Why Python and Big Data Are Essential for Business Analysts?

The exponential growth in data complexity and volume requires highly sophisticated data processing tools like programming languages. For years, the Python programming language has served professionals in web development, data science, automated scriptwriting, system administration, and even Python for business analytics. 

It has become one of the most used programming languages globally, with over 8 million developers relying on it. With it, users in multiple fields know they’re getting a programming language that allows them to easily store, manipulate, and access data with Python data analysis tools. 

There are tons of open-source libraries and packages on Python’s large and ever-expanding ecosystem that allows corporations to harness Big Data for business analysis. This article talks about the importance of Python and Big Data and their use in business analysis.

Why is Python so Important?

Python is an all-purpose programming language used to create different types of programs, and this means that it is not specialized for a range of specific problems. Its versatility and ease have combined to make it one of the most widely used languages globally. 

In 2021, a RedMonk survey showed that Python was the 2nd most popular language developers used. You can’t simply overestimate the importance of the Python programming language in today’s world. Here are several reasons why Python is so important.

#1. Rich Standard Library

Users place more importance on Python than other programming languages because it has a rich and large standard library. Python’s library allows millions of users to select from various modules depending on their needs. Every module enables users to add functionality to the Python app without writing extra code.

#2. Python Supports Several Programming Paradigms

Python supports many programming paradigms, including fully supporting structured programming and object-oriented. The language feature of Python also supports different concepts in aspect-oriented and functional programming.

#3. It is Compatible with Major Operating Systems and Platforms

Python currently supports major operating systems meaning that users on Windows and Mac. etc., can use the app. What’s more, you can utilize Python interpreters to run codes on specific tools and platforms. An interpreted programming helps Python users run the same code on multiple platforms without needing recompilation after altering it.

#4. Massive Community Support

Python has the popularity, but it stands out even more and outlines its importance in the massive community support available, especially when users need help. There’s always a chance that someone in the Python community has had the sort of issues you encounter in your code, and you can always find help and get an already-made solution to use.

#5. Python is Useful For Everybody and in Different Fields

Python is useful for many industries like consulting, healthcare, agriculture, technology, and finance, and it helps millions of users improve their work daily. For instance, farmers can use Python’s IoT tech to manage pests and crop diseases and make farm yield predictions.

#6. It is Relatively Simple to Learn

It may not be a complete walk in the park, but it is relatively simpler. If you compare Python to other top programming languages, Python comes out on top in terms of the ease of mastering the programming language in the shortest possible time. This is all down to its relatively easy syntax and exhaustive learning resources. 

#7. Several Great Ready-Made Analytical Solutions

There are great ready-made analytical solutions and brilliant python for business analytics tools that analysts can leverage. Developers take pride in designing their solutions when they need them, but analysts already have their solutions, including ML/DL libraries.

Why is Python the Best Choice For Business Analytics Applications?

Business analytics involves collecting and analyzing data to gain valuable business insights.  Businesses are always looking for how to attract customers and monitor the behaviors of their existing customers and the success of marketing campaigns. Here is where Python for business analytics comes into play. 

Users leverage Python for business analysts to use data to draw insights into the effectiveness of their business endeavors. Newbies might wonder why most people consider Python one of the best choices for business analytics applications. Below are some reasons why people use Python for business analytics:

#1. Versatility

Python is an all-purpose computer programming language with several python data analysis tools. You can use Python to make desktop and web applications, and its versatility also extends to the area of coding for complex numeric and scientific applications.

#2. Simplicity

Python’s simplicity makes it a superb attraction for coders despite its versatility. The programming language has non-complex commands that you can write as if you’re writing in the English language, and its syntax is equally simple.

#3. Learning Resources

The availability of extensive learning resources makes Python for business analytics a no-brainer. Python is open-source and expert programmers have made valuable contributions to the wealth of existing resources, and so have analysts. So when an analyst encounters a problem, there’s a good chance that another analyst already faces such problems and might have even made documented solutions to it.

How is Python Useful For Business Analytics?

The Python programming language is arguably the most popular language for business analytics globally. Here’s a highlight of just how Python for business analytics is useful:

#1. Python is Great for Predictive Analytics and Machine Learning

One objective of business analytics is to be better prepared for future happenings by predicting what is likely to happen―this is Predictive Analytics. Machine learning is one branch of predictive analysis that utilizes efficient statistical algorithms to predict the future with the help of existing information while identifying relevant insights and relationships.

A great example of predictive analysis is the streaming giant Netflix’s recommendation engine. Python is increasingly becoming the ideal programming language for machine learning, and people use it to create models for decision trees, Bayesian networks, etc.

#2. Prescriptive Analytics and Decision Science

Users rely on python for business analytics because of the prescriptive analytics tools they can create on it. The prescriptive analysis is regarded as the final phase of business analytics. It anticipates the ‘when,’ ‘why,’ and ‘what’ regarding specific outcomes and determines what to do with the information.

Decision scientists often base their data analysis around business challenges and rely on similar tools and techniques that data scientists use. But whatever tool they use, their goal is to make insights operable so that visualizations methods and models are created to communicate these insights. For this reason, experts use python for business analyst jobs while creating valuable prescriptive solutions and tools like deep learning.

BI and Dashboards

A BI dashboard or Business Intelligence dashboard is an analysis and data visualization tool that displays the current status of key performance indicators (KPIs) and several other relevant data points and business metrics for a team, process, organization, or department.

Dashboards are a component of some BI software platforms, and users use them to present analytics information to workers and business execs. BI dashboards usually contain several data visualizations that offer businesses a combined view of crucial KPIs and business trends.

These trends help businesses with a platform for strategic planning and operational decision-making. They are interactive and enable users to access data that underlies informative graphics and charts for more analysis.

More often than not, business analysts and other users of self-service BI tools create dashboards. Organizations and companies sometimes task business intelligence team members to design dashboards.

Dashboard tools offer users a comprehensive library of icons, images, and widgets that they can add to dashboards to enhance visual appeal, tabs, pull-down menus, usability, and automated functions.

Democratizing Python For Business Analytics

Data democratization is a big discussion in business analytics, and the discussion about democratizing Python for business analytics is a significant one. Democratizing data means liberating it and getting it to the decision-makers and not just data scientists.

No industry or field is safe from digital and tech evolution disruption in today’s world. Now more than ever, the global economy sees the data business as a crucial and valuable sector. Data is what empowers global giants like Uber, Amazon, and Netflix to transform their businesses and venture into new markets. 

As the data economy grows with more large corporations relying on data to make informed decisions, organizations need business analysts. Business analysts need the best skills to analyze data and extract insights properly. They also need the most effective digital tools―this is where Python for business analytics comes into play.

However, some organizations tend to thrust their data and analytics responsibilities into the arms of a central team. While there is no problem with this approach, the long-run challenge becomes how to stay sustainable and scale properly.

There are also communication and team challenges. Besides, the lack of data literacy and inability to project the business context of data brings friction in communication, affecting business decision speed or reaction time to external and internal factors. So what should organizations do to democratize tools like python for business analytics adequately?

• The establishment of a data-driven culture where relevant parties understand the importance of data and they possess basic knowledge of data-driven outcomes
• Enabling the sharing of Python for business analytics skills, knowledge, and tools needed to consume data and support business decisions with data insights.

Working With Big Data

Supply chains, employees, finance, and marketing teams generate plenty of data daily. Big data refers to an abundance of data or large datasets that come in different forms and from diverse sources.

Most organizations now know the benefits of collecting a large volume of data. While this is great, it is not enough to collect and store it. Organizations have to use the big data they collect and try to gain insight from it.

This is why data scientists and python for business analytics are crucial in today’s digital world. They help these organizations perform big data analytics, collecting, cleaning, processing, and thoroughly analyzing large datasets to allow organizations to operationalize the data they collect. Here’s how your company can work with big data with these steps.

#1. Data Collection

Collecting data is important, and it differs from organization to organization depending on the technology at the company’s disposal. Modern technology can allow organizations to collect unstructured and structured data from mobile apps, cloud storage, IoT sensors, etc.

#2. Data Processing

After big data collection and storage, the next thing to do is process it by adequately organizing it to get accurate results on analytical queries. Organizing is especially useful if the big data is unstructured and large. Besides, since available data is growing exponentially, data processing will always be a challenge for organizations, but one with solutions.

Batch processing is one way to process data by observing large data blocks over time. Stream processing could also suffice because it looks at relatively smaller batches of data at once, and it helps shorten the delay time between data collection and analysis.

#3. Cleaning the Big Data

Whether working with big data or small data, you need to scrub your data to enhance its quality and get a more solid result. It is proper to format data correctly and eliminate or account for any redundant or irrelevant data. Know that dirty data can be misleading, obscure insights, and create a flawed perspective.

#4. Analyzing Data

Getting big data to become usable is a time-consuming effort, but once it is ready, the effects that python data analysis tools can have are massive. You can easily turn that big raw data into big insights with proper Python for business analytics and some data analysis methods like Predictive analytics, data mining, and deep learning.

These four steps can help you turn big data into valuable insights for companies to base their key decisions. Being able to analyze more data faster can give big benefits to organizations, and it will allow them to use data to answer crucial questions.

Big data analytics helps organizations put massive chunks of data in multiple formats, helping them recognize risks and opportunities from different sources. Here are some benefits of big data analytics.

• Big data analytics offers market insights, including tracking market trends and purchasing behaviors of the target audience.
• It is cost-saving and can help organizations find out ways to be efficient.
• It better understands customers’ needs and helps companies do better and more accurate product development.

Tips for Working With Big Datasets

• Keep your raw data raw and try not to manipulate it without keeping a copy
• Show your workflow always
• Visualize the information
• Record your metadata―a description of how you collected, formatted, and organized observations.
• Always use version control systems to understand precisely how files have changed over time and who made specific changes.
• Opt for automation because large datasets are too big to go through manually.
• Work in a self-contained computing environment so you can capture your environment and replicate an analysis later.

Conclusion

Python has a large community that helps users solve programming issues or errors within the software. Its simple syntax means it is easy to learn and understand, making it a top choice for programmers, data scientists, and business analysts who use python data analysis tools.