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10 Data Science Applications in Real Life


Trinh Nguyen

Aug 04, 2022

Artificial intelligence and big data are the two key areas that will drive the global data science market over the next years. It’s because more and more companies look to streamline business operations by harnessing their internal as well as external data.

As the amount of data being generated by humans, machines, and sensors increases rapidly, so does the need for advanced techniques to collect, understand, process, and visualize data. The research across academia and industry has contributed immensely to this field, where machine learning algorithms are designed to automate these processes.

Data science has numerous applications, such as text mining, genomic analysis, and financial analytics. However, only a few applications have already reached mainstream usage. This article aims to identify top data science applications with the greatest potential to achieve widespread adoption in the future.

Let’s dive in!

Top 10 Data Science Applications 

  1. Automotive & Transportation
  2. Healthcare
  3. Financial – Fraud Detection
  4. Insurance
  5. Image Recognition & Speech Recognition
  6. Manufacturing
  7. Retail & E-commerce
  8. Telecommunication & Media
  9. Travel & Hospitality
  10. Education

1. Automotive & Transportation 

The transportation industry is one of the most exponentially growing areas where data science has made a significant impact. And it will continue to do so in the coming years. Today’s automobiles are not only equipped with simple sensors to monitor fuel consumption, engine performance, or tire pressure, but also advanced technologies such as motorized driver seats and self-parking features.

The opportunity in the transportation industry is tremendous. Companies will be able to do much more when they understand their customers’ dealerships better with the help of data science.

For instance, we can see the uses of data science in autonomous vehicles. They require real-time processing and decision-making based on sensory data generated from different sensors installed in the vehicle. Companies like Tesla and Uber are already using data algorithms to create driverless cars that reduce accidents and save lives.

Additionally, data science can be used to design intelligent traffic systems that use predictive analytics to reduce travel time and increase driver safety.

2. Data Science Applications in Healthcare  


Data science is being applied in many different healthcare eras, ranging from clinical decision support systems to wearable devices. Healthcare can greatly benefit from data analysis in terms of research, patient monitoring, and diagnostics.

For instance, doctors use data science to get accurate diagnoses for cancer patients based on their medical history, family health background, etc. In addition, they can also prescribe the most suitable treatment for a particular patient at a given time.

Real-time Tracking 

Data science has impacted the real-time tracking of contagious diseases such as the flu and identifying their primary cause. In addition, researchers use data science to track the use of antibiotics for various bacterial infections.

To make these advancements, hospitals need more data than what they typically generate internally. This requires hospitals to share their data with third-party technology companies like Google, Microsoft & IBM. These companies will use data science to identify trends and patterns in areas such as drug prescription and disease surveillance.

Medical Image Analysis 

Healthcare organizations are becoming increasingly aware of the potential value of data science and have begun working with experienced data scientists. Not only is the healthcare industry investing heavily in related technologies to enhance clinical decision-making but also for understanding biological processes.

For example, image processing algorithms assist medical image analysis in detecting early signs of cancer or heart disease which otherwise would not be possible with the naked eye.

3. Data Science Applications in Financial – Fraud Detection 

The financial sector has made tremendous use of data science algorithms to gain actionable insights, leading to profitable investment opportunities. It enables banks and financial institutions to tap into information sources. They were never thought to be helpful for this kind of analysis.

Data science solutions are successfully utilized in the banking, trading, and investment industries, where advanced models can predict stock prices with great precision over a specific time horizon. It has also detected new security threats and risk factors and improved many processes such as client onboarding, cross-selling, and fraud detection.

The objective is to reduce “fraud” and ensure that transactions are legitimate. Financial institutions use data science in the following ways:

Detection of Suspicious Activities

With the increasing amount of money transacted through banks and other financial institutions, it has become crucial for them to detect and prevent fraudulent activities such as check tampering, credit card fraud, etc.

Customer Profiling  

Financial institutions are keen to learn about their customer data and keep up with the latest market trends. For this purpose, they use data science to identify new markets that can be tapped into for business growth.

In addition, banks also use data science to understand the behavior of their current customers and recommend new products to improve their consumer base.

Customer Engagement

Many banks use data science algorithms to track customer behavior on an ongoing basis and provide personalized services.

For example, they use this data to send personalized marketing emails suggesting financial products that might interest their customers.

4. Data Science Applications in Insurance 

Data science supports insurance companies in predicting the chances of a claim being filed by a particular customer. This allows them to set rates for customers based on their future claims records.

For instance, if a person has an accident and files a claim within two months from the purchase of the policy, there is a high probability that the person will file another claim in the near future using heuristics or predictive models. Using such techniques, insurance companies increase their ROIs, and customers also get improved service.

Fraud Detection 

Similar to the banking industry, insurance companies need to maximize profits and minimize losses. The critical difference is that insurance businesses deal with less tangible assets such as life and health. Hence, they need tools to extract insights from data sources that are more complex.

Customer Profiles 

Insurance organizations use advanced analytic models to form profiles of their customers to customize insurance coverage.

These models also use data science for underwriting applications which decide the type and amount of insurance premiums that need to be charged.

Customer Engagement 

Insurance providers collect hundreds of data points on each customer, including age, health status, credit score, etc.

They use this data to identify eligible customers for a particular insurance plan and send them personalized offers.

5. Image Recognition & Speech Recognition 


Image recognition and speech recognition are some of the cutting-edge technologies implemented in various applications. The main goal is to use data science to process images or audio files using computer vision algorithms to identify people, objects, text, etc.

Image Recognition 

Image recognition is being used for content-enabled websites and enables customers to search for things they are looking for on the internet using images. It is also helpful in visual search, face detection, photo tagging, etc.

Besides, machine learning algorithms are used to process data collected from CCTV cameras. They can identify certain patterns that might be useful in investigation or help in finding criminals through image recognition.

Speech Recognition

Speech recognition is an area where data science is being used extensively. This technology is used in several domains like customer service, call centers, navigation tools for the visually impaired, etc.

For example, voice-based search queries have increased exponentially while text-based web searches have steadily decreased. This means that search engines have started understanding human speech and returning relevant results to their queries in the form of voice output.

6. Data Science Applications in Manufacturing 

Data science algorithms are applied to various industrial tasks, including predictive maintenance and condition monitoring. This helps businesses make practical decisions regarding product design and production processes.

Predictive Maintenance 

The biggest challenge in predictive maintenance is that there can be multiple machine breakdown outcomes based on the available data. Data science helps predict faults using supervised or unsupervised learning tools to provide early warning signs for breakdowns.

Condition Monitoring & Cost Optimization 

Data science algorithms are also being used to monitor various parameters of a production process and cause-and-effect analysis. This helps companies reduce downtime, optimize the cost of production and improve the overall effectiveness of the manufacturing process.

7. Data Science Applications in Retail & E-commerce 

The retail and e-commerce industry uses data science techniques to process orders and keep track of product inventory. Data science techniques can collect all transaction history, shopping patterns, and customers’ preferences. Using this data, companies can increase their ROI and reduce operational costs.

Online retailers use algorithms to design even more targeted ads for promoting a product by analyzing a person’s search history and online behavior.

8. Telecommunications & Media 

The telecommunications industry is a big user of data and companies are looking for ways to gather information about customers and their preferences. This allows telecom companies to offer better services such as targeted marketing and customized discounts based on previous buying behavior.

The media industry has also started using data science tools such as audience insights and sentiment analysis to understand their consumers better.

9. Data Science Applications in Travel & Hospitality 


The travel industry is also one of the growing sectors where data science has started to impact customer service improvement, identifying fraud risks and other security issues for travelers and employees.

For instance, companies like Marriott use biometrics to identify guests who are banned from entering different countries or staying at their hotels.

Airline Route Planning 

Data science has been used to find the most suitable routes and flight schedules based on historical and real-time demand and supply situations.

Airlines and airports use data science to better understand the travel patterns of their passengers and identify potential issues in real-time. This will allow airlines to optimize flight timings and ticket pricing based on demand.

Hotel Booking  

Using big data techniques, hotels can gather information about prospects, including when they plan a trip, how long they want to stay, and what type of room they would like to book. Using this data, hotels can increase prices during peak seasons and offer off-season discounts.

In addition, hotels can use machine learning methods to predict the number of rooms that would be booked on a given day. This has reduced hotel cancellations and increased customer satisfaction, leading to customer lifetime value.

For example, Expedia can give accurate hotel availability on any given day without requiring confirmation. This shows that machine learning can reduce the risk of overbooking and help hotels get more revenue with less overhead.

10. Data Science Applications in Education 

The education sector is also one of the most significant users of data science applications and machine learning, including data analytics tools to gather information about students’ performance.

Personalized Lessons 

The education industry is also leveraging data analytics to better understand student behavior and improve academics. There has been a lot of progress in personalized learning. Students having difficulty with a subject can be guided through personalized lessons using data science concepts and insights.

Data science methods are used to analyze student performance data like scores, grades, etc. Using this data, school administrators can identify areas where students are struggling and put in place measures to help them improve over time.

Better Course Content 

Even teachers are using data science applications to better their teaching methods. Schools are implementing big data tools in the classroom for this purpose.

For example, teachers can gather insights into their students’ responses to the course material using natural language processing. This allows them to improve future lessons and make adjustments based on student performance.

Technology offers new opportunities for teachers by providing them with insights into how students react to different teaching styles and adjust their instructions accordingly. This is one of the most critical applications that will help transform the education system in a big way by improving student engagement and assisting companies in designing better course content.


Data science applications have expanded beyond the confines of business and technology, with some of the most critical sectors such as transportation, education, retail & e-commerce, telecommunications & media, among others incorporating it into their operations.

While technology plays a significant role in how companies leverage big data for decision-making, the focus is shifting more toward developing new Internet of Things (IoT) devices to help collect and analyze data. The technology itself is still very nascent, but we are seeing a lot more experimentation across different sectors which eventually will lead to an explosion in data science applications.

You may also find interest in neural networks, an essential part of deep learning. Learn about what neural networks are and how they can be applied in the real world here.