Why Python Is Considered the Go-to Language for Data Science?

ElevatEd
Coding
July 12, 2024

Do you know what is the most trending topic in technology these days? You guessed it right. It is data science. Data science has been growing and gaining popularity at a terrific pace and every tech-savvy person is crazy about learning data science.

With the exponential growth of data science, the need for a reliable programming language is also growing. Amidst all the programming languages python wins this race too. Due to its simple syntax, and the availability of libraries and resources python has become the go-to language for data science. Let’s explore more about Python to understand why Python is getting so much attention from programmers.

What is Python?

Python is a high-level object-oriented interpreted programming language. An interpreted language is run line by line without being compiled into machine code.

It is one of the easiest and beginner-friendly languages with very easy syntax. Python is widely used in different companies and its demand is getting higher at a rapid pace.

The main benefits of Python are:

Dynamically typed
Easy to read and open-source
Versatile and huge library
Large community support

What Is Data Science Programming?

Data science programming refers to the use of programming with statistics to study and analyze data and extract insights from it for decision-making. The steps of data science are:

Data extraction and cleaning
Analysis of data
Visualization of data and making decisions.

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Why Python for Data Science?

Below are some reasons why Python is the most convenient language for data science programming:

Easy to learn: Due to the simplicity of syntax and large community, python becomes an easy language to start programming with.
Huge libraries: Python offers a huge variety of libraries to work with. Some of the common and widely used libraries in data science are NumPy, Pandas, and Matplotlib. Numpy supports a large number of arrays and matrices, it provides fast and efficient solutions to complex calculations on large datasets. Matplotlib helps in efficiently creating data visualization. Pandas is another amazing library of Python that is efficient in storing and manipulating large data sets faster and more easily. Hence, data cleaning and data visualization become very easy with Python which are two very important factors of data science

Open source: Due to open source, cross-platform support, and platform independence, python has become convenient for everyone in data science. Python is completely free for users and can be easily installed from a browser. Python is also supported in different operating systems like Windows, Linux, etc. It can be written on one platform and run on a different platform too.
Easy integration with other technologies: Python is very easy to integrate with other technologies like databases, web technologies, large data frames, etc. This makes Python a favorite language for many programmers.

Aren’t There Other Data Science Programming Languages?

There are multiple languages for data science programming to choose from. Some of the common languages are SQL, R, JAVA, etc. Let us explore the pros and cons of these languages for data science.

1. SQL: SQL is a query language primarily used for storing and manipulating data.

Cons:

SQL is not efficient for large and advanced statistical analysis.
SQL is not platform-independent.

2. R: R is designed for statistical analysis with strong data visualization capacity.

Cons:

R is not a very beginner-friendly language.
Unavailability of broad community.
Not efficient for very large data sets.

3. Java: Java is another reliable and scalable programming language for data science. It is very efficient for large datasets.

Cons:

Complexity of learning.
Java development cycle is comparatively longer than dynamically typed languages like Python.

Congratulations, you are now aware of the usability of Python language for data science. What’s next? Let's learn Python from basics to advance from the 98thPercentile and start your data science programming journey. Don’t forget to book our free 2-week demo class to learn more about our programs on coding.

FAQs (Frequently Asked Questions):

Q1. Is Python a good language for data science & research?

A: Yes, Python is a very good language for data science with the support of huge libraries and a broad community.

Q2. Why is Python a good language for data analysis?

A: Python is scalable, easy to use, and open source language providing a variety of libraries suitable for data analysis also it is very easy to integrate with SQL for analysis of data.

Q3. Why do data scientists use Python?

A: Data scientists use Python for its supportive features and reliability.

Q4. Can Python handle large datasets?

A: Yes, python is an efficient language for handling large datasets.

Q5. What are some common real-life examples of applications of data science?

A: Some real-life applications of data science are image recognition, financial data analysis, social media analysis, etc.

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Why Python Is Considered the Go-to Language for Data Science?