“Is Python programming really necessary for data science?” is a question that revolves in our minds again and again whenever we try to think about switching/creating our career in IT. One thing that we must be clear about is that Data science is a branch of IT too. To create a career in the IT industry, one needs to know any one programming language.

But the question remains, Why?

Data science is not a technology that has evolved in the last few months. Companies have been analyzing their data regularly for a long time. The tool they were using was Excel. Excel is an application software, designed by Microsoft to store and analyze data but the problem began in the last 5-6 years when the market experienced a data explosion. When Reliance launched Jio and provided free internet for a year, people started using the internet all the time and it started to generate data at a huge speed. Now the amount of data that was getting generated was unable to be handled by Excel as it has its limitations.

Now, with no other options left, companies were left with the only option of using tools and technologies of data science and the foundation of these tools and technologies is a programming language. As a foundation, programming language plays an important role in anyone’s understanding and upskilling in this field but the importance of programming language is not limited to the foundation only.

Apart from learning, python programming also plays an important role in the regular uses of data science tools and technologies, which are:

1. Ability to Handle Large Sets of Data with programming language:

The first problem that the organizations encountered was as the data was growing the inability of Excel to handle large data also grew. With the help of powerful libraries like Pandas, python can handle large amounts of data as compared to Excel easily. Pandas can handle data from any data format while excel can handle data in rows and columns only.

Excel has 1,048,576 rows and 16,384 columns but after a few lakh rows, excel starts hanging and starts taking time to perform tasks. In the case of large Excel files, it will take a few minutes just open the file.

2. Automation with programming in Python

Automation is a feature that helps us to create an environment where a set of tasks performs again and again automatically. This feature helps the organization in the tasks that need to be performed regularly. It becomes easier to do so with the Python libraries like selenium, beautifulSoup, requests, and many more

Examples: daily sales report, daily share market report, daily inventory/production report, etc.

3. Rich Collection of Libraries

Programming in Python is becoming the first choice for every programmer because of the abundance of libraries available in python. Python has around lakhs of libraries serving almost every domain with its inbuilt functions and still continues for improvements

If you wish to learn more about Data Science and have an in-depth knowledge of this field.
Read this blog
What Is Data Science In Simple Words?

4. Scalable

If you are working on a tool, you will have to follow the limitations of that tool in terms of data, in terms of capabilities, in terms of features, etc. But with the help of programming language, you do not stay in a particular boundary. You decide the boundary. You can minimize or maximize the features of your program as per your requirements.

5. Customization and Connectivity

Using programming language in data science enables a programmer anytime customization as per self or client’s requirements which can improve the productivity and costings of the processes involved.
Programming language allows the programmer to create connectivity with other platforms as well as the database with minimal changes.

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