Learn SQL For Data Science

Data Science is a very active domain with numerous work opportunities worldwide in various industries and organisations. To be good at Data Science, one has to have some fantastic technical skills to perform their job. However, the easiest yet most important skill for any data science aspirant or professional is Structured Query Language (SQL).

Most companies these days are shifting towards a data-driven approach. The data is stored in databases and is then processed and managed using a Database Management System (DBMS). The DBMS helps in organising the data and making its processing easier. For working on databases, SQL is the most widely used programming language. It is used by many relational database management systems such as SQL Server, MySQL, SQLite, and Oracle. However, the standard features of SQL are implemented to varying degrees in different database systems. It makes SQL one of the most important technical skills to be acquired for success in the field of Data Science.

Why do you need to learn about SQL for Data Science?

It is widely seen that many technical job opportunities in big organisations list SQL as one of the most critical required skills to apply. While it is an underappreciated programming language, it is among the top skills required worldwide. SQL will remain an essential part of Data Science as long as there is “data” in data science. The relevance of SQL in the 21st century is highly evident as it is still the most widely used programming language for database management systems, even after 40 years of its development.

Role of SQL in Data Science

Data Science refers to the study and analysis of data sets. The data present in the data sets have to be extracted from the database, which is done using SQL. Many well-known and highly used database platforms are modeled after SQL, which has become a standard across the industry. Big Data systems such as Spark and Hadoop also use SQL to process structured data and manage relational database systems.

The fundamental steps in any predictive or descriptive analysis work are identifying suitable data sources, sourcing the data, and preprocessing the data. This data is mainly stored in relational databases, and SQL is used to query these databases. Therefore, data scientists use SQL as the standard tool for data analysis on relational databases.

Essential Features of SQL for Data Science?

Below are some of the most important topics that one should learn in SQL for Data Science, but remember that these are not the limit.

• Group by Clause: The “GROUP BY” clause in SQL is used in conjunction with the “SELECT” statement to arrange similar data into groups. The group clause uses aggregation functions and the “Having clause” to apply conditions.
• String Functions and Operations: These perform operations such as matching a regular expression, converting string to uppercase, etc.
• Aggregation Functions: Performing calculations of value sets to return a single value such as Min, Max, Avg, Count, etc.
• Output Control Statements: To get results based on requirements such as a limit function to get limited rows.
• Viewing & Indexing: Indexes are unique lookup tables used by database search engines to speed up data retrieval. It can be considered similar to the index of a book.
• Date & Time Operations: While only the date value is easy to operate on, it becomes complicated when the time portion also gets involved. Hence, it is essential to practice enough.
• Operators: There are three types of operators used in SQL - Arithmetic, Comparison, and Logical.
• Nested Queries: A nested query, or subquery, returns data to the main query as a condition to restrict the final data retrieved.
• Joins: It is a crucial topic in SQL and is used to join multiple tables to generate the desired output. Concepts include types of joins, primary key, composite key, foreign key, etc.
• Windowing Functions: These are used to operate on row sets and for each row, return a single value from the underlying query. It helps reduce the complexity of questions required to analyse partitions of a data set.
• Temporary Tables: This feature allows for the storage and processing of intermediate results using the same selection, update, and join methods.
• Query Optimization: It becomes essential to use the most efficient SQL statement to access the requested data while working on large datasets.
• Common Table Expression: A temporary named result set generated using the SELECT statement that can subsequently be used in another SELECT view. It can be understood as similar to a name query with the result stored in a virtual table that can be later referenced for the main query.

Data Science

Certification Course

Pay After Placement Program

View course

SQL Resources to brush up your skills

Below are some great platforms to practice SQL Queries for interview preparation:

• SQL Zoo: One of the oldest and well-established online platforms for running SQL queries against a live database. The query result can be viewed without having to check query matches as there are many ways to solve a particular problem.
• Leetcode: It is one of the best platforms to practice with question varieties.
• Select * SQL: It is an online interactive book and aims to be the best platform on the internet for SQL education and learning. It is free for users and doesn’t require registration or downloads.
• SQL Bolt: Is is also a series of interactive exercises and lessons that allow easy learning of SQL. The lessons and topics on this site are comprehensive, and all important details are covered.
• Mode: Practice can be done based on segregated topics divided into four sections: Basics, Intermediate, Advanced, and SQL Analytical Training. Learners can read theory material and even practice SQL queries.

How to Learn SQL for Data Science

• Understand database concepts and the types of databases. Get insights into Relational Databases to learn SQL.
• The next step is to get an overview of SQL - what it is, why it is important for getting into data science, and the important topics to learn.
• Focus on topics such as Inserting, Selecting, and Updating columns in data tables.
• Learn how to create your own data table and how to delete it.
• Next, learn about Joins and Views.
• Once you have practiced Joins, move to Advanced SQL, which includes Triggers, Hierarchical Queries, etc.
• Finally, explore Query Optimization, which includes using Profiling, Explain Plains, etc.

Conclusion

SQL is a must-know technology if you want to make a career in Data Science.  It is the foundation for Data Science as it is used to process data sets and retrieve information from them. To learn SQL for Data Science, you should head over and explore our Data Science Course specifically curated for aspiring data scientists and working professionals.