Python can Predict Outcomes Based on Historical Data

by HSG on Sep 21, 2017 in Articles for Software Fans

Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.

Python's ecosystems enable faster implementation of analytics initiatives. As a software developing company you can reorganize engineers who have python experience, or those who are experienced in the field of sales and marketing be certified do basic tasks within analytics processes. Therefore, it is important for the robust business to hire python specialist because his expertise could also restructure if the analytics flops.

Python is highly flexible. As a general programming language, python is a powerful analytics tool which can solve problems faced by customers and internal organization problems. The other big data languages such as R is less flexible because it was created for a specialized purpose. Flexibility has attracted more python developers thus python professionals are many in the industry. Many experienced python experts are available for hire.

In data, analytics has a massive tool known as deep learning that is gaining popularity at a faster rate. With time, it has become a useful tool which can help business predict outcomes, based on data generated in the enterprises. When you want a tool for deep learning, python is the best alternative. Python has more libraries compared to other languages used for analytics.

Python is highly accessible, and this reason makes it most preferred first learning language to programming in the computer science department around the world. It is important for your company to embrace the power of python. Just like we have noted from Redmonk's data, python is gaining its ground. The finding shows that python is well documented compared to other languages such as R. in the coming year's python will become more abundant which will reduce the expenses of contracting a python software engineer. Consequently, it will be more affordable to implement analytics using python.

While predicting the outcome of the historical data, you will need to work with teams. Python is commonly employed in different divisions, for instance, operations, logistics, marketing, and sale for various purposes. Its diverse functions within an organization assure that professionals have been exposed to python at some point.

The experience of python professionals within an organization validates creating an analytic project from start to finish efficient with Python expert. If other sections within the firm have handled python in the past, it is effective to combine project implementation lifecycle. Thus, teams from different divisions can easily work together to analyze historical data.

Yes, Python can be used to predict the outcome on historical data. Python is flexible enough to become the best alternative for production use. Python works seamlessly for when your firm needs to integrate data analytics into a web application. Furthermore, unlike analytics languages such as R, Python provide uniformity with software development processes.

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