Every professional in the field of data science struggles to choose the appropriate programming language for their project mainly when they are new to the area. Selecting the right programming language is crucial as it is difficult to move the project once you start with the development. Among the popular choices in this context are Python, R Programming, SAS, Java, etc. However, the choice of language relies upon the individual use case; many reasons support Python as an ideal choice.
Reasons to Choose Python for Data Science Projects:
1. Less is more:
Python is known across the world for making programs work in the minimum lines of code. It automatically recognizes and relates data types and follows an indentation based nesting structure. Overall Python is simple to use and consumes less time in coding. There is no restraint to the data processing. You can compute data in laptop, cloud, desktop, and you can say everywhere. Previously, Python was supposed to be slower than other languages like Scala and Java. However, with the advent of Anaconda platform, it has caught up to speed. Now it’s quick in both development and execution.
2. Python’s Compatibility with Hadoop:
Hadoop is the most famous open-source big data platform and the integral compatibility of Python is yet another reason to choose it over other programming languages. The PyDoop package gives access to the HDFS API for Hadoop and therefore allows writing Hadoop MapReduce applications and programs. Using HDFS API, you can link your program to an HDFS installation hence, making it possible to read, write and get information on directories, files, and global file system properties. Besides, PyDoop also provides MapReduce API for complex problem solving with the least programming efforts. This API can be used to effortlessly apply advanced data science concepts such as ‘Counters’ and ‘Record Readers.’
3. Powerful Libraries:
Python has a robust set of libraries for a broad range of data science and analytical requirements. Some of the popular libraries that give this language an upper hand comprise:
NumPy – It is used for scientific computing in Python. It is excellent for operations relating to Fourier transforms, linear algebra, and random number crunching. It can easily incorporate with many different databases.
Pandas – It is a Python data analysis library that provides a range of utilities for dealing with data structures and operations such as handling statistical tables and time series.
Scipy – It is a library for scientific and technical computing. It comprises modules for everyday data science and engineering tasks such as interpolation, FFT, linear algebra, signal and image processing, ODE solvers.
Scikit-learn – It is useful for regression, classification, and clustering algorithms such as random forests, k-means, gradient boosting, etc. It fundamentally compliments other libraries like SciPy and NumPy.
PyBrain – it is short for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. PyBrain offers simple yet still powerful algorithms for Machine Learning tasks along with the ability to test and compare algorithms using a variety of predefined environments.
Tensorflow – It is a Machine Learning library created by Google’s team for research in deep neural networks. Its flexible architecture and data flow graphs allow operations and computation of data, with a single API, in many GPUs and CPUs in a desktop, server, or mobile device.
Python is a popular programming language. You can easily find some people in every department such as marketing, maintenance, customer service, development, etc. who possess a working knowledge of Python. It is suitable for large organizations where it is difficult to establish communication between different departments.
If you are willing to learn Python for data science then enrolling yourself in data science with Python program through a quality institute can offer you a good start. Top institutions have state-of-the-art resources and experienced faculty to ensure quality education for students.
