March 22nd, 2019
Python & Big Data- A Match Made In Tech Heaven
Ever wonder how big is big, when it comes to data? Well pretty big in fact it is estimated that the digital universe will amount to 44,000 Exabytes (44 trillion Gigabytes) by 2020.
What’s more fascinating is that the amount of data online is doubling every 2 years and this trend is not likely to stop anytime soon.
Python Has Unleashed Endless Possibilities For Businesses
With all this data available online, there are a number of possibilities that open up for businesses, research organizations and state authorities.
Making use of this data to understand the patterns helps in the application of AI and Machine Learning as well. However making use of this Data is only possible by having the right tools in the bag to uncover, explore and create new avenues.
One such tool is Python
Python’s long been used in analyzing Big Data for various reasons, But simply put it’s the best language out there, wonder why?
Big Data is Big But So is Python
When the data that is to be studied is huge, it’s a given you need a language that has the computational power to analyze it and provide deeper insights.
Python with its plethora of libraries can be used in numerous ways to solve complex problems and produce meaningful results on a given data. What’s more is that the number of these libraries is growing as its users grow in number too.
The Python Community is Burgeoning
For a language as old as Python, it’s a wonder how far it has managed to come and still be so popular among the data scientists of today.
Python is the go-to tool for Data Scientists today and its increasing popularity means that there is much more programming support for Python engineers, many bugs have patches readily available and the number of libraries and frameworks keep on increasing as well, helping aspiring data scientists make the most out of Python while working with Big Data.
Python is Easy, You Don’t Need a Ph.D.
Working with Python is a child’s play really, ok not really but Python with its various libraries, frameworks, and patches make it such a versatile tool for analyzing Big Data as its features inherently enable users to abstract information from the raw and unstructured data.
Furthermore, coding in Python with its user-friendly features like simple syntax, readability of code, auto identification and easy implementation means that fewer lines of code have to be written to perform the same task that another language would require to do on the same dataset.
The Python Libraries Are A Life-Saver
Pandas: is Python’s most powerful library for data manipulation. It contains a variety of functions for data import & export, merging, splitting, aggregating, selecting and reshaping.
NumPy: is a general-purpose array-processing package. It is designed for efficient manipulation of large multi-dimensional arrays of records.
SciPy: a library used for scientific and technical computing. It contains modules for integration, interpolation, optimization, linear algebra, special functions, image processing, and other engineering tasks.
Scikit-learn: It is a data processing library of Python, using operations such as classification, clustering, preprocessing, regression and dimensionality reduction.
Pybrain: stands for Python Based Reinforcement-Learning Artificial Intelligence & Neural Network. This Python Library offers flexibility, powerful algorithms for Machine Learning Tasks and a number of environments to test and compare Python algorithms.
Python is Free And For Everyone
The best thing about all of the above? They’re entirely free to use. Yes, you heard it right. Python, its frameworks, libraries and everything mentioned up above is all free and for everyone to make use of and be an expert Data Scientist. You don’t have to subscribe to anything, purchase a license or sign a contract to start using Python. Just hop onto your computer, download the app and start digging the data.