The 4 Hottest Trends in Data Science for 2020

Companies all over the world across a wide variety of industries have been going through what people are calling a digital transformation. That is, businesses are taking traditional business processes such as hiring, marketing, pricing, and strategy, and using digital technologies to make them 10 times better.

Data Science has become an integral part of those transformations. With Data Science, organizations no longer have to make their important decisions based on hunches, best-guesses, or small surveys. Instead, they’re analyzing large amounts of real data to base their decisions on real, data-driven facts. That’s really what Data Science is all about — creating value through data.

Author: George Seif

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  1. Data Science is the field which is rapidly growing with new capabilities and to reach out into every industry. With digital transformations taking place in the organizations across the globe, this blog included trends of more companies leveraging more data to understand and make better decisions. Most of the organizations adopt traditional business processes which include marketing, hiring, marketing, strategy, pricing, and using digital technologies to make the process work better and in an efficient manner. Data Science is a significant part of the transformations. Organizations no longer have to make their significant decisions based on best-guesses, hunches, small surveys. As a substitute, they’re analyzing large amounts of real data to base their decisions on real, data-driven facts. Data science covers a wide network of subjects which includes IoT, Deep learning, AI and others. It is a complete amalgamation of data analysis, inference, algorithm computation and technology to resolve multifaceted business problems. The following trends in this field are expected to continue in the coming year as well.
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    The latest trends that have gathered growing interest this year and will continue to grow in 2020 are as follows:

    Automated Data Science:
    Till today, data science requires a lot of manual work such as cleaning data, storing data, visualizing and exploring data, and modeling data to get actual results. The manual work is pleading for automation and thus automated Data Science and Machine Learning is been rise. Auto-Data cleaning is researched over the past years. Cleaning big data takes up most of a data scientist’s time. Including both startups and MNC companies such as IBM offer automation and tooling for data cleaning.

    Data Privacy and Security:
    Privacy and security are sensitive topics in the technology. Major companies are in need to move fast and innovate, but lose the trust of customers over privacy or security issues which be further can be fatal. Thus, mandatory to make it a priority, at least to a minimum of not seeping private data. Data Science has become advance, in terms of the transformation of the privacy and security protocols. That includes laws, processes, and different methods of starting and maintaining the security, safety, and integrity of data.


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