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Real time applications of data science  


The advancement in technology and its incorporation in our life has led to generation of data in huge amounts. Data is drawn from mediums like cell phones, social media, e-commerce sites, healthcare surveys, internet searches, etc. The availability of large data sets has paved the way for Big Data – a field which refers to the huge amount of information available that can be used in various sectors like finance, marketing, logistics, etc. to get insights and produce better results. Data traffic per active smartphone is expected to increase five fold from 1.4 gigabyte (GB) per month in 2015 to 7 GB per month by 2021. In 2021, 99 per cent of the region’s mobile traffic will be from data. (Source: Ericsson Mobility Report India edition).

Let’s look into how big data analytics is going to be in future:

Data production will be 44 times greater in 2020 than it was in 2009. Individuals create more than 70% of the digital universe. But enterprises are responsible for storing and managing 80% of it. Also by 2020 one third of all data will be stored, or will have passed through the cloud, and we will have created 35 zettabytes worth of data (source: https://www.modeln.com/blog/10-interesting-facts-big-data/)

Data science involves collection, preparation, analysis and visualization of large data sets. According to Data science research initiative there are two components in data science. (i) Study of the nature of the data and scientific issues related to data itself. (ii) Possible usefulness and real world applications.

Hereby listed are few real-life examples of data science application.

1. Internet Search

Whenever we have to find about any service or product we try finding it using search engines like Google, Yahoo, Bing, etc. These search engines use data science and algorithms to provide us the best match within a very short span of time. Google now processes over 40,000 search queries every second on average which translates to over 3.5 billion searches per day (Source: www.internetlivestats.com) Data science helps in Search engine optimization by helping in greatly improved relevancy ranking, providing users with extended search navigation options and improving search speed through removing unnecessary data clutter before indexing

2. Recommendations

Recommender systems apply data science for providing recommendations to the users. The most suitable example is Amazon’s recommendation engine, which provides users with a personalized webpage when they visit amazon.com. Recommender systems are not only used in e-commerce but also in other applications like recommending music and events to products and dating profiles. The recommendations are made based on previous search results for a user. Amazon Prime Day 2016 witnessed sales of an estimated 636 items per second (Source: https://www.inc.com/tom-popomaronis/amazon-just-eclipsed-records-selling-over-600-items-per-second.html)

Some of the advantages of using data analysis in recommender systems are:

  • The ability to offer unique personalized service for the customer
  • Increase sales, click-through rates, conversions, etc.
  • Opportunities for promotion, persuasion
  • Obtain more knowledge about customers

Who can forget the suggestions about similar products on Amazon? They not only help you find relevant products from billions of products, but also adds a lot to the user experience.


3. Image Recognition

Recognition of an image involves classification of data using many data points. By this technique we can classify an entire image or things within an image.

Have you noticed that Facebook provides you suggestions for tagging your friends when you upload an image? Also on google you search for images by uploading them. This is possible due to image recognition.

Additionally there are freely available databases like ImageNet and Pascal VOC. These databases comprise of millions of images tagged with keywords about what’s inside the pictures, Imagenet contains more than 14 million tagged images which are available free of cost.

4. Gaming

There are more than two billion video game players in the world, and Electronic Arts, who has 275 million active users, generates approximately 50 terabytes of data each day.(Source: https://datafloq.com/read/gaming-industry-turns-big-data-improve-gaming-expe/137)

There is enormous data generated either by social games whether it is an online social game on Facebook, a game played on an offline PlayStation or game played using Xbox. The huge data complies of all the actions of the gamers like how they interact, how much duration they spend in gaming, what are the timings, who all are playing together, etc. Data analysis teams in gaming industry extract such kind of information from its games and analyse it in order to bring more customers, increasing turnover, keeping players spend more time and improve overall gaming experience.

Some of the companies that have used data science to provide excellent gaming experience are EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard, etc.

5. Airline Route Planning

Data science tools are being used to monitor the changes in flight fares depending on the demand.

Data analytics has also been of great importance in aviation industry in predicting delays in flights, deciding whether to halt in between or directly land the flight. Also it helps in defining new programs for customers and identifying operational strategies for improvement.

On the basis of the data generated and analysed algorithms are created which can predict the future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers and deals.

Hopper, one of the start-ups in aviation uses data science to help people book the cheapest flights by making the use of predictive analytics.

There are innumerable such applications of data analytics. Data science can be a game changer for some of the industries by helping in digital transformation by enabling organisations to capitalise on big data, and use it to create opportunities and innovations. A very interesting case of Big Data in Aviation can be found at https://www.digitaldoughnut.com/articles/2017/march/how-airlines-are-using-big-data

If you wish to learn the basics of data science or want to be a professional data scientist join the data science orientation course by Microsoft at Millionlights (www.millionlights.org) and start your journey in the world of data science.