While reading for AI/ML (Artificial Intelligence/Machine Learning), I came across a discussion – if Python can be used as a “statistics workbench” to replace R, SPSS, etc? It was nice shareout by multiple knowledge folks related to languages used for problems of statistics, specifically R (read about R here).
Discussion here: https://stats.stackexchange.com/questions/1595/python-as-a-statistics-workbench
For quick reference, I will quote few of the latest thoughts from there that are in favor of Python and how it has evolved. I too conquer with most of them:
1. Python is easily the most intuitive syntax of any programming language. This makes for extremely fast development time.
2. Python is performant. It opens large datasets reliably.
3. The packages in Python are fast catching up to R’s packages. Python usage has increased tremendously last few years.
4. Readability is one of the most important qualities good code can possess, and Python is one of the most readable language.
5. Python has an extremely well-thought-out IDE now: PyCharm & Visual Studio Code.
https://stats.stackexchange.com/a/457753
Overall, Python is a general purpose language with an easy to understand syntax which would be relatively easier for usual programmers to learn/adopt. R is developed keeping statisticians in mind. Thus it has many features around data visualization and is a tad ahead currently.
A little research …
Recently DataCamp too published an article comparing R and Python for data analysis. There is a nice comparison in it on various parameters, picking just couple of them here:
Final analysis in the paper shares R being ahead in comparison for data analysis but Python having potential to catch up quickly and easily.
My thoughts …
My intent was to understand which of the programming language serves as an essential tool to demonstrate AI/ML capabilities. Looking at them, Python seems good enough for me to serve as AI/ML tool to start and probably conquer it.
Ammunition needed …
There are many python based libraries and packages that are generally used for statistical work. Below are few of them that would help in our data analysis exploration going ahead:
- scipy – python-based ecosystem of open-source software for mathematics, science, and engineering.
- cookbook – many statistical facilities, a collection of various user-contributed recipes already available
- numpy – base N-dimensional array package. Handful of example lists here
- pandas – a fast, powerful, flexible and easy to use data analysis and manipulation tool
- matplotlib – a comprehensive library for creating static, animated, and interactive visualizations
- scikit-learn – simple and efficient machine learning tools for predictive data analysis
- keras – API for deep learning
- tensorflow – API to develop and train ML models
Since I am a programmer, I maybe be biased here. But, it seems Python can and does all the needful to start with AI/ML journey.
Happy learning!