APPLICATIONS OF DATA SCIENCE

WHERE IS DATA SCIENCE APPLIED?

INTRODUCTION:

Data scientists have changed almost every industry. In medicine, their algorithms help predict patient side effects. In sports, their models and metrics have redefined “athletic potential.” Data science applications have even tackled traffic, with route-optimizing models that capture typical rush hours and weekend lulls.

VARIOUS APPLICATIONS OF DATA SCIENCE:

Google: Machine-Learning for Metastasis

How it’s using data science: Google hasn’t abandoned applying data science to health care. In fact, the company has developed a new tool, LYNA, for identifying breast cancer tumors that metastasize to nearby lymph nodes. That can be difficult for the human eye to see, especially when the new cancer growth is small. In one trial, LYNA — short for Lymph Node Assistant —accurately identified metastatic cancer 99 percent of the time using its machine-learning algorithm. More testing is required, however, before doctors can use it in hospitals.

Clue: Predicting Periods

How it’s using data science: The popular Clue app employs data science to forecast users’ menstrual cycles and reproductive health by tracking cycle start dates, moods, stool type, hair condition and many other metrics. Behind the scenes, data scientists mine this wealth of anonymized data with tools like Python and Jupyter’s Notebook. Users are then algorithmically notified when they’re fertile, on the cusp of a period or at an elevated risk for conditions like an ectopic pregnancy.

Oncora Medical: Cancer Care Recommendations

How it’s using data science: Oncora’s software uses machine learning to create personalized recommendations for current cancer patients based on data from past ones. Their radiology team collaborated with Oncora data scientists to mine 15 years’ worth of data on diagnoses, treatment plans, outcomes and side effects from more than 50,000 cancer records. Based on this data, Oncora’s algorithm learned to suggest personalized chemotherapy and radiation regimens.

UPS: Optimizing Package Routing

How it’s using data science: UPS uses data science to optimize package transport from drop-off to delivery. Its latest platform for doing so, Network Planning Tools (NPT), incorporates machine-learning and AI to crack challenging logistics puzzles, such as how packages should be rerouted around bad weather or service bottlenecks. NPT lets engineers simulate a variety of workarounds and pick the best ones; AI also suggests routes on its own.

StreetLight Data: Traffic Patterns, and Not Just for Cars

How it’s using data science: StreetLight uses data science to model traffic patterns for cars, bikes and pedestrians on North American streets. Based on a monthly influx of trillions of data points from smartphones, in-vehicle navigation devices and more, Streetlight’s traffic maps stay up-to-date. They’re more granular than mainstream maps apps, too: they can, for instance, identify groups of commuters that use multiple transit modes to get to work, like a train followed by a scooter. The company’s maps inform various city planning enterprises, including commuter transit design.

Uber Eats: Delivering Food While It’s Hot

How it’s using data science: The data scientists at Uber Eats, Uber’s food-delivery app, have a fairly simple goal: getting hot food delivered quickly. Making that happen across the country, though, takes machine learning, advanced statistical modeling and staff meteorologists. In order to optimize the full delivery process, the team has to predict how every possible variable — from storms to holiday rushes — will impact traffic and cooking time.

RSPCT: Basketball-Coaching Sensor

How it’s using data science: RSPCT’s shooting analysis system, adopted by NBA and college teams, relies on a sensor on a basketball hoop’s rim, whose tiny camera tracks exactly when and where the ball strikes on each basket attempt. It funnels that data to a device that displays shot details in real time and generates predictive insights.

British Olympic Rowing Team: Finding The Next Redgrave

How it’s using data science: Before the 2016 Olympics in Rio, the British rowing team ramped up data collection on athletes. Their hope? That by using longitudinal weight-lifting and rowing data, biomechanics data and other physiological information, they could begin to model athlete evolution. Doing so would allow the coaches to identify a promising newbie rower — a young Steve Redgrave, say — and put him on a Redgravian training regimen that might transform him into another gold-medal-winning oarsman.

Equivant: Data-Driven Crime Predictions

How it uses data science: Widely used by the American judicial system and law enforcement, Equivant’s Northpointe software suite attempts to gauge an incarcerated person’s risk of reoffending. Its algorithms predict that risk based on a questionnaire that covers the person's employment status, education level and more. No questionnaire items explicitly address race, but according to a ProPublica analysis that was disputed by Northpointe, the Equivant algorithm pegs black people as higher recidivism risks than white people 77 percent of the time — even when they’re the same age and gender, with similar criminal records. ProPublica also found that Equivant's predictions were 60 percent accurate.

ICE: Facial Recognition in ID Databases

How it uses data science: The U.S. Immigrations and Customs Enforcement, a.k.a. ICE, has used facial recognition technology to mine driver’s license photo databases in at least two states, with the goal of deporting undocumented immigrants. The practice — which has sparked criticism from both an ethical and technological standpoint (facial recognition technology remains shaky) — falls under the umbrella of data science. Facial recognition builds on photos of faces, a.k.a raw data, with AI and machine learning capabilities.

IRS: Evading Tax Evasion

How it uses data science: Tax evasion costs the U.S. government $458 billion a year, by one estimate, so it’s no wonder the IRS has modernized its fraud-detection protocols in the digital age. To the dismay of privacy advocates, the agency has improved efficiency by constructing multidimensional taxpayer profiles from public social media data, assorted metadata, emailing analysis, electronic payment patterns and more. Based on those profiles, the agency forecasts individual tax returns; anyone with wildly different real and forecasted returns gets flagged for auditing.

Sovrn: Automated Ad Placement

How it uses data science: Sovrn brokers deals between advertisers and outlets like Bustle, ESPN and Encyclopedia Britannica. Since these deals happen millions of times a day, Sovrn has mined a lot of data for insights, which manifest in its intelligent advertising technology. Compatible with Google and Amazon’s server-to-server bidding platforms, its interface can monetize media with minimal human oversight — or, on the advertiser end, target campaigns to customers with specific intentions.

Instagram: Marketing With a Personal Touch

How it uses data science: Instagram uses data science to target its sponsored posts, which hawk everything from trendy sneakers to dubious "free watches." The company’s data scientists pull data from Instagram as well as its owner, Facebook, which has exhaustive web-tracking infrastructure and detailed information on many users, including age and education. From there, the team crafts algorithms that convert users’ likes and comments, their usage of other apps and their web history into predictions about the products they might buy.

Airbnb: Search That Highlights Hip Areas

How it uses data science: 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 with a slick hack: Today, a rental gets priority in the search rankings 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.

Tinder: The Algorithmic Matchmaker

How it uses data science: When singles match on Tinder, they can thank the company’s data scientists. A carefully-crafted algorithm works behind the scenes, boosting the probability of matches. Once upon a time, this algorithm relied on users’ Elo scores, essentially an attractiveness ranking. Now, though, it prioritizes matches between active users, users near each other and users who seem like each other’s “types” based on their swiping history.

Facebook: People You Almost Definitely Know

How it uses data science: Facebook, of course, uses data science in various ways, but one of its buzzier data-driven features is the “People You May Know” sidebar, which appears on the social network’s home screen. Often creepily prescient, it’s based on a user’s friend list, the people they’ve been tagged with in photos and where they’ve worked and gone to school. It’s also based on “really good math,” according to the Washington Post — specifically, a type of data science known as network science, which essentially forecasts the growth of a user’s social network based on the growth of similar users’ networks.

CONCLUSION:

So to sum it up, several industries like banking, transport, e-commerce, healthcare and many more are using data science to better their products. Data Science is a vast field and therefore, its applications are also enormous and diverse. Industries need data to move forward and therefore, it is an essential aspect of all the industries today. Got some applications of data science that we didn't list? Share them with us in the comments.

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