Business Analytics gives valuable insights into data and helps to make informed decisions about future business strategies. However, handling tons of data is a daunting task and requires help from technology. Notably, among the most used programming languages for business analytics applications, Python ranks high.
In fact, Python for business analytics has become increasingly popular in recent years. Its ecosystem has grown tremendously, with several libraries and frameworks available to build statistical models and data analysis pipelines.
Its immense popularity in machine learning applications using proven methods like scikit-learn and TensorFlow has made Python for Business Analytics a commendable choice.
But why exactly Python? Well, to start with, it is among the top 5 programming languages used worldwide and holds popularity of 48.24%, as per Statista.
The stats-based infographic below offers insights into the most used programming languages worldwide.
Source: Statista – Top Programming Languages
However, popularity is not the sole reason behind using Python. Moreover, JavaScript and HTML/CSS rank higher than Python in terms of popularity. So, what makes Python for business analytics a demanding pair?
Here are eight reasons why Python should be your first choice for business analytics.
- Easy to Learn
- Widely Available Libraries and Frameworks
- Compatible with Top DBMS Models
- Vectorize Computations
- Open-Source Software Programming Language
- Rich Ecosystem
- Consistent with Big-Data Applications
- Offers Significant Automatization
Let’s explore these reasons in detail.
Top 8 Reasons Why Python for Business Analytics is an Excellent Choice
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Easy to Learn
Business analysts don’t have much time to speed up new programming languages. So, the programming language needs to be easy enough that you can take on any project and not feel like it’s holding you back. While no business analyst is required to learn the language themself, it’s easier to control the project when you have first-hand exposure to the programming language.
Many analyst-level employees are non-technical and need simple instructions to use data analytics software. Python fits those needs perfectly because its syntax is concise, and it has an active community (which means a lot of resources and help). More libraries are available than in other languages, which makes learning easier.
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Widely Available Libraries and Frameworks
Several Python tools and libraries are used to perform data analysis. Python Developers can do data exploration with Pandas library. They can achieve in-depth statistical analysis and predictive modeling with SciPy or StatsModels packages.
Big data processing/storage can be done using tools like NumPy, Pandas, MongoDB, and Hadoop/Spark through its support for PySpark API. And lastly, machine learning frameworks like scikit-learn, TensorFlow, Theano, and Apache Spark MLlib cover most of the use cases that organizations face.
The availability of such wide-scale libraries and frameworks makes it easier to build complex applications in a shorter time. Additionally, these tools are open source and can be used without license fees. It means organizations can use them freely without worrying about additional costs.
Furthermore, Python has a large developer community that continually adds new features to its core library and many third-party packages. It ensures that support will always be available when you need help with your data analysis or machine learning projects. And since most of these packages are open source, they’re free to use and easy to customize according to your needs.
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Compatible with Top DBMS Models
While discussing Python for business analytics, you cannot ignore the significance of DBMS models necessary to handle tons of data. The infographic image below offers insights into the top DBMS models worldwide.
Source: Statista – Top DBMS Models
High compatibility with tons of DBMS models is one of Python’s biggest strengths and a great advantage to data scientists. Since Python is compatible with top database management systems, it is applicable as a backend in almost any industry that uses databases.
For example, if you need to use relational databases such as PostgreSQL or MySQL, you’ll have no trouble using Python and Postgres or MySQL because they work very well together. Since most businesses operate on these models, it’s an excellent way to streamline your company’s processes and get new insights into your existing tools.
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Vectorize Computations
Analysis code should not be slow. With Pandas, NumPy, and other Python packages, it is possible to handle data sizes that are not very common in R or Excel. Moreover, Pandas operations often automatically vectorize computations, which also speeds things up a lot (e.g., summing values of an array instead of looping over all elements).
If you know what you are doing and need speed from your analysis code, a simple solution might be to rewrite it in Cython and execute it via PyPy.
But beware: careful benchmarking and optimization work may be required for the best results! Connecting with an experienced Python web development company can ensure a simple and effective solution.
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Open-Source Software Programming Language
Python is a renowned open-source programming language. An open-source software refers to software whose source code is freely available and may be redistributed and modified.
Open-source software has many benefits, and the notables ones include the following:
- Lower cost of development,
- Higher quality of the product as with collective community efforts at improving security and functionality,
- Freedom from vendor lock-in due to free availability of alternatives, and
- More flexibility in strategy and a more incredible opportunity for innovation.
Free, open-source alternatives are available for all major enterprise applications used today. Since open-source languages are based on open standards, developers can use them across industries without difficulty.
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Rich Ecosystem
Source: SynergyTop
Using Python for business analytics makes sense because of its rich ecosystem. You can always find a library to tackle your programming challenges. For example, if you need support in plotting charts, you might use matplotlib or seaborn to display graphics.
When it comes to deploying your software, there are options like Fabric, Docker, and Heroku to help you get it online quickly and easily. Business analytics tools have specific needs which most languages don’t meet natively out of the box.
An obvious example here would be Excel data manipulation tools (NumPy & Pandas). Some other libraries worth checking out include SciPy, Seaborn, Bokeh, and NLTK, which are some of my favorites.
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Consistent with Big-Data Applications
Handling tons of data requires a highly scalable language. Python’s architecture and design make it straightforward to develop applications that can handle Big Data with straightforwardness.
It has several libraries like Pandas, NumPy, SciPy, Matplotlib, etc., which are specifically designed for high-performance computing and have support from an active community of developers. It makes it one of the most sought-after languages for developing Big-Data applications.
Another significant benefit of using Python for Big Data is its use on multiple platforms. You can run it on various operating systems like Windows, Linux, Mac OS X, etc. It also offers a wide range of hardware and software platforms that make it compatible with other languages.
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Offers Significant Automatization
You may not think it makes sense to utilize a programming language for data analysis or reporting. However, once developers have written code that extracts and analyzes data, the next step is to get those results into a format that decision-makers can send.
That way, they can do something with them, like perform another type of analysis, make a chart, and make a presentation. Writing code means developers can ensure an advanced dashboard giving you control over how results are formatted. You can also use it in other reports/analyses.
Thanks to Python, it offers a significant level of automatization, which helps in replicability. It makes a more straightforward and quick data analysis possible because Python supports several tools like pandas, NumPy, scipy, etc.
Final Words
Python’s flexibility allows you to choose from multiple libraries and frameworks according to your needs. It gives you more control over how your application functions while allowing you to spend less time worrying about syntax details that have no impact on your application’s performance or functionality.
Moreover, Python gives you more features to explore data with speed and accuracy. Thanks to Python, one can quickly get rid of writing programs using C/C++ or Java by using its functionalities directly from the code editor without compilation. One can easily use these libraries for exploring, analyzing, and visualizing data.
Also Read: How Is Python The Finest Choice For Web Development?
