
chicken_data <- ggplot(chickwts, aes(x=feed, y=weight, color = feed)
) +
geom_point()
print(chicken_data + ggtitle("Relationship Between Chicken Weights and Feed"))
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