Data Science has become a popular field in the last few years. The primary focus of Data Science is converting meaningful data into business and marketing strategies that help the company grow. The data is analyzed in order to get a logical solution. Previously, only IT companies got involved in Data Science. But today, businesses from different sectors like healthcare, e-commerce, finance, and others are using Data Analytics to help them make quicker and better decisions. There are various tools that can be used for Data Analytics like R programming, SQL, SAS, Hadoop, and many more.
However, the easiest and most flexible tool for data analytics is Python. It can support structured programming, functional programming, and object-oriented programming. As per the 2018 StackOverflow survey, Python is the most popular programming language all across the globe. It is also the most suitable language for Data Science.
Why is Python a language of choice for data scientists?
Python is easy to use and has a unique attribute when it comes to analytical and quantitative computing. It has been the industry leader for some time and is widely used in different fields like finance, signal processing, healthcare, and others. In fact, Python is used for strengthening the internal infrastructure of Google and in creating applications like YouTube.
Python is a favorite tool and is widely used in the field of Data Science as it is an open-source and flexible language. The different Python libraries can be used for data manipulation and are easy to learn, even for a beginner. It is an independent platform that can be integrated with other existing infrastructure for solving the most complex problems. Several banks use it for crunching data, processing, and visualizing it.
Why is Python becoming a preferred language?
It is easy-to-use and powerful – Python is a beginner’s language. Any researcher or even a student with basic programming knowledge can start working with it. Time spent on different software engineering constraints and debugging codes will be minimized. Compared to other languages like C, C#, and Java, the time for implementing code is less that helps software engineers and developers spend more time working on their algorithms.
You will have your choice of libraries – Python offers a huge database of libraries for Machine Learning and Artificial Intelligence. The most popular Python libraries are Scikit-learn, PyTorch, Seaborn, Matplotlib, TensorFlow, and many more. There are several Machine Learning and Data Science resources and tutorials available online that you can access easily.
It is a scalable language – Compared to programming languages like R and Java, Python is faster and a highly scalable language. It offers flexibility for solving problems that can’t be solved with other programming languages. There are several businesses that use Python for developing rapid tools and applications.
Graphics and Visualization – With Python, you get a lot of visualization options. The Matplotlib library offers a strong foundation. You can use these packages for creating charts, graphical layouts, web-ready plots, etc.
How Python is used in Data Science?
The First Stage – First, you must have an understanding of what type of form the data should take. If you consider it as an excel sheet with thousands of rows and columns, you will know what you should do with it. You can perform some functions for deriving insights and looking for a certain form of data in every row and column. It can take a lot of effort and time for completing this form of computational task. For this, you can use the Python libraries like NumPy and Pandas for performing the job quickly using parallel processing.
The Second Stage – The next task is getting the required data. You won’t always get the data readily available for you. You might have to scrape data accordingly from the web. Python libraries like BeautifulSoup and Scrapy can help extract data from the web.
The Third Stage – This stage of Data Science involves the graphical representation or visualization of data. When there are so many numbers on the screen, deriving insights from data will be extremely difficult. One of the best ways of doing this is by representing data through pie charts, graphs, and other formats. You can use Python libraries like Seaborn and Matplotlib for performing this function.
The Fourth Stage – This next step is a highly complex computational technique known as Machine Learning. It involves using mathematical tools like calculus, matrix, and probability functions of over lakhs rows and columns. Machine Learning can be a little complex. However, this can become super easy with the help of machine learning libraries like Scikit-learn.
All of the steps mentioned above were of data that is in the form of text. But, data can be in the form of images. Python can be used for handling this type of operation as well. Opencv is an open-source library that is solely dedicated to image processing.
Why is Python popular in the Data Science communities and groups?
Python is one of the most-suited languages for data science tools and applications. There are several online Data Science with Python courses that are available for beginners. It’s easy to understand, flexible, and versatile features make Python the most important skills that big corporations want in their Data Science professional. Python is incredibly productive, thanks to the deep learning frameworks in its APIs.
In the past couple of years, Python has gone through a lot of evolution and improvement, especially after the release of TensorFlow. When it comes to the field of Artificial Intelligence, you can validate your ideas in a few lines in Python. Even Machine Learning Developers and Scientists prefer Python to build applications and tools like NLP (Natural Language Processing) and sentiment analysis.
Python’s easy-to-use syntax and compatibility are what make it one of the most popular programming languages in the Data Science groups and communities. Even if you don’t have a science or engineering background, you can learn Data Science in a small span of time.
