Python is one of the oldest and most popular languages to be learnt and deployed with relative ease. The language has been around since the 80s and since its inception has always been the center of much attention. Thus, over the years python has become more refined and received valuable contributions from its users. The language is easy to understand and write codes with. The syntax is closer to the human tongue and the format is very easy. Python can be learnt quickly and deployed on any platform like Windows and Linux. Furthermore, the language is free and usually arrives pre-installed with Linux computers. Thanks to its easy going nature, the language can be taken up by almost anyone, belonging to a plethora of skill and experience levels. This article will evaluate the contributions of python in this era of data. And try to understand why learning python for data science is the best thing to do for someone willing to contribute.
The newfound relevance
Python was always relevant due to the possibility of becoming an expert quickly. All sectors that require code based services are keen to introduce python into their repertoire of languages. It is easy to train professionals quickly with python and deploy with relative ease. Due to the unfortunate pandemic and the subsequent lockdowns, the crunch for efficiency has increased drastically. And the dependency on data analysis is undeniably rampaging. Not utilizing data can be detrimental given the precariousness of times. But the problem was satisfying a huge gap in skill and hunger for data professionals. Python emerged as a savior. Learning python and getting to the frontlines became a breeze for the enthusiasts.
Python is welcoming
Python is under constant up-gradation and development. And thankfully the language is very much aligned with data science needs. The presence of python libraries makes things a lot easier for professionals to code quickly and make use of their time most productively by concentrating on macroscopic problems. Python comes equipped with access to a number of libraries with prewritten codes. These libraries can be used for specific data science needs and are being updated constantly by leading developers from around the world.
A resourceful community
The python community is large and consists of professionals of diverse age and experience groups. The very first python users are still active in the scene and they possess a curation of experiences that are extremely relevant. These professionals are very much active in the forums and are eager to help the newbies with all they can. Thus, a fresher starting out with python can always get all the help they need while figuring out the utility of python for data science.
Ease of machine learning development: sci-kit-learn
Developing machine learning tools using python is relatively easy due to the syntax and various other aspects of the language. Among the libraries dedicated to the development of machine learning and analytics tools, sci-kit-learn is the most prominent. This particular library consists of a plethora of efficient tools for statistical modeling, regression and classifications, and even clustering and dimensionality reduction.
Scikit learn is a library enriched with supervised and unsupervised learning algorithms. The cross-validation methods are easy to execute and deploy. The presence of a toy data seat makes it easy to be adequately aware of the features of a library. And feature extraction with the help of this library is also easy to execute.
Data collection
The training and deployment of machine learning tools in python require a lot of data for the purpose of training. MySQL connector is one such library that can help collect data from MySQL. In order to obtain data from MySQL and manipulate the same, it is essential to load them in python. MySQL connector is just the library for that purpose. After loading the data sets it is also pretty easy to convert them into pandas data frames for further manipulation of the same.
Conclusion
These intricate alignments with data science and data analytics needs have naturally made python the very first choice of many developers. The most important feature of python is the possibility of learning python quickly. And furthermore, thanks to the libraries and specific components designed to serve the data science needs python is significantly faster to use when it comes to development and training. Thus those who are looking for an easy and fast upgrade in 2022 should try learning python for data science for a smooth transition.
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