Reasons to Learn Python for Data Science

Python for data science is slightly different from practicing typical programming. More precisely, Python for data science is an intermediate concept. Before hiring them for lucrative and full-time jobs, every company requires such proficiency from potential data scientists. Do you know? More than 69% of data scientists and machine learning experts leverage Python in their daily life. That’s not all, however. Python’s popularity is skyrocketing in the data science landscape.

There are no other coding platforms, but Python is extensively leveraged in data science projects. Although R and Scala have some hold in the data science horizon, they could not exceed Python’s fanbase. Similarly, many programming languages are available; some are even primitive, like Java. We don’t consider any of them for data science workloads.

You might be wondering why data science uses Python, after all? So, why did the data scientists choose Python? There are multiple reasons which we will explore in the following article. After reading the post, you can make better decisions on whether a data science with Python course will be helpful for you or not.

Let’s get started, shall we?

  1. Minimal Learning Curve

Coding, especially for a newbie, can be frightening. Python, on the other hand, is an outlier. Python is a superior language choice for data scientists to understand. It has straightforward syntax and vocabulary, making it comparatively easy to learn compared to more sophisticated languages such as C, C++, and Java.

It’s an ideal way for children to learn how to code. For non-coders, there are a plethora of low-cost or no-cost options to get started with Python.

Python is a beautiful coding language to learn if you want to go into data science because it will get integrated into your tool belt quickly and painlessly. For beginners, learning data science with Python can be an easy solution.

  1. Easily readable

Python’s syntax is clean and easy, and it closely resembles English, so everything you create will be understood by you and many others, even if they aren’t Pythonistas.

If you want to propel a career in data science, consider readability as a vital component of whatever language you choose. Python is popular among data scientists because it is simple to learn. You may also quickly study Python code examples and understand what they are attempting to do.

You’ll read and share a lot of code with your coworkers. Python makes this simple.

  1. Python is in-demand

If you learn Python, you’ll join many others who have done so. It’s one of the most extensively used data science languages (and elsewhere). According to TIOBE’s 2022 index, it is the world’s most widely implemented language. And in the field of data science, it retains the topmost position, surpassing the R programming language.

Python is used by a lot of firms to create frameworks and projects. Tensorflow, which is based on Python, was built by Google, and Facebook and Netflix are increasingly using it in their data science initiatives.

If you want to work in data science, you’ll need to know at least a little Python. Fortunately, learning is enjoyable!

  1. Massive Python community

One of the significant advantages of studying Python for data science is that you will gain access to a fantastic community of Pythonistas and will be able to join them.

Python has been around for three decades, and because it is simple to learn and use, it is relevant to a wide range of people and businesses. As a result, a large and active community of Python fans is eager to share their knowledge, answer your questions, fix your code, and debate new ideas. You can find them almost anywhere – Reddit has a particularly dynamic community, but you can also discover Python-related Discord groups.

The reason that learning python is such a good choice for data science is that learning any language is difficult, especially when you’re under professional duress. It’s made more accessible by communities like those that have sprouted up around Python.

  1. A suite of essential libraries

Python is a fantastic data science language in and of itself. There are libraries, in addition to the basic syntax, easy vocab, readability, and community. Libraries like Pandas, statsmodels, NumPy, SciPy, and Scikit-Learn are widely popular in the data science community.

Data science activities are made considerably easier with ecosystems like SciPy. Many standard data science needs are addressed by SciPy, including handling data structures, analyzing complicated networks, and machine learning methods and toolkits. Python data science libraries are widely used and constantly growing.

Python data science libraries are widely used and constantly growing. The exciting part is that as more Pythonistas join the community and contribute, new Python packages for data science roll out. Keras, for example, is a deep learning package with a minimalist design that rolled out in 2015. It’s been an essential part of the Python library ecosystem since then.

  1. Understand Data Science fundamentals with Python

Even though Python has an almost infinite variety of applications, there is a lot of overlap between learning Python and studying data science. By following these simple tutorials, you may quickly grasp the fundamentals of data science using Python. Data scientists use Python for retrieving, cleaning, visualizing, and developing models, so if you want to learn data science using Python, that’s where you should start.

  1. More job prospects

If you learn Python for data science, your skills will be more than sufficient to help you find work in other areas of computer science. Python is more solid than any professional path because it has been around for thirty years and is continually reinventing itself to be useful for new occupations and careers. It’s possible that the future of data science is in doubt or that your career aspirations have shifted. In any case, understanding Python will offer you an advantage.

Wrapping Up!

Python is here to stay among data scientists for a prolonged period. So, learning about the programming language and practicing the concepts will give you an edge in the industry. Go for learning Python if you wish to pursue data science in the future.

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