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How Python is Used in Data Analytics

Data is becoming one of the most essential aspects of running a successful business, regardless of the industry. Collecting and analyzing data is crucial for making informed decisions about how to grow your business and where to invest your resources.

The volume of data produced by businesses is also on the rise, and more tools are being developed to accurately categorize and analyze this data in a way that is both understandable and actionable. The programming language Python is one of the most popular tools for data analysts around the world. Here, we’ll break down some of the fundamental aspects of data analytics and why Python is an excellent tool for this exciting career. 

Basics of Data Analytics

Massive amounts of data are produced by people and organizations every day. Companies then store this data for analytical purposes. They use this information to make crucial decisions about things like where to target a marketing campaign or open a new location. By finding trends in existing data, data analysts help the companies they work for make smarter decisions about how to move their business forward. In a more general sense, data analysts use data to help answer questions like “why did X happen?” and “what should we do next?”

There are four types of data analytics that these professionals perform daily. These types range from least to most complex and can be done concurrently depending on the information that is being sought. 

Descriptive Analytics is the first and most commonly used type. It looks at historical data to locate patterns and then illustrates these patterns in appealing charts and graphs. Information like “how many followers did we gain after posting X content” can be found using descriptive statistics. 

Diagnostic Analytics is used to find out why a specific result occurred. For example, if a company like Blue Apron noticed an increase in cancellations, they would perform diagnostic analyses to try and find out why.

Predictive Analytics is needed when we want to know what might happen. Sticking with our Blue Apron example, once the company finds out some of the most common reasons that people are canceling services, predictive analytics could help address what could happen if certain changes were made. For example: What could happen if we lowered the price of services? What would the outcome be if we offered more vegetarian options? These questions could be answered using predictive analytic techniques. 

Prescriptive Analytics helps a business try to understand what they should do next. Utilizing artificial intelligence, this tool attempts to make the most educated guess possible with the data available. If Blue Apron decides to offer additional vegetarian meals, prescriptive analytics could be completed to confirm that this is the most cost-effective option for the company.

Why do Data Analysts Love Python?

Now that you know a bit more about the basic responsibilities of a data analyst, we can turn our attention to just a few of the characteristics within Python that make it a great fit for data analytics.


Python was developed to be easy to read and easy to learn. This comes in handy for data analysts who are trained in mathematics and statistics but lack years of experience in coding; they can still take advantage of Python's analytical capabilities. 

Collection of Libraries

Python boasts a collection of powerful libraries available for its users, especially for those who use it for data analysis. Libraries are made up of selections of specific code to assist with common tasks. They help to simplify and automate more complex tasks without sacrificing accuracy. 

Built-in Analytical Scripts

Data can be difficult for a computer to manage and process. Some of Python’s built-in tools help to make everything more manageable. These tools allow for easier correlation of information and identification of patterns, which help generate more accurate observations about the data as a whole. 


Being able to organize and locate patterns in data is an important piece of the data analysis puzzle, but it’s not the whole picture. Visual representations of data are required to help explain these conclusions to people who often have less experience with data than the analysts themselves. Python’s ability to generate appealing graphics is a crucial component to making data understandable.

To stay competitive, businesses need to make quick and smart decisions based on the insights their data provides. Python’s ability to meet these needs by efficiently managing, analyzing, and visualizing large amounts of data makes it revered by data analysts across nearly every industry. 

Learn Python with Career Centers

Whether you’re curious about learning Python by itself or integrating it into a data analytics profession, Career Centers has a host of data analytics courses available. These courses can be taken online or in person at its Manhattan campus and include one free retake of your class to make sure you get the most out of your investment. Career Centers has a Microsoft Excel Bootcamp, a Power BI Bootcamp, and a Tableau Bootcamp. Proficiency in any or all of these tools is critical for any budding analyst. 

Noble Desktop, a partner program of Career Centers, also offers many Python courses to choose from. Noble offers a 12-hour Intro to Python Programming course available which was designed specifically for beginners. If you’re looking for something a little more comprehensive, you might consider taking the Python Programming Bootcamp. This course is also intended for beginners, but it covers more than an introductory course would. Also available with Noble Desktop is a Data Analytics Certificate, which helps prepare students for a career by developing a portfolio of data visualizations and statistical analyses. 

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