Gone are the days when businesses had only one source of data coming onto them. They had customers on a handful of platforms and all they had to worry about was the structured data that came from that platform. data science

Moreover, businesses weren’t omnipresent like today, they stuck to one core platform they targeted and focused on it. out differently life was much satisfactory and so were the sales.

But then came the age where people wanted to get out of this satisfactory zone with dreams to accomplish way more. They wanted to reach out to a much wider audience with their products and entice customers from different channels.

The wave of digitization made this quite possible. Businesses were now more reluctant to establish a digital presence and target social media platforms, where their customers were naturally found. On one hand, this was a great opportunity for the businesses while on the other a big challenge for data.

Data now became haphazard or unstructured since it was coming from multiple sources. And at the same time, the demands of customers began rapidly changing. They could no longer be predicted with one’s intuition. data science

The problem was businesses had all the data in the world, but they weren’t able to generate any meaningful insights out of it with simple BI tools. Furthermore, the pressure of customer demands along with increasing competition was overwhelming businesses.

There was a need for a solution that could dive deep into the data and the processes of artificial intelligence and machine learning, to come up with meaningful insights. That’s how data science came into the picture.

Ever since its inception, the data science industry has been growing like never before. It is expanding its capabilities and penetrating a lot of industries for the good.

With more and more companies being swept off by the tides of digitization, data science is witnessing a significant boost in its adoption.

It is only with data science practices and platforms that organizations are able to leverage all kinds of unstructured and scattered data to derive decisions.

These data-backed decisions are the secret ingredient to catering to the customer’s demands more accurately and driving the business towards unprecedented growth.

However, in spite of data science being so relevant in the modern world, most businesses are shying away from it.

One of the main reasons behind it is the lack of skillful resources for the job. The job of a data scientist requires them understanding multiple programming languages completely along with deep mathematics.

While most businesses find it too hard to look for someone with such qualities, most just feel their simple BI tool is enough to accomplish the task.

But as artificial intelligence and machine learning take over the world by storm, data science is becoming even more crucial for organizations and enterprises.

It is the key to staying relevant in the market, discovering the impact of AB testing on multiple customer segments, analyzing and predicting customer behavior in advance along with making decisions that are a hundred percent backed by data.

In case businesses haven’t made up their minds yet, they must take a look at these four data science trends that are all set to take 2020 by storm-

Automated Data Science


Even in today’s world when we are talking about and implementing automated processes in all the key areas of business, data science is found significantly lagging behind.

The point is there is still a lot of manual work required for data scientists. From the comfort of saving cleaning, Imagining, and researching to lastly Simulating it to find a real outcome. The entire process is not just laborious but also tiresome.

The data science industry had been begging to automate this process for a long time, which would also increase its adoption among businesses.

With organizations finally paying attention to it, several automated machine learning and end to end data science platforms are being created that let data scientists easily handle data management and model building.

Be it Data Robot, H2O, Auto ML or Google’s Cloud AutoML, all these platforms are being designed to automate the model design and training and simplify data science.

Data Privacy

Data is one of the most sensitive assets in the field of technology. Almost every company on the planet wants to move fast and innovate, but in the process losing the trust of their customers can be fatal to their existence.

As a result, organizations and enterprises have to make data privacy and security a mandate, so that they do not end up losing their customer’s precious information.

Data science as an industry revolves around data and most of it isn’t anonymous. Imagine the catastrophe it would cause to the world if the data gets transferred to the wrong hands. Not only will it upset the customers but also cause a threat to their lives and property.

But, with time, organizations are paying attention to data privacy and security aspect of data science. We can expect a lot of security protocols and transformations in privacy surrounding data in the coming year.

Data Science for the Cloud


With the size of data exploding around the world, data science has grown into a full-fledged industry.

The abundance in this data is paving way for the cloud technology where people can store, access and share files at their convenience without having to worry about anything. As more and more data move to the cloud, companies are offering a data science platform right there.

Natural Language Processing

Natural language processing is one of the most powerful fields, and when integrated with data science opens a lot of opportunity for businesses.

This neural network are now able to extract facts out of large portions of text exceptionally fast and categorize text in to various types together with accuracy. This means data science will be able to analyze data that is far more complex.


Data Science is undergoing a constant process of transformation which is not just making the field more appealing but also helpful for businesses out there.

Even those who are not willing to adopt data science, have now a huge opportunity to capitalize on the benefits of the growing industry. There are many competitor for this like Hadoop, Java Development Services, .Net technology etc.    

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