Since the year 1950, the world has witnessed the development of various programming languages. Programming languages are the way people use to communicate with machines.
Be it JAVA, Kotlin, Node.js, Python, C++, or others, each language is developed to solve complex problems. The result of these programming languages is the software and applications that we are using today.
New technologies help companies strike a competitive edge. Data science is one of those new technologies that companies worldwide are now leveraging to keep themselves ahead of competitors.
So, in this blog, we’ll walk you through why Python is ideal for data science. But let’s first have a look at the story behind data science:
The Story Behind Data Science
In today’s digital world, companies are using data for a plethora of reasons such as understanding the user’s behaviors, designing applications, and developing new software.
Organizations need to extract their customer data in order to tailor their services and curate a futuristic plan.
Data scientists are those people who collect and extract the data with the help of algorithms, patterns, scientific methods, and other techniques. If you’re seeking a career full of amazing opportunities and great growth, you should consider becoming a data scientist.
With the innovation of Artificial Intelligence and analytics, data scientist becomes a top emerging job profile. Well, it's significant to learn multiple programming languages for budding data scientists.
But Python is regarded as the most notable programming language in the field of data science. It is a great choice for beginner data scientists as it is straightforward, comprehensible, and effective to use. Thus, the language makes it a high priority for global companies to hire Python developers for their data science projects.
Why Python for Data Science?
Here are some of the reasons that explain why Python is ideal for data science.
- It’s an Open-Source
Python is an open-source language. This means that anyone can add functions to the existing language. In fact, the world’s leading tech giants are coming up with their libraries, tools, and set of functions to accomplish their development goals.
- It Makes The Task of Data Scientists Easier
In most scenarios, Python is utilized to handle the common tasks of data science. Python libraries such as NumPy, Pandas, Matplotlib, IPython, and SciPy are specially designed for data analysis tasks.
For a data scientist who integrates the codes into the app, Python programming language is a worthy choice.
In data science, you have to implement the algorithm pattern which is too easy to do using Python. Thus, this language is an ideal choice. Python's versatile nature and diligently designed libraries make all the data science tasks hassle-free.
- Easy to Grasp
One of the traits that make this programming language popular in the developer’s community is that Python has an easy learning curve. Even beginner developers who just step into the world of data science can grasp the knowledge of python without investing much time.
Most of the professional developers don’t have much time to learn anything new. Thus, the programming language is apt for them also.
If we compare Python with the other programming language of data science such as MATLAB, it is slightly easier to learn and work on.
- Phenomenal Scalability
Python is not like other programming languages such as R. Python works excellently in terms of scalability. The language is faster in comparison to MATLAB and Stata.
Python provides several ways and flexibility to address glitches. That’s why Youtube moved to Python. Moreover, today, there are many popular apps built with Python.
- Data Science Libraries
Another benefit for data scientists in using Python is that this programming language offers a wide range of tools and libraries. Thus, data scientists consider this language as a resilient programming language that meets the needs and solves complex or puzzle problems.
Let’s just go a bit deeper into these python libraries and tools to understand their significant usage and benefits.
- Pandas
Pandas is a library that has structure and function used for data wrangling. It’s designed to work efficiently and fast for data analysis & manipulation.
- NumPy
This library is intended for array manipulation and algebraic operations. Numpy array is more helpful than other python libraries for containing numerical data.
- Matplotlib
Matplotlib is developed for data visualization tasks like 2D data view and set plots. Expert-level data scientists have to visualize their outcomes so that others can comprehend them.
- IPython
IPython is a scientific tool for quick prototyping. With this, you can implement, develop, and debug multiple applications at the same time.
It’s an Open-Source
Python is an open-source language. This means that anyone can add functions in the existing language. In fact, the world’s leading tech giants are coming up with their libraries, tools and set of functions to accomplish their development goals.
It Makes The Task of Data Scientists Easier
In most scenarios, Python is utilized to handle the common tasks of data science. Python libraries such as NumPy, Pandas, Matplotlib, IPython, SciPy are specially designed for data analysis tasks.
For a data scientist who integrates the codes into the app, Python programming language is a worthy choice.
In data science, you have to implement the algorithm pattern which is too easy to do using Python. Thus, this language is an ideal choice. Python's versatile nature and diligently designed libraries make all the data science tasks hassle-free.
Easy to Grasp
One of the traits that make this programming language popular in the developer’s community is that Python has an easy learning curve. Even beginner developers who just step into the world of data science can grasp the knowledge of python without investing much time.
Most of the professional developers don’t have much time to learn anything new. Thus, the programming language is apt for them also.
If we compare Python with the other programming language of data science such as MATLAB, it is slightly easier to learn and work on.
Phenomenal Scalability
Python is not like other programming languages such as R. Python works excellently in terms of scalability. The language is faster in comparison to MATLAB and Stata.
Python provides several ways and flexibility to address glitches. That’s why Youtube moved to Python. Moreover, today, there are many popular apps built with Python.
Data Science Libraries
Another benefit for data scientists for using Python is that this programming language offers a wide range of tools and libraries.Thus, data scientists consider this language as a resilient programming language that meets the needs and solves complex or puzzle problems.
Let’s just go a bit deeper into these python libraries and tools to understand their significance usage and benefits.
- Pandas
Pandas is a library that has structure and function use for data wrangling. It’s designed to work efficiently and fast for data analysis & manipulation.
- NumPy
This library is intended for array manipulation and algebraic operations. Numpy array is helpful than other python libraries for containing numerical data.
- Matplotlib
Matplotlib is developed for data visualization tasks like 2D data view and set plots. Expert-level data scientists have to visualize their outcomes so that others can comprehend.
- IPython
IPython is a scientific tool for quick prototyping. With this, you can implement, develop, and debug multiple applications at the same time.
Some Final Thoughts
Python is accumulating various tools and libraries to help software developers accomplish their goals. Moreover, the Python community is helping scientists find the solution to challenging problems so that they can engineer advanced solutions.
If you want to leverage Python for your projects, make sure to join hands with Python developers with hands-on expertise in all the above libraries.
Know what are the reasons that make data scientists so much stop their work these days, Read more.
