Python is a general purpose interpreted, object-oriented scripting, and high-level programming language. Python is a great for beginners or for data scientists who really want to build up their skillset. Guido van Rossum created it during the 1985-1990. It was designed to be highly readable. It has various frameworks for web development and other features to expand it for Graphical User Interface, data analysis, data visualization etc. Through its simplicity it is extensively used by many organizations for evaluating large dataset for doing data analysis. The idea of the blog is to provide you why python is good for Data Science.
Python language is among the most popular Data Science programming language not only with the top companies but also with the tech startups. It offers plenty of benefits which mean that an increasing number of people are adopting Python for their work and it I a practical choice for tech type of all kind-data scientist included and is increased adoption in numerical computation, statistical analysis, machine learning and in several data science applications.
Here are five reasons why you might choose Python for Data Science:
1. Python is easy to use
Python code is more clearly defined and is easy to understand even if you are beginners or for data scientists who want to build up their skill set. Python has few keywords, simple keywords and a clearly defined syntax. Python is great programming language whether you are an experienced data scientist or analyst, a software engineer who is going to start working more closely with machine learning or even a complete beginner, Python is going to be best programming language for data analysis.
2. Python is versatile
Python can run on a wide variety of hardware platform and its source code is easy to understand. Python is a powerful tool whatever problem you want to solve, it will help you to understand the problem more precisely. From building machine learning models, data mining, Python is a great programming language that helps you to solve data problems.
3. Python works better for building analytics tools
If you have dataset and you want to find outliers in a dataset then python works pretty well. It has number of libraries to do statistical analysis for your data. From building machine-learning models, it works well with having an interactive environment of IDE.
4. Easy Data Visualization with Python
Python have a large range of powerful visualization libraries available such a Matplotlib, Plot.ly or Seaborn and plenty of scientific packages for data visualization, Machine Learning, natural language processing, data analysis and much more.
5. Python Community is Growing
Python has a huge community including a strong and growing presence in the data science community. PyPi (Python Package Index) is a useful place to explore the full extent which was developed by the Python community. Pyslackers is a great community for Python enthusiasts.