Top 9 languages for Data Science in 2020

Of 256 programming languages, know the ones for Data Science!

Photo by Clément H on Unsplash

Data Science has been a big deal for quite some time now. In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that we know how to analyze, process, and use that data for further knowledgable business insights.

There has been enough said on Python vs R for Data Science but I am not talking about it here. We need both of them and that’s about it. I have created a list of Top 10 programming languages for Data Science that you can learn in 2020 and also while there is still some time to hit back outdoors :neutral_face:

The languages made to the list on the basis of their popularity, number of Github mentions, the pros and the cons, and their relevancy to Data Science in 2020.

1. Python

All you need is Python. Python is all you need.

Source: Python Software Foundation

I can write tens of stories on why Python is THE language for Data Science.

Because of its versatility, Data Scientists can use Python for almost any problems associated with the data science processes.

Why Python?

The object-oriented nature of Python facilitates data scientists to execute tasks with better stability, modularity, and code readability. While Data Science is only a small portion of the diverse Python ecosystem, Python is rich with specialized deep learning and other machine learning libraries and popular tools like scikit-learn, Keras, and TensorFlow. Undoubtedly, Python enables data scientists to develop sophisticated data models that can be plugged directly into a production system.

Per Python developers' survey results , 84% of respondents used Python as their main language, while for 16% it was their second language.

Data in Python

For data collection , Python supports CSV, JSON, SQL tables, and web scrapping with beautiful soup.

The data analysis library for Python, Pandas is hands down the best you can get for data exploration. Organized into data frames, Pandas can filter, sort, and display data with all the ease you can imagine.

For data modeling,

1. NumPy — numerical modeling analysis
2. SciPy — scientific computing and calculation
3. scikit-learn — access numerous powerful machine learning algorithms. It also offers an intuitive interface that allows Data Scientists to tap all of the power of machine learning without its many complexities

For data visualization , matplotlib, plot.ly, nbconvert to convert Python files to HTML documents spells out beautiful graphs and dashboards to help Data Scientists express the findings with force and beauty.