Why is Data Science so popular today?

Now that we have more clarity of what Data Science is, we need to address the next question which is “Why is Data Science so popular today?” And there are so many factors for that which are listed below.

  1. Today, data is everywhere and “Data Science” as the term suggests depends on data and we have abundance of data today, not only in terms of the personal devices that we have in terms of Laptops, mobile phones, smartwatches, iPads, tablets and so on, all these personal devices which we use to interact with the connected world around us generates a lot of data and also consumes a lot of data in this process. Then data also comes from sensors which have been put all around, then we also have the transactional data in sense that everything is online, be it a government service, hospital service, and so on and there is a digital record of all the transactions and operations happening in an organization which in turn, of course, generates a lot of data. And now so much of data is available, it has become almost imperative to do something with the data and draw insights from it.
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2. Devices have become more powerful and cheaper

As we are collecting and dealing with large amounts of data, we need storage and for storage, we need to have Data warehouses or storage on cloud and 10–20 years back, these storage devices were very expensive and in today’s era we could get a lot of storage space(say equal space as a decade back) at a much cheaper rate and the same is true for much-specialized hardware such as GPUs which allows complex Deep Learning models to be built and train the model on a large amount of data very efficiently in much smaller time than what was possible 10 or 20 years back.

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3. The democratization of both software and hardware

Software Companies like Google, Facebook have invested a lot in democratizing these technologies by making their frameworks such as TensorFlow and PyTorch openly available, so while doing so what they have done is that they have hidden the complexities such as compilation, optimization on specific hardware and they have provided a very easy interface to Data Scientist to quickly try out a lot of things with data, build really powerful models without really worrying about what is the optimization algorithm underline

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And that’s also coupled with Hardware. So, hardware also in some sense has been democratized, it’s available at much cheaper cost on cloud, we don’t need to buy and pay upfront full cost of the device, if we want them for say one week then we can get it on the cloud for that time and pay for that much only.

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So, these are the three main reasons because of which the Data Science field is growing at an interesting large pace and possibly is intersecting with all the other domains.

References: PadhAI

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