Are you looking to make your career in Data Science? That’s a great idea. But, do you know Data Science is not as easy as you think? There are several concepts and programming languages you have to study and understand. Also, more importantly, you must know the difference between them. Then, you are capable of using accurate concepts and programming language for particular tasks. That is why we are here discussing Python vs SQL.
Well, Python and SQL both are vital programming languages in Data Science. Students must understand these two languages well. Moreover, they also must know the main difference between them. So, if you are here to learn Python vs SQL, you are at an accurate place. So, keep reading this blog, to know about both programming languages. Let’s begin with a little overview of Python and SQL.
However, the main difference between Python and SQL is that data scientists use SQL to collect, edit, and extract data from databases. Whereas, Python is a general-purpose programming language. It enables data analysis and helps in creating mobile apps, numerous online apps, artificial intelligence, etc.
However, in this battle of Python vs SQL, let’s learn both languages in detail.
Python is a general-purpose programming language. It means we can use it for a wide range of tasks. For example, Back-end development, software development, and developing system scripts. Moreover, Data Scientists usually use Python because of its simple syntax and widespread use in the industry. This makes Python easier to work with while creating data analysis tools.
In addition, Python has become one of the most popular languages for data exploration. Because it is capable of functioning on a variety of platforms. Also, it focuses on readability. Moreover, there is a wide range of sectors that use Python-based software, apps, and programs because of its flexibility.
Moreover, there are various applications for Python. They are;
- General Web Development
- Data Analysis
- Machine Learning
Another name for SQL programming languages is Structured Query Language. It allows programmers to manage and collect data from databases. Also, it designs its own databases.
However, many businesses store information in relational databases by using tables, columns, and rows. Therefore, to create and maintain these databases, businesses use SQL.
Moreover, developers use SQL to provide rapid data insights, execute data studies, and extract records from large databases. Webpages, apps, and business software packages use data saved in databases. Thus, SQL developers deal with a variety of databases, including:
- Music Software
- Banking Databases
- Social Media Applications
So, now let’s learn the key features of both languages.
Python vs SQL: Key Features
The following are some of the key features of the Python programming language:
- Simple to understand and use
- Python is a high-performance interpreted programming language.
- It is free and open-source.
- Adaptable to a variety of programming languages and technologies
- Cross-Platform Programing Language
- Standard Library
- Fast compilation time
- Python helps in creating graphical user interfaces (GUIs).
Whereas, the features of SQL programming language are as follows;
- It is simple to understand.
- SQL helps in assessing data from RDMS.
- It helps in defining the data.
- It can query the database.
- SQL helps in creating the table and database.
- It helps in figuring out data in the database and making changes as needed.
- In a database, SQL defines views, stored procedures, and functions.
- It allows users to provide permissions to tables, procedures, and views.
Now, let’s discuss the benefits and drawbacks of Python and SQL.
Python vs SQL: Benefits and Drawbacks
Benefits and Drawbacks Of Python
Simple to learn, write and read.
Supports LibrariesImproved productivity
Free and Open SourceDynamically Typed
Runtime ErrorNot memory efficient
Bad in mobile computing
Benefits and Drawbacks of SQL
|Multiple data views|
Faster query processing
As we have discussed the benefits and drawbacks of both programming languages. Now move to the key difference between Python and SQL.
Python vs SQL: Key Points Of Difference
Python is a high-level programming language. Data scientists use Python for data analytics, web development, prototyping, and other technical activities. Moreover, this language combines a high degree of data structure and dynamic typing to speed up the application development process.
On the other hand, SQL is an open-source relational database management system. Anybody can use SQL. From a complete novice to a skilled data scientist working on a project, may download and use.
Python has numerous libraries available. But, SQL has no library.
Python is compatible with all websites available on the internet. Whereas, SQL is compatible with both mobile and desktop apps.
So, to use Python, students must have a bachelor’s degree in computer engineering or software engineering. Also, learn the principles of other programming languages. Whereas, SQL is suitable for people with a bachelor’s degree in computer information systems, computer engineering, or any other IT-related major. For example, B.E./B. Tech/MCA.
Students who complete the Python certification course will be able to pursue a range of careers. The careers include;
- Python Developer
- Product Manager
- Data Analyst
- Machine Learning Engineer
On the other hand, the career opportunities SQL provides are;
- Business Intelligence
- SQL Server Database Administration and Development
- Data Science
Python and SQL: Which One Is Best
Well, this is another point in the battle of Python vs SQL. It makes no difference whether you are using SQL or Python. Also, every programming language has its own advantages and disadvantages.
However, SQL helps in querying and extracting data. The ability to combine data from many tables inside a database is one of the most powerful capabilities of SQL. But, SQL cannot do higher-level data manipulations and transformations. For example, regression testing and time series.
On the other hand, Pandas is a Python-specific library. It makes data analysis easier. So, you can extract data using SQL and make changes in the structured data using Python.
However, we have discussed Python vs SQL, in the above blog. We have learned important information about both programming languages. However, Data Science Students need to learn both languages and know where to use a particular language. So, now I hope you can now understand the difference and be able to select which one is suitable for a particular task.