10 AI and Data Science Buzzwords and What They Really Mean

Data Science has emerged as a standalone industry itself serving the needs of multiple other industries and sectors by providing valuable factual insights and automation of data-driven tasks. Further, due to multiple reasons of which talent is the most significant, the adoption rate of Data Science is slower. It has been proven that data-driven decision tools can reduce costs for companies’ operations and at the same time create new markets.

On the other hand, Artificial Intelligence is a landmark in the history of our technological innovations that have happened from time to time in the past. If you look at it from a positive angle, AI is a blessing for humanity because it has got huge potential to solve many of the unsolved problems which we have been trying to solve for ages. For example, the way AI is revolutionizing healthcare & medicine, space science, reusable energy, autonomous vehicles, and many more is commendable. So, undoubtedly AI is the future technology; it is already solving many of the problems and going to solve many more in the future. To support this AI & ML development, several tools and frameworks were built by various communities allowing advancement in these technologies. With more awareness of these technologies and an enormous amount of data being available, the world started huge development in these areas to accomplish tasks using AI, ML, and DL. 

With so much going around these two disciplines, we have listed a few keywords & buzzwords that you should know if you are into Data Science and AI.

Reinforcement Learning

Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. 

Natural Language Processing

Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.

Predictive Analytics

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

Real-Time Analytics

Real-time data is information that is delivered immediately after collection. There is no delay in the timeliness of the information provided. Real-time data is often used for navigation or tracking.

Feature Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data. A feature is a property shared by independent units on which analysis or prediction is to be done. Features are used by predictive models and influence results.

Adversarial Machine Learning

Adversarial machine learning is a machine learning technique that attempts to fool models by supplying deceptive input. The most common reason is to cause a malfunction in a machine learning model.

Augmented Intelligence 

Augmented intelligence is an alternative conceptualization of artificial intelligence that focuses on AI's assistive role, emphasizing the fact that cognitive technology is designed to enhance human intelligence rather than replace it.

Augmented Analytics

Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. 


DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics.

Explainable AI

Explainable AI is artificial intelligence in which the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision.

Hope this was helpful.

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