After the year 1950, the world has seen the development of programming languages. Programming languages are the way people use to communicate with machines. Be it JAVA, Python, C++ 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 and digitalization keep the world alive and advance. Data science is one of the technologies that companies are now using to keep themselves ahead of competitions.
So here in this blog, we’ll learn more about data science, programming language, and how organizations of all sizes are using it.
The story behind Data Science
In today’s digital world, companies are using data for a plethora of reasons such as understandings the user’s behaviors, designing applications, and developing new software. Organizations like to extract their customer data to customize their services and to create a futuristic plan.
Data scientists are those people who collect, extract the data with the help of algorithms pattern, scientific methods, and other techniques. If you’re looking for a career full of opportunities and immense growth, then become a data scientist. With the innovation of Artificial Intelligence, analytics, data scientist becomes a top emerging job profile. Well, its significant to learn multiple programming languages for budding data scientists.
But Python is considered to be the most prominent language in the field of data science. It is an optimal choice for beginner data scientists as it is simple, understandable, and effective to use. Thus, the language makes it a top priority for companies to hire python developers for data science projects.
Why Python for Data Science?
Python is an open-source language. This means that anyone can add function in the existing language. In fact, big tech giants are coming up with their tools, libraries, and set of functions to achieve their development goals.
Python- Most preferred for Data Science
In most scenarios, Python is used to tackle the daily chores of data science. Python libraries such as NumPy, Pandas, Matplotlib, IPython, SciPy are specially designed for data analysis tasks. For data scientists who integrate the codes into the application, Python is a worthy choice. In data science, you need to implement the algorithm pattern, that’s too easy to do which makes this language an ideal choice. Python versatile nature and diligently designed libraries make all the data science tasks hassle-free.
Easy to Grasp
One of the qualities that make it popular in the developer’s community is that python is easy to learn. Ever the beginner developer who just step into 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 language is suitable for them also. If we compare Python with the other programming language of data science such as MATLAB, it is slightly easy to learn and work on.
Python is not like other programming languages such as R. Python works brilliantly when it comes to scalability. The language is faster in comparison to MATLAB and Stata. Python provides multiple ways and flexibility to solve glitches. That’s the reason why Youtube shifted its codes to python. On similar lines, now a lot of companies are writing codes in python for their web-based applications and products.
Data Science libraries
Another perk for data scientists for using python is that the language offers a wide variety of tools and libraries. Therefore, data scientists consider python as a resilient programming language that fulfills the needs and solves puzzle problems. Let’s just go a bit deeper into these python libraries and tools to understand what are their significance usage and benefits.
Pandas is a library that has structure and function use for data wrangling. Diligently designed to work efficiently and quickly for data analysis and manipulation.
This library is for array manipulation and algebraic operations. Numpy array is beneficial than other python libraries for containing numerical data.
Matplotlib is developed for data visualization tasks like 2D data view and set plots. Expert data scientists need to visualize their results so that other folks can understand.
IPython is a scientific tool for quick prototyping. With this tool, you can execute, develop, and debug multiple applications simultaneously.
Wrapping UpData science is growing rapidly. Python is accumulating multiple tools and libraries to help developers achieve their goals. Additionally, the python community is helping scientists find the solution to a difficult problem so that they can build advanced solutions. If you are looking for the python development company, that works on all these above libraries efficiently and effectively, feel free to contact us.