Python Development
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Python for Business Analytics Applications

By Hari KrishnaJuly 20, 2020, 3 p.m. Application development company
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A guide on how to outsource python projects to India

A guide on how to outsource python projects to India

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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.

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The business analytics stream is all about collecting and analyzing data to gain business insights. Using data to draw relevant conclusions about the efficiency of business operations, how to attract new customers, how to monitor customer behavior, marketing campaign success, and product usage trends, among others, is the work of every business analyst. The data helps to identify and optimize future outcomes such as tapping new revenue streams and streamlining processes for the business. Business analytics is a versatile tool for any industry: global technology business, nonprofits, education, etc.

Business Analytics Trends

Business Analytics Trends

As Business analytics is a rapidly evolving field today, experts are pushing the analytics capabilities further by developing new and powerful tools that have immense capabilities.


Advancements and improvements in the field of AI & ML are proving to be hugely beneficial to business analytics. Capability to analyze complex datasets, learning the insights quickly, and recommend suitable ‘next’ steps are all being made possible with AI & ML techniques. These acts were manually done and were cumbersome in the past. 

Big Data

Along with the digitization of every space in today’s times, comes a huge amount of user data. This data is turning out to be one of the best and most potent resources for the world of business and its improvement. Businesses are now trying to capitalize on the Big Data that is available by gaining valuable insights about both existing and potential customers. 

Read also: How Business Intelligence turns your data into Business opportunity

Neural Networks

Artificial neural networks are inspired by the brain’s neural networks and developed to leverage the computing power of vast interconnected computer systems. They are designed to process information quickly and automatically perform new tasks. They are designed to make predictions with unparalleled accuracy.

Deep Learning

Deep learning, much like neural networks, imitates the human brain to extract relevant patterns from available data. As a subset of machine learning, deep learning algorithms enable nonlinear processing of the data resulting in speedy analysis of complex data. Coding languages as Analytics Tools Excel was being used to meet business analytics needs of the past. However, it is not versatile enough to meet the demands of modern business. It is not scalable and was built for times when the datasets were small. Coding languages of modern times such as R, SQL and Python are many times more data fluent than their counterparts of the past.

Whereas R offers advanced options for statistical operations and SQL is ideal for creating an organization’s data structures, Python is the best option when it comes to data visualization and forming progressive machine learning applications. Despite its qualification as a traditional object-oriented language/tool, Python has risen in stature as the most powerful data analytics tool with the rising popularity of libraries such as pandas, NumPy, and scikit- learn, among others.

Read also: 5 real-life revolutionary use cases of Predictive Analysis

Python and Business Analytics – The Best Fit

Python and Business Analytics

For organizations and data analysts spending most of their time in Excel, it is a good idea to shift to Python. Why Python when any programming language can serve the purpose? 

For IT buffs, they know Python is the best language that can be used for automation scripts, web development, data science, and system administration applications. Python also allows users to access, store, and manipulate data. This has made Python useful for coding applications for different verticals and domains: finance, healthcare, consulting, RPA, artificial intelligence, and technology. 

Here are a few reasons why you can shift to Python for coding business analytics applications.


Python is a general-purpose programming language and this means that it can be used to make both web and desktop applications. The language also finds use in coding for scientific and complex numeric applications. This proves its versatility.


Python language has a simple syntax and commands can be written as in the English language. This is a great attraction for coders.

Availability of learning resources

Python is an open-source language and many expert programmers have made precious contributions to the existing resources. Therefore, when a programmer runs into a problem, there are high chances that someone else had already encountered the same challenge. Python is well documented and there are plenty of resources for learning. For coders, help is always available around the corner and are likely to find a ready-to- solution quickly. 

Ready to build your business analytics solution in Python.
Availability of outstanding off-the-shelf analytical tools

Unlike developers who choose to design new tools, analysts are known to work with numerous Python libraries that offer already-designed brilliant analytical tools. As an example, there are many ML and DL tools that are ready-made and can be used just as they
are. This open-source coding system of Python helps to drastically improve work outcomes. 

Ability to Automate and Replicate

Ability to Automate and Replicate

Business analysts do not fancy manually repeating complex analysis tasks. They would prefer to delegate these tasks to dedicated helpers. This is where Python steps in. Python helps to automate and replicate processes with ease and in the least amount of time and is being heavily used by automation systems such as that of ProAI, for strategic planning and financial analysis.  Python applications can be effectively used for jobs like web scraping or collecting online data, handling text errors when merging different datasets, and repeating complex analysis tasks several times.  Python scripts are simple and immensely readable and enable everyone in the team to stay on the same step. 

Data too big to fit into Excel sheets

Such data, that have a million rows of information (SKU details, customer information, etc.), can be easily analyzed using Python routines. 

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Advanced modeling requirements

Business insights that are too complex to be unraveled manually can be studied with the help
of ML capabilities of Python.

  • Price forecast with the help of econometrical modeling
  • Market segmentation using clusterization algorithms
  • Product classification with tree-based algorithms
  • Product price elasticity estimates

Python is the language of choice in creating advanced flexible models that these situations demand. 

Evaluation of Metrics (Descriptive Analytics)

Evaluation of Metrics

Evaluation of business metrics to understand trends is termed descriptive analytics and this is a job usually performed by data analysts in a business organization. Analysts can perform this activity efficiently using Python.

Python’s ability to access, categorize and display data is unparalleled. Profiling the data, manipulating data with the help of libraries (Pandas), create suggestive visualizations using Matplotlib, and streamlining workflows, all can be executed with ease and efficiency.

Exploratory data analysis or EDA helps the business to keenly observe data before making any assumptions. EDA ensures helps analysts to produce valid results that can be applied for
desired outcomes and goals for the business. 

Forecasting Performance and Outcomes (Predictive Analysis)

The next important job for any analyst is to be able to predict what will happen for the business in the future. Here, streamlined statistical models and algorithms find use. Forecasting the future based on the current data and information available help to identify important relationships and deep insights into where the business is headed. 

Machine learning assumes importance here and Python is surely the language that can be used to code applications such as decision trees and Bayesian networks that prove helpful. TensorFlow (Google) is a popular Python library that allows business analysts and coders to access machine learning algorithms (both supervised and unsupervised). 

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Making Time-Bound Key Decisions (Prescriptive Analysis)

Anticipating what, when and why certain outcomes are likely to occur and what actions are to be taken using that information forms the basis of the science of prescriptive analysis. Decision scientists work much like data scientists and make use of the same tools andtechniques. These professionals create deep-learning tools which are designed to use neural networks that optimize the predicted outcomes.

  All of the above paragraphs lend enough credence to the fact that Python has turned out to be one of the most popular languages used for coding business analytics applications. Today, Python has emerged as a potential data processing and management solution for creating applications that help businesses to make sense of AI & ML data, neural networks, deep learning and Big Data in a jiffy.

Why Choose SayOne
At SayOne Technologies, a premier application development company in the USA, we have expert Python developers who can put together any business analytics application according to your need. If you want to have quick meaningful insights into your business data, get in touch with us for a free consultation today.

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Python is a versatile programming language that plays a pivotal role in business analytics. It offers powerful libraries and tools that allow professionals to manipulate, analyze, visualize, and interpret data effectively. Python's flexibility, readability, and extensive community support make it an ideal choice for various business analytics applications.

Python can be applied in business analytics across various domains. It can help in data preprocessing, predictive modeling, statistical analysis, market segmentation, customer behavior analysis, and more. Python's libraries like pandas, numpy, scikit-learn, and matplotlib provide tools to perform data manipulation, machine learning, and visualization tasks efficiently.

Yes, Python is suitable for businesses of all sizes. Small businesses can leverage Python's simplicity and cost-effectiveness to gain insights from their data. Large corporations benefit from Python's scalability, which allows them to process and analyze vast amounts of data. Python's broad ecosystem ensures that solutions can be tailored to meet the specific needs of any business, regardless of its size.

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