While we know that Data Science is being used for COVID-19 research, we should remember that post-COVID-19, there will be a significant change in landscape. Here are the recent advancements in the world of Data Science and Analytics over the last week.
Why Learning Python is now essential for all Data Scientists
As technologies such as Machine Learning, Artificial Intelligence, and Data Science are advancing at a great pace, it has become a popular career choice among people. A Data Scientist needs to learn multiple programming languages, but one can start by grasping any one language with clarity. Python is regarded as the most popular programming language. It is an open-source language and makes tasks hassle-free. Python is faster than languages such as R or MATLAB. It has several Data Science libraries such as NumPy and Scipy. More tools are being added to Python as we progress.
Why Data Science can be a game-changer in 2021
We have noticed a surge in Data Science services in various fields. The big data market is estimated to reach US$103 billion by 2027, after being valued at US$49 billion in 2019. Several market reports suggest that the Data Science market will grow at a CAGR of 30%. There is an increase in the growing need to gain insights from large datasets. This will help you gain a competitive advantage. The inclination of enterprises towards data-intensive business strategies is also helping Data Science to flourish as a field.
5 ways Data Science landscape will change post-COVID-19
As we have seen, Data Science plays an important role in our fight against the coronavirus. It helps in assisting the government. It is being used to extract data and insights from various sources available and has proved to be an effective tool. However, once the crisis is over, we may witness changes in the Data Science landscape. The Data Science community must be able to think beyond predictions. It should be able to restrain itself from using any Data without an extremely critical judgment. Robust Natural Language processing solutions and advancement in streaming analytics are among the 5 ways Data Science landscapes will change.
How new technology in Data Science is impacting Data Scientists
While we are aware that Data Scientists are in great demand, we must also remember that to excel in this field, you would have to relearn various technologies. However, one of the biggest challenges is to keep up with the pace of technology itself. New programming languages, open-source contributions from Google, Facebook, new tools and various libraries are introduced ever so often, all across the globe. There is a great amount of learning required to keep pace with these advancements and changes.
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