Data science industry is continuously evolving in every aspect. It leverages many fields and now no organization or company gets success without data science.
A data scientist has to collect and analyze data through various sources such as machine learning, transfer learning, etc. Data scientists used models to decipher problems and generate best results.
When we talked about data science, a data scientist should know at least one or two programming languages that are helping to make programs and modules.
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Programming Languages:
Many programming languages are used to implement algorithms and multiple applications.
When programming languages are being mentioned, one programming language must come into everyone’s mind, which is Python. It is an open-source programming language and has a big community.
Why Python is everywhere:
In 1991 Guido Rossum developed python. Since its birth, Python recognized as the most popular programming language all over the world. With its immense lucidity and readability, it is widely used. With most powerful analytical libraries make it first choice for data scientists.
Python doesn’t provide great power for econometrics, business analytics and communication. Python helps in engineering, data wrangling and web scrapping and many others. Python also helps in implementing machine learning on a large scale.
Why Python Is Important:
Python is used for general purposes, but it can be used much more than that. Python has most advanced API, which is quite useful in Al and machine learning.
Most data scientists use five python libraries that are, Spicy, Pandas, Scikit-learn, Seaborn and Numpy
Here are some key features of Python programming language that describe its purpose and usage.
Simple and easy to learn
Simple Python has high readability and it is straightforward to use as compare to other languages. We can say python is a programmer-friendly language.
Expressive Language
Python is expressive than other languages, so anyone can easily understand the codes.
Interpreted
Python codes are easily interpreted by an interpreter, line by line at a time.
High-level language
High-level programming languages offer a strong conception of detailed programming ideas and concepts. They have the ability to create codes that make a computer independent. Python comes under high-level languages because it is easy-to-use.
Object-Oriented language
Python is known as an object-oriented language and it follows the class concept and object.
Platform Independent
Python is a platform-independent language that means its code can easily run on any program such as Linux, UNIX, Macintosh, or Windows; thus we can say it is an independent language.
GUI Programming
You can design Graphical user interfaces using Python.
Huge Standard Library
It has a huge range of standard library for different purposes, such as Spicy, Pandas, Scikit-learn
Encompasses lots of extensions
It has lots of extensions to use.
Overwhelm community support
Python receives overwhelming responses from the community and popular all over the world.
Is python enough for data science?
Many programming languages are used in data science industry for implementation, but python is most popular due to its high readability and simplicity.
People ask is python enough for data science, the answer is “NO.” The combination of two most conductive programming language makes your work more enjoyable.
Python’s libraries such as numpy, Pandas and Scikit-learn make it an excellent choice for machine learning purposes.
You can choose any other language such as “R” or “SQL” to increases your chances of success in the field.
Python can perform the non-statistical task as well as statistical tasks, but it provides better coverage to non-statistical distribution. Web scraping in python is much easier than any other language because of its libraries.
Why Not Use Two Languages:
It’s not necessary to learn more languages but having knowledge of two languages makes your profile strong. Most data scientist jobs require knowledge of Python or R.
Many people think that using two programming language at the same time create some problem. That’s true because accessing another language makes them smarter.
Summing it up:
Only having Python in your bucket doesn’t make your first choice of employer. Simple Knowing Python is like a coffee without sugar, milk, or cream. You must have a little bit of access to other languages.
Learn at least two programming languages and play each to strengthen your efficiency. To get more chances of hiring in a lucrative field of data science, polish your skills and get command on more programming language rather than trust only Python. To choose best data science course in Hyderabad click here.
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