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Data science has been proven useful in about every industry.Data Science Uses

Data science helps us achieve some major goals that either were notpossible or required a great deal more time and energy just a few years ago, such as:

Additionally, here are a few examples of how businesses are using data science to innovate in their sectors, create new products and make the world around them even more efficient.

Data science has led to a number of breakthroughs in the healthcare industry. With a vast network of data now available via everything from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new ways to understand disease, practice preventive medicine, diagnose diseases fasterand explore new treatment options.

Tesla, Ford and Volkswagen are all implementing predictive analytics in their new wave of autonomous vehicles. These cars use thousands of tiny cameras and sensors to relay information in real-time. Using machine learning, predictive analytics and data science, self-driving cars canadjust to speed limits, avoid dangerous lane changes and even take passengers on the quickest route.

UPS turns to data science to maximize efficiency, both internally and along its delivery routes. The companys On-road Integrated Optimization and Navigation (ORION) tool uses data science-backed statistical modeling and algorithms that create optimal routes for delivery drivers based on weather, traffic, construction, etc. Its estimated that data science is saving the logistics company up to 39 million gallons of fuel and more than 100 million delivery miles each year.

Do you ever wonder how Spotify just seems to recommend that perfect song you're in the mood for? Or how Netflix knows just what shows youll love to binge? Using data science, the music streaming giant can carefully curate lists of songs based onthe music genre or band youre currently into. Really into cooking lately? Netflixs data aggregator will recognize your need for culinary inspiration and recommend pertinent shows from its vast collection.

Machine learning and data science have saved the financial industry millions of dollars, and unquantifiable amounts of time. For example, JP Morgans Contract Intelligence (COiN) platform uses Natural Language Processing (NLP) to process and extract vital data from about 12,000 commercial credit agreements a year. Thanks to data science, what would take around 360,000 manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal are investing heavily in data science to create machine learning tools that quickly detect and prevent fraudulent activities.

Data science is useful in every industry, but it may be the most important in cybersecurity. International cybersecurity firm Kaspersky is using data science and machine learning to detect over 360,000 new samples of malware on a daily basis. Being able to instantaneously detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.

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What is Data Science? A Complete Guide. | Built In