Reasons that makes Python Powerful for Data Science!

Data science involves extrapolating useful information from bulky stores of structured and unstructured data. These datasets are usually unordered, have inappropriate and redundant information and are difficult to correlate with any meaningful accuracy. Hence the data has to be processed and modeled before anything insightful can be drawn from it. Therefore something robust like python is required to provide a fast and effortless data processing and exploration.

Python is a high-level, interpreted, general-purpose programming language that supports multiple programming paradigms. Python is widely used in the field of data analysis and data science. It provide us with the ability to deal with mathematics, statistics and scientific functions using programming. It provides a huge number of libraries to deals with data science application. It is internationally acclaimed that Python helps in handling the data better for a plethora of reasons.

Some of the major causes that makes python an appropriate programming language to be used for data science are:

1. Easy to Learn: The most important reason for the popularity of python for data science is the ease with which it could be learned and implemented. Rather than being a middle level language like Java, C++, etc., Python is a high level language which closely resembles the widely spoken English language. It enables us to perform very complex tasks by using just a few lines of code. In other words, it enables us to focus more on logic rather than on programming. Also, being a flexible and open source programming language has added to it’s popularity.
2. Wide Library Set and Frameworks: Python has a vast library set that is useful in various data handling scenarios and other paradigms. One can import these libraries and can make miracles happen within few lines of code. Some of the popular Python libraries used by Data Scientists are SciPy, Tensorflow, Numpy, Pandas, Matplotlib, etc. And not to forget, python frameworks like Django make web development handy and give us the ability to make scalable and secure apps and websites just using one platform.
3. Availability of better Analytics Tools: Data Analytics is a very important stage of the Data Science, that is carried on the data, straight after the data has been gathered. Python provides better tools for analyzing data through libraries like Pandas, Numpy, SciPy, etc., which helps in penetrating the data deeply, extracting insights and understanding the patterns and relationships existing in the data. Ultimately this is helpful in making better data-driven decisions and lessen our labor.
4. Multiple Visualization Tools: Once data has been gathered and analyzed it becomes necessary to present the data and insights in the most expressive and comprehendible way. Visual information is much easier to understand, comprehend, operate and remember. Hence to make data more accessible, easier-to-use and understand python has incorporated powerful libraries like Matplotlib, Seaborn, pandas plotting, etc., which helps us in getting a good sense of data, creating charts, graphical plots and web-ready interactive plots.
5. Supports Tools and Libraries for Deep learning: Deep learning algorithms are inspired by the human brain architecture. Python has always been a good option whenever it comes to implementing deep learning algorithms that are inspired by artificial neural networks. Some of the python packages like Tensorflow, Keras, Scikit-learn, etc., helps the Data Scientists in implementing certain deep learning models. Scikit-Learn helps in quickly implementing popular algorithms on datasets and solve real-world problems.

All these features together have made python a tremendously powerful language that can alone be used for all the data science related chores. It has empowered the data scientists to accomplish more in less time by the means of it’s easy syntax and huge community support.

Nancy Tayal