# Updated Data Scientist Interview Questions For 2020

Some time back, IBM predicted that the demand for data science job roles would increase by 28% till the year 2020. We are definitely close to this reality, and it is not hard to see why. Data science, big data, and machine learning are one of the most preferred fields in the job landscape because of the importance delivered. We gather so much data each day, every day. If we fail to put this data to use, all this information may go to waste and unnecessarily sit in your storage systems.

Contrary to this, when this data is put to use effectively through data science, it helps in making better decisions, understanding user patterns, and improving user satisfaction – to name a few.

In an attempt to utilize data in their favor, a lot of organizations hire data scientists for improved data analysis. But, the data science interview is not always as easy as it sounds. For this reason, we have prepared an updated list of data scientist interview questions that you need to check.

## Learning Of Blog

• Data Science Interview Questions 2020

o Explain the Process of Logistics Regression.

o How Can You Make a Decision Tree?

o What are the Ways to Avoid Overfitting of a Model?

o What is the Difference in Multivariate, Bivariate, and Univariate analysis?

o If You Have a Dataset with 30% Missing Variable Value, How Will Deal With The Situation?

o What can You Say About P-Value?

o Using Which Algorithms You Can Fill Missing Values Continuous and Categorical Nature?

• Conclusion

Even when you are looking forward to completing a data science certification for improved career prospects, you will definitely come across these questions. Hence, without further ado, let’s move ahead and discuss these questions.

## Data Science Interview Questions 2020

If you are preparing for an interview, here are the questions as suggested by data science experts. Let’s see how you can answer certain questions.

### 1. Explain the Process of Logistics Regression.

Logistic regression is a method, which is used to understand the underlying relationship between an independent and dependent variable with the help of logistic function (Read: Sigmoid).

This is an apt answer that can be used to solve this question. You can further create a graph to depict this sigmoid function depending upon the proficiency level of the interview.

### 2.  How Can You Make a Decision Tree?

Although you may have already learned how to make a decision tree during your data science certification, here’s how you can answer this question:

• Take the dataset and calculate its predictor attributes and target variables.
• Now, find out the information gain corresponding to every attribute.
• Select the root node, which should be the attribute that has the highest information.
• Repeat till you have decided the decision node corresponding to every branch.

### 3. What are the Ways to Avoid Overfitting of a Model?

Any model which only considers a small dataset [ignoring the whole data] is called an overfitting model. Here’s how you can avoid overfitting:

• Create a simple model with fewer variables and noise.
• Utilize techniques for cross-validation such as K folds.
• Utilize techniques for regularization like LASSO.

### 4.  What is the Difference in Multivariate, Bivariate, and Univariate analysis?

• The multivariate analysis includes more than three variables and more than one variable that is dependent. For instance, when you are comparing the price of a house.

•  The bivariate analysis includes two variables, which are used to find relationships and causes. For example, how ice-cream sales are affected by temperature.

• The univariate analysis includes only one variable, which is used to find patterns. For example, the weight of a group.

### 5.  If You Have a Dataset with 30% Missing Variable Values, How Will Deal With The Situation?

Here are the ways through which you can find missing values. You may have already studied these methods in your data science certification.

• When the dataset is huge, you can eliminate these rows that have missing values. This is simple and effective.
•  When the dataset is small, the missing values are substituted using the average or mean of the present data.

### 6.  What can You Say About P-Value?

• When P-value> 0.05, then you can accept the corresponding null hypothesis.
• When P-value <= 0.05, then you can reject the corresponding null hypothesis.
• When P-value at 0.05 cutoff, then the null hypothesis can go in any direction.

### 7.  Using Which Algorithm You Can Fill Missing Values of Continuous and Categorical Nature?

In this case, you can utilize k nearest algorithm. This because it has the ability to find out the nearest neighbor. When we don’t have a value, the nearest neighbor is calculated based on every other feature.

## Conclusion

There’s no doubt in the fact that data scientists have multiple opportunities. However, to grab these opportunities, you need to prepare yourself for the interview. Hence, review the above questions and complete a data science certification to understand the type of questions that you may have to face in an interview. Don’t forget to keep practicing and preparing.