Top Data Science Interview Questions and Answers

27-Nov-2020

Data Science is undoubtedly one of the rising concepts in the tech world and it becomes an important thing for the professional to seek interview advice when applying for a data science job or its related field. So it is always required a range of skills before preparing for the data science interview. Every interviewer or who have obtained Data Science certification looks for practical knowledge of data science and its related things with high-end knowledge. In this blog, you will learn about the important data science questions related to the data science interview that could be faced by both fresher and experienced candidates during the interview.

1. Why Data Cleansing is Important?

Data cleansing is a way or process of removing or updating the incorrect, duplicated, incomplete, or incomplete information. It is always mandatory to improve the quality of data in order to get better accuracy and productivity. 

Sometimes data is captured in improper or irrelevant formats that affect plenty of things. But when data cleansing is done, then it filters the usable data from the multiple systems that would produce improper results. So, there is a big importance of data cleansing in every single manner.

2. Which are the important steps of Data Cleaning?

The process of data cleaning depends on the type of data because multiple types of data need different sorts of cleaning. It is one of the mandatory steps before analyzing data in order to increase quality and accuracy. More than 75% of data scientist consumes their time in data cleaning. Below are the most required steps of Data Cleaning:

• Improving Data Quality.
• Treatment for Missing Data
• Find out the Structural errors.
• Removing Duplicate Data.

3. What is p-value?

p-value helps you to find out the strengths of your results whenever you perform a hypothesis test. P-value is a number between 0 and 1 that you can calculate such as Lower p-values, i.e. ≤ 0.05, which means you can simply reject the Null Hypothesis, and a high p-value, i.e. ≥ 0.05, means you can accept the Null Hypothesis.

In other words, you can say that a P-value is the complete calculation of the chances of events other than suggested by the null hypothesis.

4. How is Data Science different from Big Data and Data Analytics?

Data Science uses varied algorithms and tools to create reliable and meaningful insights from raw data. It includes multiple tasks such as data analysis, modeling, data cleansing, etc. Whenever you get the Data Science certification, you will learn about these things very easily.

Whereas Big Data is a complete combination of structured, semi-structured, and unstructured data that is generated through various channels.

Data Analytics provides the important operational insight into very complex business scenarios. It helps the organizations to predict the upcoming opportunities, and any kind of threats.  

Basically, Big Data is used to handle the large volume of data that includes the high-end practices for data management and processing it at a high speed. Data Analytics is linked to obtaining useful insights from the data using mathematical or non-mathematical procedures. Data Science is the process of making a system that can help to learn from data and make decisions by observing the past experiences of data analysis.

Also check: Future Scope of Data Scientists

5. What is Normal Distribution?

Normal Distribution is also called the Gaussian Distribution. It is a kind of probability distribution that indicates which most of the values lie near the mean. 

Following are the characteristics of Normal Distribution:

• One part or half value in the Normal Distribution is to the right of the center, and the remaining half one to the left of the center.
• The distribution has a curve of bell-shaped.
• The total area that comes under the curve is 1.

6. What is the importance of A/B testing

The main purpose of A/B testing is to choose the best one among two varied hypotheses. This testing could be used for testing a web page, banner testing, page redesigning, etc. The first step in A/B testing is to set a conversion goal, and then find out the best analysis for performing the better for the given goal.

7. What is the Difference between univariate, bivariate, and multivariate analysis?

Univariate data, as the name suggests, contain only one variable. The univariate analysis describes the data and finds patterns that exist within it. 

As its name suggesting, Univariate Data includes only one variable. It describes the data and looks for reliable patterns.

In Bivariate data, there are two different variables. It analysis deals with the varied causes, analysis, and relationship between those two different variables.

Multivariate data could three or more variables. It is almost similar to the bivariate, but in Multivariate, there is more than one dependent variable.

8. What is the difference between “wide” and “long” format data?

Wide-format is a format of data where you get a single row for each data point with multiple columns in order to hold the varied attribute’s values. Whereas the Long-format is a format of data where you have multiple rows for each data point as like the varied attributes, and every row consists of the particular attribute’s value.

9. What is clustering?

Dividing the data points into varied groups is called Clustering. In this process, the division is performed in a way that every single data point in the same group is more related to each other.

Some of the Clustering types are given below:

• Hierarchical clustering.
• Density-based clustering.
• Fuzzy clustering
• K means clustering.

10. What is the difference between a tree map and heat map?

A Heat Map is a kind of tool which is used to compare the different categories with the help of size and colors. It is also used to compare the two different measures. Whereas the Tree Map is a type of chart that indicates the hierarchical data or part-to-whole relationships.

11. What is the hyperbolic tree?

A hyperbolic tree is a graph drawing and an information visualization method that is inspired by hyperbolic geometry.

12. What is the difference between the expected value and the mean value?

The mathematical expectation is also known as the expected value. The mean value is the average of every or all the data points.

13. What are the main steps need when making a decision tree?

You are required to follow the below steps while making a decision tree:

• Establish the Root of the Tree Step.
• Calculate Entropy for the Classes Step.
• Calculate Entropy after Split for every Attribute.
• Calculate the gained Information of each split.
• Perform the Split.
• Perform Further Splits Step
• Complete the Decision Tree

Data Science interview questions and answers Top Data Science Interview Questions and Answers