15 AI and Data Science Buzzwords
Published: 2021-10-04 10:54:28

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 being 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.

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.

**Big Data**
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
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**Machine learning**
Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.

**Supervised learning**
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

**Unsupervised Learning**
Unsupervised learning is a type of machine learning in which the algorithm is not provided with any pre-assigned labels or scores for the training data. As a result, unsupervised learning algorithms must first self-discover any naturally occurring patterns in that training data set.

**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.

**Deep Learning**
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.

**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.

**Bonus: Buzzwords**

**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 method that aims to trick machine learning models by providing deceptive input. It includes both the generation and detection of adversarial examples, which are inputs specially created to deceive classifiers.

**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**
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.