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What is Data Science? Who is it essential for?

To explain Data Science in simple words: It is a study of where information is extracted from, what it represents and how it can be converted into a valuable source in the building of IT and Business approaches. 

Data science is a field which is composed of statistics, mathematics, and computer science disciplines efficiently and incorporates techniques like cluster analysis, machine learning, visualization, and data mining.

In the past few years, Data Science has calmly spread to include organizations and businesses worldwide. It is widely used by astronomers, analytics, and research, geneticists, entrepreneurs as well as engineers.


Role of a Data Scientist

A data scientist aims to transform data into knowledge, knowledge which is used to make rational decisions. They possess all three set of skills - Mathematics and Statistics, Machine Learning and Subject Matter Expertise. Patrons who are masters in all three skills are less in number. The three skills required for data science mastery are explained below: 


Machine Learning

Machine learning is a subfield of artificial intelligence based on statistics. It involves machines learning how to complete tasks without being explicitly programmed to do so. This part is explained in details further.

Mathematics and Statistics

While everyone knows what math is, statistics is the study of data: how to collect, summarize and present it. The statistics part will be covered in details later on.

Subject Matter Expertise (SME)

In general, a domain expert or subject-matter expert (SME) is an individual who is a specialist in a specific area or topic. An SME should also have basic knowledge of other technical subjects too. In Data Science an SME Provides industry/process-specific context for what the patterns identified by the algorithms and models mean.

Such Individuals who master all three skills are also called as Unicorn Data Scientist. Despite how rare unicorn data scientists are they are rapidly growing in demand. Also, there doesn't appear to be any end in sight for the growth of this demand. As a result, in the very near future, this specific set of skills will be in high demand, whether you're a data scientist or applying data science practices to your current job role. The rarity of data scientists combined with their high demand leads to much higher salaries for data scientists and IT professionals with similar skills.

Data Science Blogs


Data Science website is a platform for data scientists who can explore varied sources of data, build algorithms and models, and deploy work seamlessly. It also manages a blog, in which the articles are written by professionals who are currently working as data scientists.

Kaggle is a platform for analytics and predictive modeling, which is turning data science into a sport. A Data Science blog and kaggle’s competition, No Free Hunch, discusses tutorials, news and expert interviews exclusively related to data science.


Data Science Communities

KDnuggets is the best resource for data science where we gain knowledge through news, publications, webcasts, courses, etc. This is a community for patrons who want to convey their thoughts and gain the understanding of data related areas like Data Science, Data Mining, Business Analytics, and Machine Learning

Data Science central is the widely preferred community for those who are highly immersed in the culture of data science. Participating in the forum discussions, read blog posts from peers and stay updated with the latest research on Data Science Central.

Quora is a global platform for gaining knowledge through question and answers, Where people engage in discussions, ask questions and find resources on any topic virtually. This community aims at “the technical approach to extraction of knowledge from data.”

There exists a lot of articles on O’Reilly, which seem to be very interesting. They cover most of the articles related to artificial intelligence and data science written by experts and influencers in data science. Therefore, they sound technically strong and also share concepts which are advanced. O'Reilly is notable for putting on events, for example, Strata that help characterize the data science group.

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On 1/22/2021 at 1:37 AM, Harish said:

Machine Learning

Machine learning is a subfield of artificial intelligence based on statistics. It involves machines learning how to complete tasks without being explicitly programmed to do so. This part is explained in details further.

This may be of interest.

Google AI Takes Down Human Champ of World's Most Complex Board Game




In 1997, a Russian chess grandmaster Garry Kasparov was beaten by DeepBlue, a computer developed by IBM. Kasparov was the best chess player in the world, and his defeat sparked widespread fears that machines were getting too advanced. 

But it was a different story for computer scientists who love seeing and testing the power of technology. Recently, Google developed a new computer designed to play a game that is way more complicated than chess: The ancient Chinese game of Go.


Go, which has more permutations than there are atoms in the universe, is thought to be the most difficult board game in the world.

Today the Google program, known as AlphaGo, defeated world champion Lee Se-dol in the first of five matches in Seoul, South Korea.


Go originated in China over 3,000 years ago. Winning this board game requires multiple layers of strategic thinking.



Two players, using either white or black stones, take turns placing their stones on a board. The goal is to surround and capture their opponent's stones or strategically create spaces of territory. Once all possible moves have been played, both the stones on the board and the empty points are tallied. The highest number wins. 


As simple as the rules may seem, Go is profoundly complex. There are an astonishing 10 to the power of 170 possible board configurations - more than the number of atoms in the known universe. This makes the game of Go a googol times more complex than chess




It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. The system starts off with a neural network that knows nothing about the game of Go. It then plays games against itself, by combining this neural network with a powerful search algorithm. As it plays, the neural network is tuned and updated to predict moves, as well as the eventual winner of the games.



It teaches itself,  based only on "knowing" the rules of the game!  This is really astounding. Any Go players who can comment on this remarkable achievement?


Edited by write4u
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