## Introduction

Hello Ninjas, are you looking for Data Science interview question? If yes, then you have made it to the right platform. We have gathered a list of the most asked and popular interview questions for Data Science. Data Science is a potpourri of statistics, artificial intelligence, maths, machine learning, and algorithms. Therefore, If one wants to get their dream job, having a list of selected and popular interview questions makes preparation much smoother.

Also Read About, __AEM Interview Questions__ and __Operating System Interview Questions__

## Easy-level Interview Questions

**1. What do you mean by Data Science?**

**Answer: **__Data Science__ is an area of study that deals with significant data volume using modern-day technology such as statistics, __Artificial Intelligence__, maths, __Machine Learning__, and algorithms. Using these, we identify relevant patterns in our data for making strategic decisions. We use it to create data models to get an optimal solution for our problem.**2. How Data Science and Data Analytics are different from each other?**

**Answer: **Data analytics analyzes the data to see valuable patterns and solve predefined problems. It uses __Data Mining__, modeling, analysis, and database management tools. Whereas, Data Science uses artificial intelligence, machine learning, algorithms, and asking relevant questions. The relevant information is extracted from unstructured or unorganized data.

**3. What is Sampling?**

**Answer: **Usually, a large volume of data is available for analysis, but performing data analysis on such massive data is not possible. In such scenarios, sampling plays an important role. A small portion of data samples are selected, and suitable analysis is performed. The choice should be made in such a way that it correctly represents the rest of the data.

**4. What is selection bias?**

**Answer: **Selection bias occurs while sampling. Data is sampled so that an indiscriminate piece is not achieved. Selection bias can also be referred to as non-random sampling. Therefore, in this, the sample doesn't truly represent the dataset.

**5. What do you mean by linear regression?**

**Answer: **There are generally two types of variables, dependent and independent. Linear regression helps understand the relationship between these dependent and independent variables. It tells us how the dependent variable changes with respect to the independent variable. Simple linear regression is the case in which only one independent variable is present. But, when there is more than one independent variable, then it is called multiple linear regression.

**6. What do you mean by logistic regression?**

**Answer: **Logistic regression is a logistic model. It allows us to understand the relationship between binary dependent and independent variables. This kind of regression is usually used for prediction or classification. The outcome of logistic regression is definite or a discrete value.

**7. How is Data Science different from traditional application programming?**

**Answer: **In Traditional programming, a program is written in assembly or high-level compiler languages such as C, C++, Python, etc. In such programming, we judge the input based on The output. Generally, we write many essential steps to solve a problem. In comparison, __Data science__ uses artificial intelligence and machine learning and works on patterns observed in the data. Data science algorithms use mathematical analysis to give out the rules to match the inputs to outputs.

**8. What do you understand by the term tensors?**

**Answer: **Tensors usually portray various applications, including videos or images. This mathematical object consists of linear algebra, through which selection vectors (vectors being a mathematical object) are mapped to numerical values.

**9. Explain Boltzmann Machine's concept.**

**Answer: **Boltzmann Machine discovers unique features which portray complex regularities. This type of machine consists of repeating neural networks, and decisions are made by binary nodes using a simple learning algorithm. It uses the algorithm to optimize the quantity and weight of particular complications.

**10. What do you mean by Power Analysis?**

**Answer: **We use power analysis while calculating the smallest sample size during an experiment. This analysis is done before data collection, which aids the researcher in determining the minimal sample size, given some significant level, effect size, and statistical power.