In the last decade, enormously large amounts of data were generated with the advent of smartphones and increased access to the internet. Businesses quickly realized the fact that user-generated data is the key to target their prospective customers, as it allows them to peep into the personal product preferences of people in general.
As per a Statista report, 76% of LinkedIn data science openings in the US in 2019 demanded knowledge of Python.
Besides, per a 2019 survey report by Business Broadway, Python has emerged as a mandatory language to execute AI and Data Science tasks. Three out of 4 executives active in the data science industry believe that aspiring professionals must have a good grasp on the fundamentals of Python to be able to transform data insights into report-friendly documents.
Packages in Python such as SciPy, NumPy, and Pandas have proved themselves as the most compatible with generating great results for data analytics operations.
Reasons Why Python Dominates Data Analytics Programming & Algorithm Development
Ease in Use & Implementation
Python is regarded as a beginner’s language in data analytics that any student can learn with much ease. The time required for debugging codes is also minimized with the use of Python.
Besides, in comparison to Java, C, and C++, code implementation time reduces significantly with Python that ultimately helps software engineers and developers to allow themselves time working on algorithms.
An Abundance of Libraries
The popular programming language provides a huge database of libraries. Find below the most used libraries in Python meant for AI and ML development:
- Scikit-Learn – It allows efficient handling of fundamental ML algorithms such as linear & logistic regressions, clustering, classification, etc. in data science projects.
- Pandas – It is meant for high-level data analysis and structures. Facilitation of data merging and filtration happens here. Further, data gathering from sources such as Excel is managed in Pandas
- Keras – This library supports deep learning. It provides for prototyping and fast calculations by utilising GPU in addition to the CPU of a computer.
- TensorFlow – for training, setting up, and utilizing artificial neural networks consisting of large datasets.
- Matplotlib – to create histograms, 2D plots, charts, and various other visualization elements.
- NLTK – to work in tandem with natural language recognition, computational linguistics, and processing.
- Scikit-image for processing pictures.
- PyBrain to work on neural networks, reinforcement & unsupervised learning.
- Caffe to facilitate deep learning that helps in smooth shifting between GPU and CPU. Further, the framework allows the processing of 60+ million images in a day exploiting an NVIDIA K40 GPU.
- StatsModels for data analysis, and to develop statistical algorithms.
Scope for Scalability
In comparison to programming languages such as R and Java, Python has the upper hand over both of them, as it's much faster and scalable. Python offers a great deal of flexibility in solving problems that cannot be solved making use of other languages.
Graphics & Visualizations
One can choose from a plethora of visualization options exploiting the Matplotlib library of Python.
Python Requires Significantly Less Coding
Yes, one will be surprised to know that, with Python, you will need to write only 1/5th of the code in comparison to the amount of coding required in other OOPs languages. As a result, testing the code becomes easy for developers.
Platform-Independent
Python’s code for machine learning algorithm development can be run across platforms such as Windows, Linux, Unix, macOS, and twenty-one more. Developers can make use of the PyInstaller package to help their Python code get ready to be executed on varied platforms. It eventually saves a lot of money and time by avoiding the costs involved with running tests on diverse platforms.
Uncomplicated Code
Data science professionals across industries find it much easier to read and understand Python code as compared to coding in other languages. And therefore, developers face no problems in understanding the code written by their peers, or to changes to it.
There exists no confusion, conflicting paradigms, or errors. It allows ML and AI developers across the world to swiftly exchange their ideas, algorithms, and tools.
Tools such as IPython offer added convenience features like debugging, testing, tab-completion among many others.
Large & Flourishing Community
It’s a big support to any developer located across the world to get help from their peers in error-solving when writing code in a specific programming language. With Python being the most-popular open-source language in ML and AI development, there are huge communities and forums of Python developers that exist online. You are free to exchange and discuss your programming ideas, errors, and queries, 24/7 with your fellow programmers and developers.