Data Science
Interview Questions
(30 days of Interview Preparation)
, INEURON.AI
Q1. What is the difference between AI, Data Science, ML, and DL?
Ans 1 :
Artificial Intelligence: AI is purely math and scientific exercise, but when it became computational, it
started to solve human problems formalized into a subset of computer science. Artificial intelligence has
changed the original computational statistics paradigm to the modern idea that machines could mimic
actual human capabilities, such as decision making and performing more “human” tasks. Modern AI into
two categories
1. General AI - Planning, decision making, identifying objects, recognizing sounds, social &
business transactions
2. Applied AI - driverless/ Autonomous car or machine smartly trade stocks
Machine Learning: Instead of engineers “teaching” or programming computers to have what they need
to carry out tasks, that perhaps computers could teach themselves – learn something without being
explicitly programmed to do so. ML is a form of AI where based on more data, and they can change
actions and response, which will make more efficient, adaptable and scalable. e.g., navigation apps and
recommendation engines. Classified into:-
1. Supervised
2. Unsupervised
3. Reinforcement learning
Data Science: Data science has many tools, techniques, and algorithms called from these fields, plus
others –to handle big data
The goal of data science, somewhat similar to machine learning, is to make accurate predictions and to
automate and perform transactions in real-time, such as purchasing internet traffic or automatically
generating content.
Page 2
Interview Questions
(30 days of Interview Preparation)
, INEURON.AI
Q1. What is the difference between AI, Data Science, ML, and DL?
Ans 1 :
Artificial Intelligence: AI is purely math and scientific exercise, but when it became computational, it
started to solve human problems formalized into a subset of computer science. Artificial intelligence has
changed the original computational statistics paradigm to the modern idea that machines could mimic
actual human capabilities, such as decision making and performing more “human” tasks. Modern AI into
two categories
1. General AI - Planning, decision making, identifying objects, recognizing sounds, social &
business transactions
2. Applied AI - driverless/ Autonomous car or machine smartly trade stocks
Machine Learning: Instead of engineers “teaching” or programming computers to have what they need
to carry out tasks, that perhaps computers could teach themselves – learn something without being
explicitly programmed to do so. ML is a form of AI where based on more data, and they can change
actions and response, which will make more efficient, adaptable and scalable. e.g., navigation apps and
recommendation engines. Classified into:-
1. Supervised
2. Unsupervised
3. Reinforcement learning
Data Science: Data science has many tools, techniques, and algorithms called from these fields, plus
others –to handle big data
The goal of data science, somewhat similar to machine learning, is to make accurate predictions and to
automate and perform transactions in real-time, such as purchasing internet traffic or automatically
generating content.
Page 2
