Posted by Python is said to be a simple, clear, and intuitive programming language. That’s why many engineers and scientists choose Python for many scientific and numeric applications. Perhaps they prefer getting into the core task quickly (e.g. finding out the effect or correlation of a variable with an output) instead of spending hundreds of hours learning the nuances of a “complex” programming language.
This allows scientists, engineers, researchers, and analysts to get into the project more quickly, thereby gaining valuable insights in the least amount of time and resources. It doesn’t mean though that Python is perfect and the ideal programming language on where to do data analysis and machine learning. Other languages such as R may have advantages and features Python does not. This article will dig deep into the reasons behind learning & practicing data analytics with python.
Python vs R
Users might have already encountered this in Stack Overflow, Reddit, Quora, and other forums and websites. They might have also searched for other programming languages because after all, learning data analytics with python or R (or any other programming language) requires several weeks and months. It’s a huge time investment and data professionals don’t want to make a mistake. To get this out of the way, just start with Python because the general skills and concepts are easily transferable to other languages. Well, in some cases users might have to adopt an entirely new way of thinking. But in general, knowing how to use Python in data analysis will bring them a long way towards solving many interesting problems.
Many say that R is specifically designed for statisticians (especially when it comes to easy and strong data visualization capabilities). It’s also relatively easy to learn especially if you’ll be using it mainly for data analysis. On the other hand, Python is somewhat flexible because it goes beyond data analysis. Many data scientists and machine learning practitioners may have chosen Python because the code they wrote can be integrated into a live and dynamic web application. Although it’s all debatable, Python is still a popular choice, especially among beginners or anyone who wants to get their feet wet fast with data analysis and machine learning. It’s relatively easy to learn and you can dive into full-time programming later on if you decide this suits you more.
Widespread Use of Python in Data Analysis
There are now many packages and tools that make the use of Python in data analysis and machine learning much easier. TensorFlow (from Google), Theano, scikit-learn, NumPy, and pandas are just some of the things that make data science faster and easier. Also, university graduates can quickly get into data science because many universities now teach introductory computer science using Python as the main programming language. The shift from computer programming and software development can occur quickly because many people already have the right foundations to start learning and applying programming to real-world data challenges. Another reason for Python’s widespread use is countless resources will tell users how to do almost anything. If users have any questions, it is likely, that someone else has already asked them and another that solved it for them (Google and Stack Overflow are your friends). This makes Python even more popular because of the availability of resources online.
Due to the ease of learning and using Python (partly due to the clarity of its syntax), professionals can focus on the more important aspects of their projects and problems. For example, they could just use NumPy, scikit-learn, and TensorFlow to quickly gain insights instead of building everything from scratch. This provides another level of clarity because professionals can focus more on the nature of the problem and its implications. They could also come up with more efficient ways of dealing with the problem instead of getting buried with the ton of info a certain programming language presents. The focus should always be on the problem and the opportunities it might introduce. It only takes one breakthrough to change our entire way of thinking about a certain challenge and data analytics with python programming might be able to help accomplish that because of its clarity and ease.
In Summation
The aforementioned points are explained to help data enthusiasts understand the involvement & importance of data analytics with python. In modern times, data leveraging with artificial intelligence & machine learning has become the go-to step for establishments looking to secure their spot in the native & global market.