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In today’s extremely competitive global economy, the way business is done has dramatically changed

Data Science is a multidisciplinary field that uses scientific inference and mathematical algorithms to extract meaningful knowledge and insights from a large amount of structured and unstructured data. These algorithms are implemented via computer programs which are usually run on powerful hardware since it requires a significant amount of processing. Data Science is a combination of statistical mathematics,  machine learning, data analysis and visualization, domain knowledge and computer science.

As it is apparent from the name, the most important component of Data Science is “Data” itself. No amount of algorithmic computation can draw meaningful insights from improper data. Data science involves various types of data, for example, image data, text data, video data, time-dependent data, etc.

Data Science is such a field which can be applied to almost every industry to solve complex problems. Every company applies Data Science to a different application with the view of solving a different problem. Some companies completely depend upon Data Science and Machine Learning techniques to solve a certain set of problems, which, otherwise, could not have been solved. Some of such applications of Data Science and the companies behind them are listed below.

  1. Internet Search Results (Google): When a user searches for something on Google, complex Machine Learning algorithms determine which are the most relevant results for the search term(s). These algorithms help to rank pages such that the most relevant information is provided to the user at the click of a button.
  2. Recommendation Engine (Spotify): Spotify is a music streaming service which is quite popular for its ability to recommend music as per the taste of the user. This is a very good example of Data Science at play. Spotify’s algorithms use the data generated by each user over time to learn the user’s taste in music and recommend him/her with similar music in the future. This allows the company to attract more users since it is more convenient for the user to use Spotify as it does not demand much attention.
  3. Intelligent Digital Assistants (Google Assistant): Google Assistant, similar to other voice or text-based digital assistants (also known as chatbots) is one example of advanced Machine Learning algorithms put to use. These algorithms are able to convert the speech of a person (even with different accents and languages) to text, understand the context of the text/command and provide relevant information or perform a desired task, all just by speaking to the device.
  4. Autonomous Driving Vehicle (Waymo): Autonomous Driving vehicles are one of the bleeding edge of technology. Companies like Waymo uses high-resolution cameras and LIDARs to capture live video and 3D maps of the surrounding in order to feed that through Machine Learning algorithms which assist in autonomously driving the car. Here, the data is the videos and 3D maps captured by the sensors.
  5. Spam Filter (Gmail): Another key application of Data Science which we use in our day-to-day life is the spam filters in our emails. These filters automatically separate the spam emails from the rest, effectively giving the user a much cleaner email experience. Just like the other applications, Data Science is the key building block here.
  6. Abusive Content and Hate Speech Filter (Facebook): Similar to the spam filter, Facebook and other social media platforms use Data Science and Machine Learning algorithms to filter out abusive and age-restricted content from the unintended audience.
  7. Robotics (Boston Dynamics): A key component of Data Science is Machine Learning, which is exactly what fuels most of the robotics operations. Companies like Boston Dynamics are at the forefront of the robotics industry and develop autonomous robots that are capable of humanoid movements and actions.
  8. Automatic Piracy Detection (YouTube): Most videos that are uploaded to YouTube are original content created by content creators. However, quite often, pirated and copied videos are also uploaded to YouTube, which is against their policy. Due to the sheer volume of daily uploads, it is not possible to manually detect and take down such pirated videos. This is where Data Science is used to automatically detect pirated videos and remove them from the platform.