“Data Science” was a term first used 1960s. It was used to name a job that dealt with making sense of a lot of data. Originally, Initially, it was applied in areas such as astronomy and medicine, but it quickly became relevant in the business sector, assisting in more intelligent decision-making. Over time, Data Science has developed, integrating computer science statistical methods, and various sub-disciplines to derive significant predictions and insights.
In recent years, Data Science has witnessed remarkable growth and adoption in recent years. Nowadays, Data Science is the Most In-Demand Profession In the field of IT. Currently, a majority of companies are moving towards a data-centric approach. This data is stored in a database and managed and processed through a Database Management System (DBMS), which simplifies and organizes our work.
This is where the Structured Query Language (SQL) becomes important. SQL is a fundamental tool in data management used in DBMS. It plays a vital role in the data science workflow, enabling professionals to extract valuable insights from large and intricate datasets.
SQL is a standard database language used to communicate with databases more briefly it is used to access and manipulate database data. SQL stands for Structured Query Language. It was developed by IBM Computer Scientists in the 1970s. By executing queries, SQL can create, update, delete, and retrieve data in databases like MySQL, Oracle, PostgreSQL, etc.
In this article, we will go through the complete curriculum of SQL that a Data Science student or professional should learn to excel in this field.
Need of SQL in Data Science
SQL (Structured Query Language) is used to manipulate data. By performing different operations on the data stored in databases, such as updating, removing, creating and altering tables, views, etc. Using SQL as the primary API for relational databases by big data platforms and organisations is standard.
Data science is the study of data in its entirety. We must extract data from the database in order to work with it. SQL enters the picture in this situation. A key component of data science is relational database management. A data scientist can define, define, create, and query the database using SQL commands. Many different industries and organisations have used NoSQL to manage their product data, yet SQL is still the best choice for many.
Elements of SQL for Data Science
Following are the key points of SQL that are most helpful for Data Science. Every Data Scientist must know these necessary SQL skills and features.
- Relational Database Model
- SQL Query Commands
- Handling Null Values
- Key Constraints
- Working with SubQuery
- Creating Tables and Databases
SQL For Data Science Page Index
Introduction to SQL:
Setting Up the Environment:
Working with Data:
Connecting SQL with Python
Important topics of SQL in Data Science that you need to learn
FAQs on SQL for Data Science
Q1. Is SQL for Data Science best ?
SQL is a very useful tool for the Data Science, using SQL databases for the database management it makes it easier for the user to see the code in a more organized and clean form. It can be one of the best tool for the management of databases in Data Science.
Q2. Is SQL better than Python ?
SQL is more faster than the Python for simple queries as SQLs databases have a well defined schema already embedded in it and also the data used at the computation level is also well defined in the SQL.
Q3. What is the salary of SQL developer in India ?
In general , salary of SQL developer in India ranges between 2.0 lakhs to 8.0 lakhs, average is 4.0 lakhs.
Q4. Is SQL easier than coding ?
Yes, SQL is easier than the general purpose coding languages as it is narrower domain than coding. SQL comprises of queries, data management while coding includes all the programming languages, their synatxes which it self a big thing to learn.
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