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Data science is helping lenders cut down on risk by utilizing segmentation with predictive analytics. This allows companies to not only predict the behavior of their clients (for instance, how likely they are to pay off a credit card), but can take data from market behavior to forecast trends. Even data collected from search engine trends and use can be turned into valuable insights for understanding market movements.

Micro-loans are changing the financial landscape of areas suffering from poor conditions and poor credit. Without much collateral to offer, many entrepreneurs in such regions find it difficult to impossible to obtain a loan. Using novel credit scoring systems, many involving the online presence of applicants, people are able to connect with lenders and start with micro-loans to build their business.


With IoT devices that are easily installed into automobiles, data can be collected in real-time, and analyzed to create quick and accurate assessments of a customer’s driving habits. This helps insurance providers understand the risk potential of their clients by presenting findings from the composed data.

These IoT devices also help insurance providers help their policy holders stay safe as they can alert drivers to various risks such as accidents, traffic delays, and poor weather conditions. Helping their clients avoid these travel hindrances not only helps the driver, but safer drivers means less accidents and therefore less payouts the company must make.

Health Care

The cure for cancer is closer than ever, with treatments improving exponentially with technology. We've finally reached a point where data science developments, and the diverse human experts behind them, are helping in early detection efforts that are saving lives.

Treating a patient can be tedious. There are many unknown biological factors to consider, and the results of many, many studies on which to base a diagnosis and recommended treatment plan, and it has been a widely accepted practice to more forward with drugs that only work a percentage of the time. Data science, however, offers a more customized health care regimen that can be personalized to meet the needs of each patient as an individual – rather than a statistic. It takes into account your medical history along with trials, digital patient networks, research, and direct observation to give each patient the best chance of recovery.


Create a luxurious, bespoke experience for your customers with data science! We can create easy to use databases that will track, organize, and analyze client’s preferences and previous experiences to save their partialities and make recommendations for the ultimate in hospitality. Hotels can now even use data science to recommend the best suited local activities and businesses based on predictions that account for taste, time, and popularity.


RFID readers have been integral in manufacturing, allowing objects and machines to track and manage processes from production to delivery in real-time. Now, with IoT, you can take it a step further and train machines to talk to each other. This vastly decreases unexpected interruption for industrial equipment, and allows machines to self-adjust based on data received from satellites, sensors, and IoT objects.

Now that more and more of the manufacturing process is being run by advanced machines and automated robotic mechanical solutions, sensors imbedded within them allow manufacturers to utilize data taken from the factory floor to create specialized software. These software solutions analyze the data they are fed to discover inefficiencies in production and help find solutions to increase both quality and speed and overall improve the existing processes.



By now, everyone knows the amazing insights that can be gleamed from data science applications, but the available functions in the world of advertising are taking it to new levels. By combining the powers of data science with the art of human sales it is possible to greatly improve the landscape of advertising and publishing in a way that benefits both the brands and publishers. Data can be used to understand connections between companies, and where they are funneling their media money to better understand prospects before they pitch. This in-depth understanding of the market allows for the development of a more personalized sales approach, in addition to more quickly discovering matching brands and agencies - thereby reducing time and improving quality.

Social Media

Machine learning is changing the way we process linguistic data. The introduction of Sentiment Analysis, a natural language processing technique, has illuminated the trends of online customer interactions for businesses by letting them know how people are feeling about them. Not only can this technique give you an idea of how many "positive" versus "negative" reactions and comments you're receiving, but can also discover key words and phrases and build a model around their corresponding sentiment type using text analysis. This allows companies to track in real time the impact their brand is having and how people are reacting, and allowing them to respond in turn.

The key to user engagement is aligning content with personal interests, says Erran Berger, VP of engineering at LinkedIn. But, as the amount of data traded on sites such as LinkedIn, Facebook, and Instagram, the more difficult it is to create the personalized experience such websites want to create for the people who frequent them. With the combined powers of data and machine learning predictions, that task has become less difficult. They take into account data found on profiles and from activity such as “likes,” to streamline your experience and order feed to meet your preferences – whether you realize them or not.

Did you know that it’s not just what you post, but also when, where, and how that’s affecting your online presence. By processing the massive amounts of data available regarding user behavior and interactions across social media platforms, important trends emerge regarding the impact of your digital brand. Data science helps illuminate these trends, and turn them into real solutions.

Real Estate

Are you ready for a consumer-to-consumer application to make your next move a breeze? Considering factors such as location, livability, and other characteristics of the property is nothing new, but the involvement of data science takes it to a new level by also considering information such as your commute time and cost of move to provide an even more in-depth house-hunting experience that proves just how helpful harnessing data can be.

Buying real estate can be overwhelming, and the overwhelming number of homes can mean the perfect property is lost among the noise. But when real estate agents team up with data scientists, the entire process is improved for both buyers and sellers. Using data available across multiple sources, and then narrowing down results based on the needs of the client, delivers a manageable set of options with the greatest quality of relevancy.


Data Science is shaping how you shop. As internet commerce grows in popularity, companies such as Amazon and even Facebook Marketplace are collecting data as you browse, taking note of what you linger on, what you add to your shopping cart, and which products make it through checkout. This data not only helps them track trends and plan for future products to feature, but to also make personalized recommendations to customers based on their developed preference profiles – often leading to more frequent web store visits, and certainly more purchases in a manner that benefits everyone involved.

Deep learning has grown in popularity to assist in the rise of on-demand grocery delivery services. By creating and combining an embedding for the products, one for the shoppers, and yet another for the stores, data scientists are able to predict which product a shopper will pick up next with 60% accuracy based on store location and previously selected item. This means more efficient shopping, meaning faster delivery, quicker processing, and more profits that rises with the satisfaction of customers.

Sales & Marketing

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Ride share apps are growing in popularity, and that wouldn’t be possible without data science. These apps overlay data consisting of everything from traffic patterns to nearby users and their own intended destinations to match up the perfect carpool, thereby lowering the cost were an individual to ride solo.

Companies like Waze also use data science to create an efficient transportation experience by using data to find the most efficient routes. Data also helps ensure that their employees experience a good work environment by allowing both rider and driver to rate one another easily and anonymously to create a profile and rating for each, allowing transparency and safety for both parties.