Why Learn Python for Data Science?

With technologies like artificial intelligence and machine learning receiving huge investment from enterprises, data scientists have become a sought after corporate commodity. The demand is growing and is not expected to decline anytime soon. Many of the best data scientists are well-versed with Python. Python is a high-level programming language with an easy syntax, which makes it possible to write minimal code. This makes the program lightweight and efficient.

A Data Science using Python Training offers over 40 hours of instructor-led sessions with a focus on hands-on learning. The course takes you step-by-step from beginner through intermediate to advanced concepts in an intuitive way that will help you grasp Python’s concepts. It is offered in online and classroom modes to suit your convenience. The course teaches you everything you need for a successful career in data science – right from the fundamentals to advanced concepts.

Here are five reasons why you should learn Python for data science:

  1. Simple Syntax

While it might not seem like much at first glance, the complexity of the syntax plays a huge role in choosing to learn a language. Python’s syntax is very easy, making it possible to pick the language up very quickly and easily. The simple syntax of the language enables developers and data scientists to write minimal code quickly and effectively. This makes the program more efficient and lightweight. Its learning curve is not nearly as steep as that of Java or C++. Its simplicity makes Python a great language to start with if you are just getting started with programming. This is just one of the many reasons data scientists prefer approaching Data Science using Python.

  1. Scalable

Python is more scalable than other language used in data science like R. It is also much faster than Stata and MATLAB. Many major applications of today, including YouTube, have adopted Python for its scalability due to the flexibility it offers with problem-solving. Data scientists have used the language to build many successful applications. Python processes are lightweight and execute faster than other languages.

  1. Dedicated Data Science Libraries

There is a plethora of different libraries available for Python and the number continues to grow. Python also has several libraries and resources dedicated to data science, many of which are updated regularly. While there were some hiccups that some developers faced with these libraries, the bugs have been squashed over the years.

Here’s a list of some libraries for analytical data operations:

NumPy is used to perform scientific computing operations. It features several advanced mathematical functions that help operate on multi-dimensional arrays and matrices. It makes it possible to perform various operations with these arrays and matrices.

SciPy works in conjunction with NumPy arrays to allow numerical integration. The library contains tools to help solve linear algebra, probability, integral calculus, and other mathematical problems. It has been tested rigorously and underwent many changes and bug fixes.

Pandas is used in conjunction with NumPy to deliver data structures to modify tables and time series. It contains several tools for analysis, and one particular feature that stands out of the package is its ability to translate complex operations into one or two simple commands. It contains many methods for filtering, grouping, and combining data. There have been several new releases of the library, with each one having new features and fewer bugs.

Matplotlib enables you to quickly and easily create two-dimensional graphs, histograms, and non-Cartesian coordinates graphs. A lot of other plotting libraries are also intended to work alongside Matplotlib.

  1. Community

The Python ecosystem has allowed it to come this far, and the community played a very active role in helping it accomplish that. Several volunteers are working to develop libraries for Python as Python has extended its support to the data science community. This will result in the development of advanced tools that will make data science that much more efficient. Having an active community is vital because they can provide solutions to your coding problems and thus provide you with more resources that would make your program more efficient. There are also resources like StackOverflow that can help you with anything from debugging or choosing the best library for the application.

  1. Graphics

Python allows you to visualize data easily with the help of libraries like matplotlib. Other libraries like Seaborn and ggplot are based on matplotlib. Crunching data and getting visual and informative reports are important to data scientists and these libraries help them do just that.


Whether you’re just starting to explore data science or you’re a seasoned professional, Python is an essential part of every data scientist’s toolkit. Learning the language will make you a valuable asset to any data science team. According to Glassdoor, the average salary of a data scientist in the US is $120,000. Many professionals who earn this much or more are well-versed with Python.