You've probably heard about the top skills needed for Data Science. Do you have any ideas where to begin? SQL is the simplest and most important skill to learn.
Before you can develop this skill, you should understand the role of SQL in data science and why every Data Science expert considers SQL to be important for data scientists. So, let's look at how SQL is important in data science.
SQL is the querying language used by all relational databases. It is also the industry standard for current big data platforms that use SQL as their primary API for relational databases.
We will go over some of the key aspects of SQL and its applicability in the current Data Science scenario. Then, we'll go over the key SQL elements that are required for Data Science.
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SQL's Importance in Data Science
The study and analysis of data is known as data science. To analyse the data, we must first extract it from the database. This is where SQL comes into play. Relational Database Management is an essential component of Data Science.
While many modern industries have shifted to NoSQL for product management, SQL remains the best choice for many CRM, business intelligence, and in-office operations.
Many database platforms are based on SQL. This is due to the fact that it has become a standard for many database systems. In fact, modern big data systems such as Hadoop and Spark use SQL to maintain relational database systems and process structured data.
While Hadoop has batch SQL features, Impala and Apache Drill have interactive query capabilities.
What SQL Skills Do Data Scientists Need?
Aspiring Data Scientists must possess the following SQL skills:
1. Understanding of the Relational Database Model
A Relational Database Model System (RDBMS) is the most important and fundamental concept for any aspiring Data Scientist. To store structured data, you must be well-versed in RDBMS. SQL can then be used to access, retrieve, and manipulate the data.
2. Understanding of SQL commands
A Data Scientist must be familiar with the following SQL commands:
- Data Manipulation
- Data Query Language
- Language Information Definition
- Control Language for Language Data
3. No Value
A missing value is represented by the value null. In a table, a field with a Null value is empty. A Null value, on the other hand, is not the same as a zero value or a field with blank spaces.
4. Indexes
A database search engine can easily locate values in a row by using special lookup tables. We can quickly load data into the database using SQL indexing.
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5. Joins
Table joins are the most important relational database concepts that a data scientist must understand. There are two kinds of joins: inner and outer joins. They are further subdivided into Inner, Left, Right, Full, and so on.