Top 20 best applications of data science, using real-world examples

Data science and applications of data science

Before discussing applications of data science, let’s discuss data science. What is data science, and what are its application examples and uses in the real world?

Data science is the in-depth study of large amounts of data and involves extracting some meaning from raw, structured, and unstructured data. Data processing is used to extract meaningful data from large amounts of data. This process can be done using statistical techniques, algorithms, scientific methods, and various technologies. Various tools and techniques are used to extract meaningful data from raw data. Data science is also called the future of artificial intelligence.

For example, Jaggrup loves reading books, but whenever he wants to buy a book, there are so many options in front of him that he always gets confused about which book to buy. This data science technique is useful. When you open Amazon, you’ll see product recommendations based on your past data. If you choose one of them, you will be encouraged to purchase these books along with this set, as this set is the most purchased. Therefore, recommending all the products and displaying a set of books purchased in bulk is one of the examples of data science.

Real-world Applications of Data Science

1. Search Engine

The most useful application of data science is search engines. As you know, when we search something on the Internet, we mainly use search engines like Google, Yahoo, DuckDuckGo and Bing. Therefore, data science is used to speed up searches.

For example, if you search “Data Structures and Algorithms Course,” Internet Explorer will display the first link to the Geeks for Geeks course. This is because the Geeks for Geeks website is most frequently visited to get information about data structure courses and computer-related topics. Therefore, this analysis is done using data science to get the most visited web links.

 2. In transit

Data science is also advancing in areas such as transportation, such as driverless cars, in real time. With the help of driverless cars, it is easy to reduce the number of accidents.

For example, in a self-driving car, training data is fed into an algorithm, which learns to deal with speed limits and various situations on highways, busy roads, narrow roads, etc. with the help of data science techniques. Analysis will be done. while driving, etc.

 3. Financial sector

Data science plays an important role in the financial industry. The problem of fraud and risk of loss are always present in the financial industry. Therefore, the financial industry needs to automate loss-risk analysis for companies to make strategic decisions. The financial industry is also using data science analysis tools to predict the future. This allows companies to estimate the customer’s lifetime value and stock market movements.

For example, data science is a major part of the stock market. In the stock market, data science is used to study past behavior using historical data, and the goal is to study future outcomes. Data is analyzed to predict future stock prices according to a set schedule.

E-Commerce- Applications of Data Science 

Once upon a time, all the people of a particular city used to shop in the same mall. This mall had a physical location with several indoor fountains, a jewelry store, and possibly a body shop. Now, residents of the same city can shop in their own personal digital mall, also known as the Internet. Online retailers often automatically adjust their web storefronts based on the data profiles of their visitors. This includes adjusting the page layout and customizing featured products. Some stores may also adjust prices based on what consumers are willing to pay. This is called individual pricing. Even websites that don’t sell anything have created targeted ads. Here are some examples of companies using data science to automatically personalize your online shopping experience.

E-commerce websites like Amazon, Flipkart, etc. use data science to improve the user experience with personalized recommendations.

For example, when you search for something on an e-commerce website, you will see suggestions similar to your interests based on historical data. You’ll also see recommendations based on the most purchased products, the most rated products, the most searched products, and more. All this is done in the following way: Data Science Support.

4. Creating ads

Sovereign brokers deals between advertisers and outlets like Bustle, ESPN and Encyclopædia Britannica. Because these transactions occur millions of times a day, Sovrn mines massive amounts of data for insights that are then transformed into intelligent advertising technology. Its interface, which is compatible with Google and Amazon’s server-to-server bidding platforms, allows you to monetize media with minimal human oversight. Additionally, advertisers can target their campaigns to customers with specific intent. 

5. Curating vacation rentals

Data science helped Airbnb totally revamp its search function. Once upon a time, it prioritized top-rated vacation rentals that were located a certain distance from a city’s center. That meant users could always find beautiful rentals, but not always in cool neighborhoods. Engineers solved that issue by prioritizing the search rankings of a rental if it’s in an area that has a high density of Airbnb bookings. There’s still breathing room for quirkiness in the algorithm, too, so cities don’t dominate towns and users can stumble on the occasional rental treehouse.

In Health care, applications of data science

In the healthcare industry, data science acts as a boon. Data science is used for:

• Tumor detection.
• Drug discovery.
• Medical image analysis.
• Virtual Medical Bot.
• Genetics and genomics.
• Predictive modeling for diagnosis, etc.

The healthcare sector in particular has greatly benefited from data science applications.

6. Medical Image Analysis

Steps such as tumor, artery stenosis and organ contour detection use various methods and frameworks, such as MapReduce, to find optimal parameters for tasks such as lung texture classification. It applies machine learning techniques, support vector machines (SVM), content-based medical image indexing, and wavelet analysis for solid texture classification.

7. Genetics and Genomics

Data science applications also enable advanced levels of treatment personalization through genetics and genomics research. The goal is to understand how DNA affects our health and to find individual biological connections between genetics, disease, and drug responses. Data science techniques allow the integration of different types of data and genomic data in disease research, leading to a deeper understanding of genetic issues in response to specific drugs and diseases. The availability of reliable individual genomic data will lead to a deeper understanding of human DNA. Improved genetic risk prediction is a major step toward more personalized care.

8. Drug development

The drug discovery process is extremely complex and involves multiple disciplines. Great ideas are often limited by billions of tests and huge financial and time expenditures. It takes an average of 12 years to make a formal application.

Data science applications and machine learning algorithms simplify and shorten this process, adding perspective to each step, from initial screening of drug compounds to predicting success rates based on biological factors. Such algorithms can use advanced mathematical modeling and simulations instead of “laboratory experiments” to predict how compounds will act in the body. The idea behind computational drug discovery is to create computer model simulations of biologically relevant networks to simplify predictions of future outcomes with high accuracy.

9. Customer Support for Patients: virtual assistance

Clinical process optimization is often based on the concept that patients do not actually need to visit a doctor in person. Mobile applications can provide more effective solutions by bringing doctors closer to the patient.

AI-powered mobile apps can provide basic health care assistance, typically through chatbots. Simply describe your symptoms or ask a question, and you will receive important information about your medical condition from a wide network of connections between symptoms and causes. The app can remind you to take your medicine on time and schedule a doctor’s appointment if needed.

This approach promotes a healthy lifestyle by encouraging patients to make healthy decisions, saving time waiting in line for appointments, and allowing doctors to focus on more important matters.

10. Medicine and drug development

The process of making the medicine is extremely difficult and time-consuming and must be done with great discipline, as it can be life-threatening. Without data science, the development of new drugs would take a lot of time, resources and money, but with the help of data science, it becomes easily possible to predict the success rate based on biological data and other factors. Algorithms based on data science predict how it will react in the human body without laboratory experiments.

Applications of data science are used in various fields, which are given below

11. Image Recognition

Currently, data science is also used for image recognition. For example, if you upload an image to Facebook with your friends, Facebook will suggest people tag in the photo. This is done with the help of machine learning and data science. Once an image is recognized, data from the person’s Facebook friends is analyzed, and after analysis, if the face in the photo matches someone else’s profile, Facebook will suggest automatic tagging.

12. Targeting Recommendations

Targeted recommendations are the most important application of data science. No matter what a user searches for on the internet, they will see tons of posts everywhere. This can be explained well using an example. Let’s say you want a mobile phone, search for it on Google, and then change your mind about buying it offline. In the real world, data science helps companies that pay for mobile advertising. So everywhere on the internet, on social media, websites and apps, I see recommendations for the phones I search for. Therefore, you will shop online.

13. Airline route planning

The airline industry is also growing with the help of data science, making it easier to predict flight delays. Additionally, just as an aircraft may have a direct route from Delhi to the US or may have stopovers en route after reaching the destination, it can also be used to decide whether to land directly at the destination or Have to stop on the way. helpful.

14. Data Science in Sports

In most of the games where the user plays against an opponent, i.e. a computer opponent, machine learning as well as data science concepts are used to improve the performance of the computer with the help of past data. There are many sports that use data science concepts, including chess and EA Sports.

15. In delivery logistics

Various logistics companies, like DHL, FedEx, etc., are taking advantage of data science. Data science helps these companies find the best route to ship their products, the best time for delivery, the best mode of transportation to reach their destination, and much more.

16. Autocomplete

The autocomplete feature is an important part of data science because it allows users to simply type a few letters or words and have the line autocomplete. Google Mail uses the data science concept of autocomplete feature, which is an efficient option to autocomplete entire lines when writing a formal email to someone. Autocomplete features are also widely used in social media search engines and various apps.

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17. Fraud and risk detection

Early applications of data science were in finance. Businesses were tired of bad debts and losses year after year. However, they had a large amount of data collected during the initial paperwork while approving the loan. They decided to bring in data scientists to prevent losses.

Over the past few years, banking companies have learned how to divide and conquer data to analyze risk and probability of default through customer profiling, past spending and other important variables. This also helped in promoting banking products based on the purchasing power of the customer.

In Government Applications of Data Science

Although some consider the US government to be “overly online”, government agencies have access to vast amounts of data. The agency not only maintains its own database of ID photos, fingerprints, and phone activity but can also obtain a warrant to obtain data from any data warehouse in the United States. For example, investigators often access Google’s warehouses to obtain lists of equipment used at crime scenes.

Although many view such activity as an invasion of privacy, the United States has minimal privacy regulations, and government data will not be lost anytime in the near future. Here are some ways government agencies can apply data science to their vast data stores.

18. Predicting recidivism among incarcerated people

Widely used in the US justice system and law enforcement, Equivent’s North Point software suite attempts to measure the risk of recidivism among incarcerated individuals. Its algorithms predict that risk based on a questionnaire that includes a person’s employment status, education level, and more. Although none of the survey items explicitly mentioned race, a comparable algorithm found that black people had a higher risk of recidivism than white people in 77 percent of cases, as challenged by North Point. Even though they are the same age and gender, they have similar criminal histories, according to a ProPublica analysis. ProPublica also found Equivalent’s predictions to be 71 percent accurate.

19. Databases and Mining Using Facial Recognition Software

U.S. Immigration and Customs Enforcement is using facial recognition technology to mine driver’s license photo databases with the goal of deporting illegal immigrants. This practice has been criticized from both an ethical and technical perspective (facial recognition technology remains unstable) and falls into the category of data science. Facial recognition is built on facial images, aka raw data, with AI and machine learning capabilities.

20. Detecting tax fraud

By some estimates, the U.S. government loses $1 trillion per year due to tax evasion, so it’s no surprise that the IRS has modernized its fraud detection protocols for the digital age. To the chagrin of privacy advocates, the agency has improved efficiency by creating multidimensional taxpayer profiles from public social media data, various metadata, email analysis, electronic payment patterns and more. Based on these profiles, government agencies predict the tax returns of individuals. Those whose actual and expected returns differ significantly are flagged for audit.

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