Top 10 Data Science Applications

Data science massive effect on technology trends has seen a constant growth in different industries including transportation, e-commerce, healthcare, gaming and bioinformatics in form of artificial intelligence, machine learning, deep learning and natural language processing.

Big-data Analytics

Data collection, deductions, visualization and comparisons made from analyzing large data sets is enormous. Emergence of Exabyte’s, Zettabytes and Yottabytes of data have been a challenge for most government agencies and industries who need their data managed to enhance productivity.

Some important tools used for analyzing and visualizing data are Microsoft excel, Tableau, R programming, Python, SAS, Knime, QlikView, Splunk.

Some big data technologies used to store and analyze data are as follows:

Apache Hadoop, Pig, Hive, Sqoop, NoSQL, Microsoft HDInsight, polybase and many others.

Robotics Engineering

Source: John Deere

Advancement in data science through advanced machine learning has improved the field of robotics by reducing challenges in robotics engineering – such as prediction, reasoning, computer vision, communication, bionics and behavior patterns, which results to data used in programming robots and other automated devices.

Automation has become a major part of robotics engineering with repetition of multiple tasks which involves collection of large data for improvement process been improved by data science.

Healthcare Data

Source: US hit leaders news

Data science has improved the healthcare industry through EHR (Electronic health records) to improve predictive medical care, radiology, cognitive decisions, AI-assisted robotic surgery, virtual health assistance for patient, customer support, genetics and genomics.

Image and Speech Recognition

Source: Stanford

Image recognition means identifying and detecting objects through a digital platform such as digital image or video. Data science through artificial intelligence and machine learning has improved image recognition with the use of image data sets, computer vision and image recognition software’s.

Artificial intelligence has helped in Speech Recognition with the invention of product services such as Google voice, Cortana, Siri, Amazon echo etc.

These products are built to recognize speech patterns, despite your inability to type on your device and convert voice requests to text.

Top 10 Data Science Applications

Search Engine Result Refining and Website Recommendation


Search websites such as Google, Yahoo, Bing, AOL, Ask and so on, make use of data science algorithms to deliver the best results that match our typed word in each search bar of listed websites. These search engines may display results with website recommendations based on previous search results for a user. By recommendation, companies such as Amazon, Netflix promote their products to viewers in response to data collected for user’s interest.


The gaming sector has improved so much with the help of machine learning algorithms positioning the motion gaming by analyzing subsequent moves of opponents and re-adjusting itself according to level of play amongst opponents.

Gaming companies such as Sony, Nintendo, EA Sports, Zynga, Sega, Activision-Blizzard have improved immensely the user satisfaction of their products.

Airline Routing Planning

Tremendous growth has occurred in the aviation sector with the presence of data science to decide airline passenger seat to purchase, predict flight delay, and effectively drive customer loyalty programs.

Fraud and Risk Detection

Fraud and risk detection have compelled companies to learn how to prioritize data usage by customer profiling, calculation of past expenditures, feasibility and viability studies based on previous activities to avoid risk, debt and losses annually.

Business Logistics

For a business to be successful, optimization of business data is essential. Data science play a major role in delivery and transportation logistics, vehicle telematics, intelligent and automated warehousing (amazon warehouse robots), inventory management, supply chain processes such as analyzing, counting, picking, route optimization, shipment details and transparency which lead to significantly low operational costs.

Driverless Cars

The combination between data science, Artificial Intelligence and Machine Learning has enhanced the invention of driverless cars. Two major holders of advancement in driverless cars are Google and Tesla.

Autonomous cars also known as driverless cars are data-driven. A driverless car needs information to automate commute at various locations, which has made GPS (Global Positioning Systems) use of trilateration to locate positions very vital for navigation.  Driverless cars collect data in form of real-time images and videos of traffic variations with the help of its sensors

Real life driving experience in form of data are programmed for auto-drive efficiency. The more data programmed into the car system the better its efficiency. Sensors, processors and actuators are the main hardware components of a driverless car.

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