Last updated on January 20th, 2024 at 05:49 am
Learning SQL or Standard Query Language is a mandate for anyone looking to build a career in data science. It is used for interacting with and extracting data from relational databases. Most modern systems today capture data stored in one or multiple databases like Oracle, MySQL, SQL Server and Redshift. Hence, it is important to have an in-depth understanding of SQL to glean data from these systems and use them efficiently.
Apart from writing queries and handling data, it aids in communicating with people, visualising results and building models. It is also an essential element in machine learning. Despite being a powerful tool, it is easy to learn, easily shareable, familiar and relevant worldwide.
What is SQL?
SQL or Standard Query Language is a declarative language for controlling and acquiring data. Data scientists use it to develop, decipher, control (insert, update, delete) and combine tables. Furthermore, it is used for filtered results with ORDER BY statements, WHERE clauses and the like.
SQL helps data scientists access data and work directly with a database without using a different programming language. It makes running complex queries easier because one can do so with SQL syntax and without writing code, making extracting anything from a database easy.
5 Reasons Why SQL is Important in the Field of Data Science
SQL is essential for Relational Database Management which plays a major part in data science. Here are 5 main reasons why SQL is important in data science:
1. It is a powerful language
SQL programming is used for manipulating data, creating new tables, inserting data into tables and retrieving results of queries. SQL syntax is similar to the SQL programming language, which makes it easy to learn. Developers familiar with Standard Query Language also find it easier to learn Python objects and programming. With SQL, it is possible to:-
- Query the database and acquire results comprehensibly without manually going through every row with tools like R scripts or Excel.
- Quickly acquire the necessary answers you need without the need to write code or try multiple algorithms.
2. It is globally recognised
Being familiar with data science tools like R, Spark and Python makes it easier to learn SQL. More importantly, it is a mandatory skill recognised globally to help manipulate and interact with data stored in databases. Knowing how to write queries in SQL can be used in all database applications and tools without any in-depth knowledge of statistics.
3. SQL is sharable
SQL is also widely used for sharing data and helping data scientists communicate with other non-technical members of an organisation who might require the same information. For instance, if the marketing team of a company requires understandable information from a raw dataset, then it is the duty of the data scientist to glean, process, clean and provide it. This helps enhance flexibility and work efficiency among teams.
4. It is a common tool
Data experts and business users widely use SQL for querying databases like data lakes and warehouses. Aside from being another tool that helps access Spark and Hadoop, it is also used by primary data analysis tools like Tableau to query relational databases.
5. It is relevant
SQL is commonly used in multiple data science tasks like:-
- Exploring data and understanding it better
- Cleaning up data
- Prepare data for analysis
- Building models on the prepared data set
- Visualising results and reporting on them.
Why is Learning SQL a Mandate for Becoming a Data Scientist?
We have consolidated a list of reasons why learning SQL is a mandate for pursuing a career in data science:-
- It helps handle structured data: SQL is required to work with structured data stowed in relational databases and raise a query in said databases.
- Big data platforms provide useful extensions: Platforms like Hadoop offer extensions for raising SQL command queries to manipulate data efficiently in HiveQL.
- It helps experiment with data: SQL is a standard tool that provides data scientists with the opportunity to experiment with data by creating test environments.
- It helps in analysing data: SQL skills are integral in data analytics. It helps work with data stored in relational databases such as MySQL, Microsoft SQL and Oracle.
- Helps in preparing data: SQL helps in data wrangling, which removes errors and combines complicated datasets. It is essential for working with numerous big data tools because it aids in preparing data and making it more accessible.
Conclusion
SQL skills are mandatory for data scientists. It helps comprehend data efficiently to facilitate effective decision-making. Therefore, a career in data science is the most lucrative choice you can opt for because of the golden opportunities that lie in its wake.
Large corporations are constantly looking for data scientists who can glean and analyse data from large data sets to help make better decisions that will facilitate the ultimate growth of an organisation. To upskill yourself and further your career in this field, you can sign up for the comprehensive Postgraduate Program in Data Science and Analytics offered by Imarticus Learning.
