There's the expectation for Data Scientists who have done some initial research to readily be able to explain basic statistical terms (such as sampling, hypothesis testing, and variance vs. standard deviation). Similarly, you should have an understanding of the popular technical models and methodologies (such as Neural Networks, Bayesian Network, supervised machine learning, unsupervised and reinforcement learning, and much more).

But should you ask most Data Science enthusiasts what data science actually is, and you will find the majority of them lost. Terms like Data Mining, Big Data, Data Science, and Data Engineering all seem synonymous. This is because we lack the understanding that lies behind the emergence of these individual technologies. In future tutorials, we'll address the difference between these so-called synonymous terms. However, we'll restrict the scope of this tutorial.

This tutorial will help you out in identifying the terminologies and concepts that a professional Data Scientist must comprehend to crack an entry-level interview.

Some tips:

1. Do not rush into answering the question. Rather, first, ensure that you understand the concepts that are relevant to the question. Only then try to answer the question.
2. Even if you do not understand the question completely, make a habit of narrowing the answer choices, especially in multiple-choice questions, and then make an informed guess.
3. This tutorial will guide you towards more realistic questions that are usually asked in interviews. You can maximize the output from this tutorial if you give us feedback on topics that you'd like to see.