Why Use Python for Data Science?

Python for Data Science

With the advancement of machine learning, AI, predictive analytics, data science is becoming a more popular career choice, it is always beneficial to know multiple programming languages. However, as a budding data scientist, this can be a tough choice to decide where to start. There are many to choose from, like Java, Python, Scala, MATLAB, and R.

Learning to code can be a daunting prospect even for a businessperson who needs to dabble in data science. It seems that now, learning Python for data science is the best route.



Currently, Python is one of the most widely used programming languages, and most data scientists use Python for data science. And the stats support that too.

As per the study conducted by Burtch Works – the majority of the data scientists/analysts prefer Python over R or SAS, which were the most common languages used some time back.

Advantages of Python and Why We Use Python for Data Science

Here we will be discussing a few of the major advantages of Python and why we recommend you use it for data science

  1. Easy to Learn – Python is a very easy-to-write language as most of the codes are very intuitive and hence has a significantly shorter learning curve than its rivals like R, Java, etc.
  2. Scalability – Python scales very fast and provides multiple solutions for the same problem, making it the programming language of any data scientist. Because of the sheer speed at which Python is scaling up, organizations like YouTube are adopting Python.
  3. Libraries and Frameworks – Python supports a large number of libraries and packages exclusively designed for machine learning / deep learning. A sophisticated Artificial Intelligence algorithm can also be implemented and is readily available in Python. It has a wide array of packages for statistics and basic data science tasks like data manipulation/ data wrangling. A few popular libraries are Pandas, Scipy, NumPy, Sklearn, etc.
  4. Graphics and Visualizations – All kinds of activities a BI analyst is supposed to do on advanced tools like Tableau /PowerBi can be replicated or even upgraded using Python. Packages like matplotlib, plotly, streamlit are game-changer and result in aesthetically advanced informative visuals, which is the need of the hour for any organization.
  5. Web Development – Python has Full Stack web development frameworks like Django, Pyramid, web2py, which can help you create appealing apps which can further act as a wrapper to your machine learning models.
  6. Huge community support – Python has an ever-growing strong community that keeps it updated and helps you debug your code. Almost all kinds of problems and solutions are readily available on the internet.
  7. Jobs and Salary – Python Developer being one of the highest-paid jobs globally, it also enjoys the honor of having an increasingly high-demand job profile. Hence, it is a very attractive option for anyone to learn. Have a look at the average Python developer salary in the US by state, and this might give you a better understanding of how well the python developers are paid.

Source- DAX

The bottom line is that Python is a very popular language for data science for all these good reasons and more. It’s versatile, dynamic, and easy to learn. Yet it can solve problems in math, statistics, and more. Overall, Python is a win-win for businesses and their data science teams. Hence it is strongly recommended as a must-have tool for any data scientist or analyst.

~ Nishkam Shivam, Data scientist @ Bristlecone | Ex- Walmart | Ex- Accenture 

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