A good Data Scientist has the skills to retrieve and work with data. Becoming well-versed in SQL will help in that case. Let’s start by going through the following topics to get familiarized with SQL for Data Science:
Everyone is busy picking up Python and R for Data Science, but without the knowledge of databases, trying to work in Data Science could soon become a huge setback. So, before getting into SQL for Data Science, you should understand how an RDBMS works.
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Basics of RDBMS
An RDBMS (relational database management system) is the root of SQL, as well as other database systems such as MS SQL Server, Oracle, MySQL, IBM Db2, and Microsoft Access. The relational model by E. F. Codd is the basis of the RDBMS.
Here, data is stored in the simplest form of data storage called tables, which is a set of related data entries and has numerous rows and columns. It is quite common to use a table for data storage. Every table is divided into fields that contain specific categories or information. A record or a row is the individual entry in a table, while columns are vertical entries under each field.
If there is an empty row or column under a field, it is referred to as a NULL value. It is left blank and should not be confused with a ‘0’ value or a field that has spaces in it.
SQL Constraints –
SQL constraints limit the type of data that is entered into a column of a table, aiming for accuracy and reliability of the data. These rules can be enforced either on columns or the whole table and referred to as column-level constraints or table-level constraints, respectively.
Following are a few popular constraints in SQL and their functions:
- NOT NULL: The column cannot have a NULL value.
- DEFAULT: It is the default value of a column when no other value is entered.
- CHECK Constraint: All values in a column should satisfy certain conditions.
- UNIQUE: All column entries should have different values.
- PRIMARY Key: Each row/record should have a unique value and no NULL values.
- FOREIGN Key: Also called a referencing key, it links two tables by matching the values of a column or columns with the primary key of a different table.
- INDEX: It is used to create and retrieve data from the database.
Data integrity ensures the accuracy and consistency of data. Following are the categories of data integrity in RDBMS:
- Entity integrity: No duplicate rows
- Domain integrity: Valid entries of a type, format, or range for a column
- Referential integrity: Cannot delete the rows used by other records
- User-defined integrity: Business rules that do not fall into the above three integrity types
Data normalization ensures an efficient organization of data in a database by eliminating redundant data and making sense out of data dependencies. This reduces the storage consumption by a database, and the data is stored logically.
Normalization guidelines come in different formats that lay a database structure for the purpose of the data organization, and they are called normal forms. The database structure should comply with the first normal form (1NF), then with the second normal form (2NF), and in the end, with the third normal form (3NF). One can choose to go further to the fourth and fifth normal forms, and so on. However, going up to the third normal form is more than enough.
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Structured Query Language (SQL): An Overview
Our SQL Tutorial discusses it in detail, but to learn SQL basics for Data Science, let’s revisit the definition of the Structured Query Language or SQL. In simple words, SQL is used to operate or communicate with relational databases. The terms RDBMS and SQL are, however, conflated mostly for convenience. This ANSI standard language comes with various versions as well.
SQL functions as a means to create databases and fetch, modify, update, and delete rows. It also takes care of storing data, data manipulation, and retrieval of the data.
The Data Science Life Cycle and How SQL Comes into Play
Is SQL required for Data Science? To answer this question, first, you will have to understand how Data Science works. You can read all about it from our previous blogs. For now, you can focus on the Data Science life cycle, which has three distinct phases. Throughout these phases, you can see how one can use SQL for Data Science.
Data Conditioning and Cleaning
After the accumulation of data is done from all available sources, the data has to be efficiently organized and structured to eliminate redundancy and duplication for carrying out data modeling. This is the DBMS normalization that we discussed earlier.
Due to its concept of constraints and the primary key, structuring and conditioning can be executed effectively using SQL. Through SQL, a proper relationship can be set up between different data types.
The importance of SQL in Data Science is also evident in the use of SQL’s automatic batch scripts in data cleaning. This way, all irrelevant and corrupt entries can be cleaned out from time to time without the need for manual intervention. While R can also be used to achieve the same, using it in conjunction with SQL can considerably reduce the amount of code and add more flexibility.
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Data Modeling and Mining
Big Data is, in fact, an enormous block of unstructured data that can only be saved by a DBMS and SQL. Big Data is handled by data modeling, and this process is another example of SQL in Data Science.
Let’s say, a student registers for an exam. His exam_ID, phone_number, and email ID are unique and can be used as a common key. The exam_ID will be a unique value and can be used to establish relationships through phone_number or email_ID.
Data mining is another process where you can witness the use of SQL for Data Science. Data mining involves the analysis and identification of trends or patterns in huge datasets. Undoubtedly, organized and structured datasets make the process easier.
SQL has the ability to return particular records for a specific query. Indexing can be done in the case of complex queries to return results faster. A number of built-in SQL functions are available, such as listing data in the ascending order, grouping in a certain order, etc., which are very convenient and useful in Data Science.
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You might think that an Excel sheet can also execute filtering operations on large datasets and fetch results. It is, however, not at par with the scale at which SQL can handle data. There is no definitive answer to how much you can use SQL for Data Science. This section of the blog will attempt to give you a glimpse into it.
To understand the use of SQL for Data Science from a business perspective, you will need to be familiar with the schema, relationships, and queries that are used to get accurate results. SELECT is one of the most-used commands to fetch data from a table.
During data analysis, the data is examined at a more complete and comprehensive level. The data is then grouped (GROUP BY) at different levels, and once a level is chosen for analysis, the select statement is divided into dimensions and metrics.
HAVING is another functionality that can be used after grouping to add another layer of data filter. Most times, it is used to clean the output.
Microsoft has SSAS for data analysis across multiple databases. This tool can be used for modeling, integration, reporting, and analysis.
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This blog is only touching the surface to give you an idea of how integral SQL is for Data Science. Irrespective of the many sub-domains in Data Science, SQL for Data Science still holds a strong position and is one of the most requested skills from Data Science aspirants. The best way to learn SQL for Data Science is to enroll in and complete a certification course that offers real-time project work, which will validate your skills and proficiency and will provide you with hands-on experience.