problems solved by data science

Data at Work: 3 Real-World Problems Solved by Data Science

By Patrick Smith

At first glance, data science seems to be just another business buzzword — something abstract and ill-defined. While data can, in fact, be both of these things, it’s anything but a buzzword. Data science and its applications have been steadily changing the way we do business and live our day-to-day lives — and considering that 90% of all of the world’s data has been created in the past few years, there’s a lot of growth ahead of this exciting field.

While traditional statistics and data analysis have always focused on using data to explain and predict, data science takes this further . It uses data to learn — constructing algorithms and programs that collect from various sources and apply hybrids of mathematical and computer science methods to derive deeper actionable insights. Whereas traditional analysis uses structured data sets, data science dares to ask further questions, looking at unstructured “big data” derived from millions of sources and nontraditional mediums such as text, video, and images. This allows companies to make better decisions based on its customer data.

So how is this all manifesting in the market? Here, we look at three real-world examples of how data science drives business innovation across various industries and solves complex problems.

AirBnB uses data science and advanced analytics to help renters set their prices.

The vacation broker Airbnb has always been a business informed by data. From understanding the demographics of renters to predicting availability and prices, Airbnb is a prime example of how the tech industry is leveraging data science. In fact, the company even has an entire section of its blog dedicated to the groundbreaking work its data team is doing. The team understands the importance of data quality, data mining, and data analytics.

Faced with a large amount of data from customers, hosts, locations, and demand for rentals, Airbnb went about using data science to create a dynamic pricing system called Aerosolve, which has since been released as an open-source resource.

Using a machine learning algorithm, Aerosolve’s predictive model takes the optimal price for a rental based on its location, time of year, and a variety of other attributes. For Airbnb hosts, it revolutionized how rental owners can best set their prices in the market and maximize returns. And that’s not all — Airbnb’s data scientists have also recently launched Airflow , an open source workflow management platform for building data pipelines to ingest data easily.

There’s no shortage of need for these solutions, and for the foreseeable future, we’ll be seeing explosive growth in data science solutions for technology companies like Airbnb

Data science revolutionizes sports analytics.

After the 2003 book Moneyball (and corresponding 2011 film) became successful, sports teams have realized that their data is more powerful than they had ever imagined. Over the past few years, the Strategic Innovations Group at the consulting firm Booz Allen Hamilton has been doing just that — working to transform the way teams utilize data.

Using data science and machine learning tactics, Booz Allen’s team developed an application for MLB coaches to predict any pitcher’s throw with up to 75% accuracy, changing the way that teams prepare for a game. Looking at all pitchers who had thrown more than 1,000 pitches, the team developed a model that considers current at-bat statistics, in-game situations, and generic pitching measures to predict the next pitch.

Now, before a game starts, a coach can analyze an opposing team’s lineup and run predictive models to anticipate how to structure his plays to add capability for his team and change how the game itself is played.

Nonprofits solve the most pressing social issues with data.

Founded in 2014, San Francisco-based Bayes Impact is a group of experienced data scientists assisting nonprofits in tackling some of the world’s heaviest data challenges. Since it’s founding, Bayes has helped the U.S. Department of Health make better matches between organ donors and those who need transplants, worked with the Michael J. Fox Foundation to develop better data science methods for Parkinson’s research, and created methods to help detect fraud in microfinance. Bayes is also developing a model to help the City of San Francisco harness data science to optimize essential services like emergency response rates. Through organizations like Bayes, data science has the power to make a significant social impact in our data-driven world.

So, what does all of this mean for the job market? With the ever-increasing need for data-driven solutions across every industry, the demand for data scientists has outpaced supply. According to a recent study by McKinsey , “By 2018, the United States will face a shortage of up to 190,000 data scientists with advanced training in statistics and machine learning as well as 1.5 million managers and analysts with enough proficiency in statistics to use big data effectively.”

It’s no wonder, then, that data scientists are one of the few non-managerial positions included by Glassdoor in the top 25 highest-paying jobs in America . Plus, in their annual list of the 25 Best Jobs in America , Glassdoor rated data scientists as No. 1 one due to the high median base salary, a number of openings, and career opportunity.

Two things are certain: There is a serious need for data scientists in today’s job market, and no shortage of life-changing problems that data wranglers can solve.

Data at Work: 3 Real-World Problems Solved by Data Science

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