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Global Business Jet Market: Industry Analysis and Forecast 2021-2027

Data Science – Dream of Dreams or Nightmare of Nightmares?

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The brain is a marvel. You could tell a two-year old that a certain picture is a dog, and the child will instantly associate the figure of a dog to that picture. Next time they see a dog, they will associate it with the word ‘dog’. If you show them the picture of another 4-legged animal, they may be confused, because, well they have not learned the name of that animal yet.

This is exactly what data science aims to achieve. Ever seen the front camera trying to guess your gender and age, but it shows your age 20 years off and the opposite gender? That is data science at work. Data science, in short, is the use of many scientific and technological processes to obtain knowledge and insights into the data. It is not perfect, and is very flawed, as of now. But with advancements in technology and mathematics, we can refine it to be the closest to the human brain, or even better.

Data science aims to transform the normal computer into a machine that can well, think for itself. It is the stepping stone for a whole new generation of artificial intelligence, computers which do not need to be programmed to execute tasks. All the science fiction movies such as ‘I, Robot’ may not be science fiction anymore. Day by day, we inch closer to that dream and a potential nightmare.

Data science has many applications as well as fields that pave the way for further applications. Some of the most popular applications of data science are –

Fraud and Risk Detection – One of the most common areas where risk detection is used is the spam filter in Gmail. Ever happened that an important email that was urgent never made it to your inbox, and only later you found out that it went to the spam folder? Data science aims to improve fraud and risk detection to such an extent that emails can be scanned and if they contain the keywords and phrases of spam emails, they go to the spam folder. Right now, the software is not too perfect, the consequence of which is you lose track of important information. In general, it can be used to improve the security of NetBanking and other places where sensitive information is entered.
Healthcare – Data science can help find cures for diseases such as the novel coronavirus. All viruses have a protein structure and the viruses for which cures have already been discovered can be compared to viruses for which treatments have not been discovered in order to find defects in the viruses which can help find a cure.
Advanced Image Detection – As I had mentioned before, the software can take examples of how people look at their age and compare that to your face to get an accurate age and gender.
Speech Recognition – Voice assistants like Google Assistant, Siri, Bixby can use data science in order to speak more naturally. Google Duplex was one such thing. It can learn from your schedule and use natural language processing to call hairdressers, set meetings, and help you throughout the day. In essence, everyone will have their own assistant now.
As is with any breakthrough technology, Data Science has its advantages and disadvantages. Some of its biggest advantages –

Smarter Products – As I mentioned before, products can use machine learning to help tailor more to your needs and give you what you need, when you need it.
Widespread Applications – Data science can be used in almost any field, from banking to medicine to the cell phones in our hands. This versatility also helps ensure that data science will not be an obsolete field in the near future.
But this future comes at a price. Some disadvantages of data science are –

Huge Amounts of Data – To train models that can predict outcomes and determine patterns efficiently with minimum error, massive amounts of data are required. This amount of data is not something that companies have at hand. This very often results in low efficiency of the system.
Data Privacy – As mentioned above, companies need huge volumes of data to have accurate models ready to be implemented in the real world. These volumes of data are taken from users. This data is then essentially public and not private, which is a big risk to privacy and security.
So what do you think? Is Data Science the dream of dreams, or nightmare of nightmares?

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