As a career, data science represents a world of opportunities for professionals in India. According to reports, by 2026, India’s big data market will account for 32% of the global figure and generate a revenue of $20 billion. This means that in-demand skills such as SQL for data science, among many others, will be needed by individuals to leverage the growth of this industry.
Structured Query Language, or SQL, is a querying language for managing relational databases. It is useful for performing several actions on data. For example, inserting, querying, deleting, and updating database records. Read through this article to understand more about the importance of SQL for data science and the best practices for using this skill.
Use of SQL in Data Science
Here are some of the applications of SQL for data science:
1. Data Extraction
Data scientists often employ SQL programming for extracting data from a database and, thereafter, analyzing it. SQL queries are also beneficial for filtering and selecting data according to criteria such as time, location, and other variables.
2. Data Aggregation
SQL is useful for calculating summary statistics, grouping data into specific categories like age and gender, and measuring averages like gross domestic income.
3. Data Transformation
It can help clean and transform data and create new views or tables that get analyzed easily. Additionally, SQL can be utilized for merging or joining tables and calculating new variables.
4. Data Exploration
SQL for data science professionals is crucial for identifying patterns and relationships between different variables. That apart, SQL queries enable them to filter data and evaluate different metrics for further exploratory data analysis.
5. Data Visualization
Another use is that SQL can help create views and tables that are visible. This is done with the help of data visualization tools like Power BI and Tableau.
Importance of SQL for Data Science Professionals
Data science revolves around studying data after extracting it from the database. And that is where SQL comes into play—in the extraction process. Furthermore, data scientists use SQL commands to control, define, manipulate, create, and query the database.
Several modern industries use NoSQL technology for their product data management. But SQL remains the top choice for several in-office operations and business intelligence tools. Since several database platforms are modeled after SQL, the programming language has become the standard for a majority of database systems.
Modern big data systems like Spark and Hadoop also use SQL for relational database management and processing structured data. Moreover, SQL for data science is also quite essential for data wrangling and preparation.
Different Types of Queries in SQL
The different types of queries in SQL for data science are as follows:
1. Select Query
The select query is the least complex and is frequently used in Microsoft Access databases. It also finds application in selecting and displaying information from a table or a progression according to the requirement. The select query creates a virtual table where information can be modified.
2. Action Query
Action query can change multiple records simultaneously rather than just single records in a select query. The different types of action queries are as follows:
Append Query: It adds the set consequences of a query into a current table
Delete Query: It removes all records in a hidden table from the outcomes of a query
Make Table Query: It creates a table according to the set consequences of a query
Update Query: It is useful for refreshing fields in a table
3. Aggregate Query
The aggregate query is useful for summing up any chosen property in a table. You can further divide the summation into measurable ‘sums’ like standard deviation and midpoints. The different types of SQL aggregate functions available in Microsoft Access are as follows:
Sum
Min
First
Group By
Avg
Max
Last
4. Parameter Query
A parameter query can work with different types of queries to deliver what you want. While using
a parameter query, you can give a command to pass a parameter to another query, like a ‘select or activity query.’ It will clearly tell the other query what you require it to do.
The parameter query always considers an exchange box where the end client can enter any parameter value. The parameter query can be considered an altered select query.
Best Practices for Using SQL in Data Science Projects
1. Use Particular Column Names in Select
The ‘select query’ is useful for retrieving data from a particular table in a database. However, it can also be an expensive method when you need to retrieve data from a large database with multiple rows containing a huge amount of data.
A dataset’s columns are not usually useful for one particular task. Therefore, you can always specify column names in the ‘Select statement’ to make it less expensive and faster.
2. Prioritize SQL JOINs Over WHERE
The ‘JOIN’ clause is useful for combining rows from multiple tables with a related column between them. But the ‘WHERE’ clause helps while choosing rows according to a condition mentioned in it.
At times, data scientists use the ‘WHERE’ clause to select rows from two tables when specific columns and their values are found in both of them. In such cases, the ‘WHERE’ clause can lead to poor readability and confusion. Therefore, data scientists should always choose the ‘JOIN’ command over ‘WHERE’.
3. Use HAVING and WHERE Judiciously
Both ‘WHERE’ and ‘HAVING’ are useful for filtering data logically. But there’s little difference in their mechanisms. The ‘WHERE’ clause is for selecting records according to the conditions within them. The ‘HAVING’ clause works for selecting records from groups according to aggregations of one column or multiple columns.
The two clauses are sometimes used interchangeably, which is a bad practice. You should execute ‘WHERE’ before ‘GROUP BY’ in a query. Meanwhile, you should execute ‘HAVING’ after the ‘GROUP BY clause.’