How Do Different Industries Use Data Science - mages

Studies have shown that an overwhelming majority of large organizations are investing in Big Data and AI projects.
This is one of the major reasons why most smaller companies are moving towards becoming data-driven and are ready to invest in data science as a whole.

Technological advancements like big data, AI and data science and analytics have made companies more productive and data-driven, leading to an eventual increase in profits.

From healthcare to finance to media, every industry is now unlocking insights into their data through data science to make more optimized and strategic decisions.

This incorporation of data science into multiple industries has produced transforming and visible results – the efficiency of operations has increased significantly.

The demand for data science and analytics professionals has risen at a commensurate rate. However, there is a difference in how each industry uses data science for its benefit.

For all aspiring Data Scientists, it is important to understand how it is used in different industries and what career opportunities are available.

Ways In Which Different Companies Use Data Science

Finance Industry

The finance industry is a data and number-driven industry where there is a constant drive for tangible improvement. Data science is applied in different fields of the finance industry including managing risk, cutting down trading costs, increasing financial security and marketing.

For instance, if you make a suspicious transaction from your bank account, the bank would then flag it as a suspicious transaction and would attempt to communicate with you to verify. AI-powered methods are used in smartly identifying potential frauds. With the help of these algorithms, fraud can be prevented by detecting any breaks present in the usual spending patterns.

Another common way in which data science is used in the finance industry is by performing real-time data analytics. With the help of real-time analysis and visualisation tools like Tableau, data is continuously generated and analysed. Due to the nature of the industry, real-time analysis is necessary to make split-second decisions.

Transportation Industry

Another important industry where Data science has transformed operations is the transportation industry. Here, the implementation of data science is focused around improving safety and reducing the cost of transportation by optimizing routes.

Much like the Financial Industry, real-time data scraping, cleaning, and analysis is necessary for Transport. Monitoring data is crucial to deal with delays, figure out patterns and causes of such delays, etc. AI-powered tools are used to optimise scheduling (with the end goal of minimising cost).

With the help of weather data and GIS, dramatic changes in weather patterns (which in turn affect scheduling) can be accounted for and managed.

Entertainment and Media

Have you ever wondered how Netflix always suggests movies and series of the genre you like? Data Science and Machine Learning Algorithms power Netflix’s (and most others’) recommendation engines.

In the entertainment industry, data science is used to collect data and analyse the same to suggest shows and movies based on your preferences, after making well-educated and science-backed methods to predict what you *might* like. However, the use of data science is not only limited to these customer retention strategies.

Other than helping streaming platforms with forecasting and data visualization techniques, data science can also help these platforms in optimizing their decision-making process. This is all done by providing them with data-rooted solutions rather than just following traditional methods of improving user experience.

Retail and eCommerce

You have searched for something on Amazon and now you see the same ad everywhere while playing a game or scrolling through social media, this is an example of recommendation engines (same basic principle as the Netflix example above). In the eCommerce or retail industry, data science is used to understand and analyse multiple attributes and correlations between products. This helps the platforms understand the taste and needs of the (potential or existing) customer. With the help of this analysis, you can see multiple product recommendations on eCommerce platforms.

On the backend, data science can also help the product team to analyse and optimise the product design process. With the gathered data, it helps you answer certain questions like what are the existing loopholes in the product? What new products must be added to the product mix?

Answering all these questions helps you strategize your product mix and make it optimal as per your audience.

Health Care Industry

One of the important transformations that data science has made includes the healthcare industry. This industry comprises a huge amount of data collected from genome sequencing, social media, mobile health devices and Electronic Health Records. This collected data is then analysed with the help of data science to make improvements in treatments and clinical care.

Other than this, different applications are created with the help of programming languages like Python and using machine learning concepts that use data from your smartphone or sensor devices to provide you with personalized care in the form of health-monitoring apps.

Technologies like AI/ML are effectively used to make drug discovery less expensive and more efficient by incorporating a predictive element into the old trial-and-error method.

Manufacturing Industry

Being one of the oldest industries in the world, the manufacturing industry has gone through multiple transformations and data science is all set to add another transformation. With data science, crucial processes like delivering the right product at the right time are transformed.

Moreover, machine learning and data science are used to reduce the risk of unplanned downtimes. Scientists implement models to gather data from sensors available on machines. This helps you predict the maintenance and plan accordingly to avoid downtime losses.


It is inevitable that data science will significantly impact every industry if it hasn’t already done so, and has already transformed the decision-making process.

If you’re keen to investigate patterns, realise (and then analyse) the “big picture” puzzles and questions we ask ourselves, and use that knowledge to improve decision making, then you’re the perfect candidate for a Data Science Course.


Need guidance or course recommendations? Let us help!

    Mages Whatsup