Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, whether it is structured or unstructured. It is the art of uncovering the insights and trends that are hiding behind data. It’s when you translate data into a story, so you use the storytelling to generate insights. And with these insights, you can make strategic choices for a company or organization. The very exercise of going through analyzing data, trying to get some answers from it, is data science.
Machine Learning is likely the connection between data science and artificial intelligence since machine learning is the process of learning from data over time. However, it’s not the only thing connecting those two together. But, machine learning is the branch of AI that works best with data science
An example of this is Facebook’s facial recognition system which, over time, gathers a lot of data about existing users and applies the same techniques for facial recognition with new users. Another example is Google’s self-driving cars which gather data from its surroundings in real time and processes collected information to make intelligent decisions on the road. Some of the applications of data science are as listed below:
Search engines (Google, Bing, Yahoo, etc) make use of data science algorithms to deliver the best result for our searched query in a fraction of seconds. Considering the fact that, Google processes more than 20 petabytes of data every day. Had there been no data science, Google wouldn’t have been the ‘Google’ we know today.
Starting from the display banners on various websites to the digital billboards at the airports – almost all of them are decided by using data science algorithms. This is the reason why digital ads have been able to get a lot higher CTR than traditional advertisements. They can be targeted based on a user’s past behaviour.
A lot of companies have fervidly used this engine/system to promote their products/suggestions in accordance with user’s interest and relevance of information. Internet giants like Amazon, Twitter, Google Play, Netflix, Linkedin, imdb and much more use this system to improve the user experience. The recommendations are made based on previous search results for a user.
You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses face recognition algorithm. Similarly, while using WhatsApp web, you scan a barcode in your web browser using your mobile phone. In addition, Google provides you with the option to search for images by uploading them. It uses image recognition and provides related search results.
Some of the best examples of speech recognition products are Google Voice, Siri, Cortana etc. Using a speech recognition feature, even if you aren’t in a position to type a message, your life wouldn’t stop. Simply speak out the message and it will be converted to text. However, at times, you would realize, speech recognition doesn’t perform accurately.
Fraud and Risk Detection
One of the first applications of data science originated from the Finance discipline. Companies were fed up of bad debts and losses every year. However, they had a lot of data which use to get collected during the initial paperwork while sanctioning loans. They decided to bring in data science practices in order to rescue them out of losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them to push their banking products based on customer’s purchasing power.
Apart from the applications mentioned above, data science is also used in Marketing, Finance, Human Resources, Health Care, Government Policies and every possible industry where data gets generated.