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Why Python for Data Science

Why Python for Data Science

By: SMITH THOMPSON

Suppose you are a student looking for a career opportunity or starting a new career. In that case, the Data Science industry offers a massive growth opportunity and will also give a great start to your career. As the newer technologies are coming, Machine Learning, Artificial Intelligence, Data Science, and predictive analytics are becoming more popular. Though many programming languages like Java, Python, MATLAB, and R., an aspiring Data Scientist must know at least one of the programming languages. However, the knowledge about more programming languages will help you reach the zenith.

Python is the most common programming language suggested for Data Science. You should consider python as essential when you learn Data Science to further your professional career. There are many reasons for it but first, let us look at what python is.

What is Python?

Python is an object-oriented, interpreted, high-level programming language with dynamic semantics. Python has built-in data structures combined with dynamic binding and dynamic typing, making it attractive for rapid application development. It can also be used as a scripting language to connect existing components. The easy-to-learn and simple syntax emphasise readability, thus reducing the cost of programming maintenance. Code reuse and program modularity are also possible in python as it supports modules and packages. The extensive standard library and python interpreter are available in the source form on all major problems, free of cost.

As there is no compilation step, the edit-test-debug cycle is incredibly fast, making the programmers fall in love with Python. A bug or wrong input will never cause segmentation fault as the interpreter raises an exception as soon as it discovers an error. This feature makes debugging python programs easy. A source-level debugger will allow the inspection of local and global variables, setting breakpoints, evaluating expressions, and line-wise stepping of the code when the interpreter doesn’t catch an exception.

Why is Python Preferred for Data Science?

Python is the choice of the Data Scientist for the daily tasks they perform when it comes to programming languages. It is also one of the leading science tools used across various industries. Python is the best choice for those Data Scientists who need to integrate data with web-based applications or those who need to incorporate statistical code into the production of databases. Data Scientists also need to implement algorithms daily, and python is best suited for it.

Python also has a massive community for your help and support. If you are stuck somewhere and are unable to resolve it, you might get frustrated. In this case, python has a full-time community to help you learn python with ease.

Also, specific packages or libraries like the Pandas, NumPy, and SciPy are tailored for certain python functions.

According to some engineers coming from various industries, there are specific Python APIs used for deep learning that is available and the scientific packages of python, which has made python extremely productive.

Easy to learn

It is easy to learn python, which makes it popular among beginners and those familiar with programming languages. Python has an easy-to-understand syntax and a much shorter learning curve when compared to other programming languages like R, which makes it more preferable over other languages. Also, there are many online data science certification courses available that can ease your learning.

Scalability

Python is much faster than other languages like MATLAB.   Python facilitates scale as it gives the Data Scientists multiple ways to approach a program. Python can be found across many different platforms and industries, empowering the development of various applications. YouTube also shifted to python usage for this reason.

Different Data Libraries

Python offers many different Data Science libraries and data analysis, making it more convenient to use than other programming languages. Python has hundreds of different libraries and frameworks to support users in various tasks they need to perform. Some of the python libraries are:

  1. Pandas- Pandas provides data manipulation control and is also great for data handling and data analysis.
  2. NumPy- NumPy aids in high-level math function with data manipulation. It is free of cost library for numerical computing.
  3. SciPy- SciPy can be used for various data modifications and optimisation. It also has many special functions which can be used for scientific and technical computing.

Python is also planning to add many more libraries to its collection in the recent future. Many Data Scientists using python find new solutions to their problems which seemed unresolvable to them, and there are many Data Scientist training courses that one can take up.

Learn Python with Data Science - Free Online Course | Great Learning

Many Visualizations Options

Python provides its users with many visualisation options. Libraries like the Matplotlib offer a solid foundation with packages built around it, like the ggplot, pandas plotting, and the seaborn. The packages used in visualisation help create charts, graphical plotting, and web-ready interactive plots that the programmers then use.

Web Development

With many Django and flask libraries and frameworks, python makes your web development process easier like never before by speeding up your work efficiency and saving your time. Compared to PHP, work done using PHP in a few hours will take only a few minutes using the python libraries. Some of the full-stack frameworks for web development by python are

  • Django
  • Web2py
  • Pyramid
  • TurboGears

Some of the micro-frameworks provided are

  • Bottle
  • Flask
  • Hug
  • CherryPy

In addition to all the libraries, python also has some powerful machine learning libraries, making it a significant advantage for Data Scientists to use python as a programming language. Building robust and appropriate neural networks is not much tricky using these libraries. Some of the machine learning libraries that python provides are mentioned below:

  1. Scikit-learn- It is mainly used for machine learning. Also known as Sklearn, it is built on the libraries like NumPy, SciPy, and Matplotlib. It provides to its users some machine learning algorithms through a consistent interface. It also supports both supervised and unsupervised tasks. Few available algorithms are DBSCAN, k-means, random forest, and many more.
  2. Keras- It is a very famous high-end API that also acts as an interface for the Tensorflow library. It is used for building a neural network using a backend by TensorFlow, which is very user-friendly.
  3. Tensorflow- Since Tensorflow was initially written in C, it provides its user with the power and performance of python without compromising on its simplicity. It is a high-level library and is not suggested for beginners to work on.
  4. Pytorch- Created by Facebook’s artificial intelligence research group, Pytorch is another framework for machine learning in python. It has a lower level API which makes it more complex than Keras. Thus it is not suggested to be used by beginners.

Bottom line

With its growing popularity and the increase in its usage by Data Scientists, the use of python for Data Science will never end and continue to increase inevitably. As the technologies advance with artificial intelligence and deep learning, we will see more libraries coming up in python supporting AI and machine learning. Python is used for Data Science and will continue to be used in the years to come. So it would greatly help your prospects if you join for data scientist training online to learn the fundamentals of advanced applications. And, to find the best possible course to help with your learning, Greatlearning will always be by your side with its bank of online training and certifications facilities.

Reference

https://analyticsindiamag.com/why-should-you-learn-python-for-data-science/#:~:text=Unlike%20other%20programming%20languages%2C%20such,YouTube%20migrated%20to%20the%20language

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