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Why is python important for business analytics

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Vismaya C,

Python is all over the place, and it's finding application in the domain of web development, data science, RPA, artificial intelligence, and much more. Almost every industry right from healthcare, retail, finance, to manufacturing is using Python applications in one or the other form to improve their business efficiency. Its ability to store, access, and manipulate data has made it a darling for data science applications. The flexibility to access data and the simple coding syntax has made it one of the most popular programming language for business analytics applications. 

Why is Python important for modern business?

Python is replacing excel spreadsheets, and it is primarily due to the inability of MS-excel to provide the scalability that modern business needs. Excel serves its purpose as long as 

  • the datasets are small

  • we do not need multi-layer processing and analysis

  • real-time information is not needed

It is clear as crystal that in the present data-driven business ecosystem, the above expectations fall way off the mark. Python has emerged as a potential data processing and management solution for creating applications that help businesses to make sense of their data.

If you spend a considerable amount of your daily working hours on excel, then it will be very wise for you to turn to Python for your data application. 

How is Python useful for Business Analytics?

Business Intelligence (BI) tools

BI aims to help businesses draw insights from their past data to make better decisions for the future. BI tools are often built on Python to access, classify, and process data to derive useful outcomes. The BI dashboards mostly built on Python creates a visual interface for the business to define and track metrics and KPIs. Unlike in the case of excel spreadsheets, you do not have to hover across various documents to observe their business data; BI dashboards give a comprehensive view of your overall business.

Predictive Analytics

Predictive Analytics is the branch of data science and machine learning, where the data of past events is analyzed to predict future outcomes. Python is the go-to language for machine learning applications, and many of the machine learning algorithms based on decision trees, bayesian networks, etc. are created using Python. Google's TensorFlow is a highly popular open-source library of supervised and unsupervised machine learning algorithms. Data scientists can access this library to fetch algorithms and use them for predictive and other data analytics applications.

Prescriptive Analytics

Prescriptive Analytics is a subsidiary of BI. It is the science of anticipating what outcome will occur when it will occur and why it will occur and what possibly can your business do with this information. It uses your business data and applies it to the decision-making process to help you make data-driven insightful decisions. Python is used to build Prescriptive Analytics tools that run through your business data and creates models and visualization methods to communicate the information and help you improve your decision-making prowess. Deep Learning is one such example of Prescriptive Analytics.

Python is Mainstreaming Business Analytics

It is a widely accepted fact that Python is an extremely user-friendly programming language for data analytics operation. With data becoming the epi-center of every business operation, there is no doubt that Python will emerge as a monopoly in the coming days for business analytics tools development. Python has a wide range of open-source packages and libraries, thus creating an ecosystem where it will find a vital role to play in the field of business analytics and machine learning.

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