10 Real-World Data Science Applications

Not only is data science a highly paid field in India, but it also allows you to work on projects in various industries, from education to healthcare, shipping logistics to marketing. This guide will explore how data science applies to ten industries, often paired with machine learning and artificial intelligence. 

10 real-world data science applications

Data science is a broad term that could refer to many different activities. For example, artificial intelligence (AI) is inextricably linked to data science. AI acts as a powerful tool for data science, and AI developers need to use data science to train and develop deep learning models. However, data scientists can also work in education, aviation, logistics, finance, marketing, security, and healthcare, to name a few. It’s likely you also use data science in your day-to-day life. Take a look at ten examples of data science applications in the real world. 

1. Education

You can find data science examples in education systems in many different ways. First, teachers use data to understand their students’ performance and look for areas where the class could use additional support. Similarly, schools can analyse their teachers' performance to discover where the educators can use more support. In both cases, you can also use data science to measure the difference you make with these interventions to understand better whether they work. 

Educators can also use data science to improve their curriculum. By pairing assessment data from teachers and students, the institution can adjust its curriculum to reflect the strengths of its educators and the needs of the students. Data science tools, such as those that provide data visualisation, make it easier to work with educational data and analyse it for meaningful insights. 

2. Airline industry

Airlines are another example of the many data science uses in the real world. The airline industry uses data science to make flying safer and more efficient and to help the airline make more money by predicting delays, competitive discounts, and ticket pricing at different times of the year. Data science helps make flying safer because analysts can look at data from the past to set the best airplane routes for the future, which can also help determine when the best time to refuel airplanes is. This kind of advanced planning can also help limit the fuel planes require by looking at the most fuel-efficient routes. 

Airlines can also use data to improve customer service by offering a faster and safer experience at the airport. Additionally, data science helps airlines understand how to price tickets during busier seasons or times, using historical trends to predict trends in the future. 

3. Delivery logistics

For logistics companies, data science makes it easier to analyse data from freight types, transportation methods, and other aspects of the supply chain. Using data science tools, you can optimise shipping routes for variables like the weather or other incidents that prevent the supply chain from running smoothly. This information can save your company money and help you look for new markets or revenue sources. 

4. Banking and finance

Financial institutions use data science in many ways, from analysing asset performance to minimising risk. You can use data science tools to analyse the past performance of financial products, like stocks or managed funds, to make predictions about how those assets will perform, providing valuable data to drive decisions. 

When it comes to minimising risk, data science is a powerful tool for financial professionals to gauge the creditworthiness of borrowers or other companies or look for fraudulent activity. Financial institutions can take this one step further and use data science to provide personalised customer service for their clients by using their data to inform their user profiles. 

5. Product and website recommendation systems

Companies like Netflix and Amazon use recommendation systems to suggest additional content users might like. This technology uses data science to analyse user behaviour, such as the terms they search for, what links they click on, what movies they watch, and so on. Understanding what the user is interested in, the recommendation system can compare it to other users who like similar things or suggest items similar to those the user wanted. Search engines work similarly and also require the use of data science.  

6. Fraud and risk detection

As mentioned above, financial institutions can use data science to prevent fraudulent purchases. However, you can also use data science to prevent fraud in many other industries. Computer network security professionals can use data science to look for suspicious patterns that might indicate a security threat. When cybercriminals strike, data science helps security professionals retrace their digital steps, look for clues, or alert the public about what to watch out for. 

7. Healthcare

Medical professionals use data science at all ends of the healthcare spectrum. Data science is important in developing new drugs, understanding new treatments, and capturing and analysing medical imagery such as X-rays. Doctors use data science to gather more information about their patients' health, giving them more data to make informed diagnoses. Healthcare facilities also use data science tools to manage patient data. 

8. Targeted advertising

In marketing, data science gives you tools to create personalised advertising and marketing campaigns designed to speak directly to your customer base. First, marketing professionals can use data science to analyse the website and social media behaviour to create profiles of their customers. Then, they can use data science to deliver their marketing campaign to a filtered group most interested in engaging with their brand. 

9. Augmented reality

Data science is an important component of artificial intelligence used to create augmented reality or overlay digital information within a physical world. For example, you might play a game using augmented reality on your phone or use an app to project an image of an item you want to purchase so you can see if it fits into your room. This technology depends on data science to train the machine learning algorithms to function appropriately in any given environment. 

10. Virtual assistants

One example of how anyone can use data science, no matter what industry you work in, is with a virtual assistant like Alexa or Siri. This technology uses a deep neural network with natural language processing to hear and understand your verbal request, execute the command, and respond to you verbally. This speech recognition technology uses data science to function. 

Learn more with Coursera. 

To build your foundation of data science skills, consider the IBM Data Science Professional Certificate on Coursera. This 10-course series, which takes five months to complete, will help you prepare for a career as a data scientist.