Top 10 Basic Data science Interview Questions and Answers for 2020

Shashank Shanu

9 months ago

Data Science Interview Questions
Data Science Interview Questions
After months and year of your learning, one of the most important part of your Data science journey is the interview process. Interviews are very rigorous process where the candidates are judged on different areas of expertise such technical and coding skills, knowledge and clarity of basic concepts of data science, statistics, machine learning and many more. If you willing to apply for data science jobs, it is very important to know what kind of interview questions generally interviewers, recruiters and hiring managers may ask.
So, in this article, I will try to give top 10 questions which may be asked by an interviewer during your interview process.
So, without wasting much time, let’s start…

Question 1. Describe Univariate, Bivariate and Multivariate Analysis.?

Univariate, Bivariate and Multivariate Analysis
Univariate, Bivariate and Multivariate Analysis
Answer: These are the types of analysis methodologies having a single, double, multiple variables.
Univariate analysis is a type of analysis which will have one variable and due to this there are no relationships, causes. Univariate analysis is mostly used to summarize the data and find the patterns within it to make actionable decisions.
A Bivariate analysis is a type of analysis which deals with the relationship between two variables. These sets of paired variables come from related sources, or samples. The strength of the correlation between the two variables will be tested using Bivariate analysis.
A multivariate analysis is a type of analysis where we try to find the relationships between more than two variables. In real world this is the most important and used type of analysis.

Question 2. What Do You Understand by The Term Normal Distribution?

Normal Distribution
Normal Distribution
Answer: Normal distribution is a type of continues probability. It is a set of continuous variables spread across a normal curve or in the shape of a bell curve. It is the most commonly used distribution curve and very useful to analyze the variables and their relationships when we have the normal distribution curve.
The normal distribution curve is symmetrical. The non-normal distribution also tries to become normal distribution as the size of the samples increases this is known as Central Limit Theorem. It is also very easy to apply the Central Limit Theorem. This method helps to make sense of data that is random by creating an order and interpreting the results using a bell-shaped graph.

Question 3. What Is Linear Regression?

Linear Regression
Linear Regression
Answer: It is one the most commonly used algorithm for predictive analytics. Linear Regression is used to find relationship between a dependent variable and one or more independent variable. The main task in the Linear Regression is the method of fitting a single line within a scatter plot.
The Linear Regression consists of the following three methods:
  • Determining and analyzing the correlation and direction of the data.
  • Deploying the estimation of the model.
  • Ensuring the usefulness and validity of the model.
It is extensively used in scenarios where the cause-effect model comes into play. For example, you want to know the effect of a certain action in order to determine the various outcomes and extent of the effect the cause has in determining the final outcome.

Question 4. What is R square?

R square
R square
Answer: R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.
The definition of R-squared is the percentage of the response variable variation that is explained by a linear model.
R-squared = Explained variation / Total variation
R-squared is always between 0 and 100%.
0% indicates that the model explains none of the variability of the response data around its mean.
100% indicates that the model explains all the variability of the response data around its mean.
In general, the higher the R-squared, the better the model fits your data.

Question 5. What is the difference between Supervised learning, Unsupervised learning and Reinforcement learning?

Machine Learning
Machine Learning
Answer:
Machine Learning
Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.
Building a model by learning the patterns of historical data with some relationship between data to make a data-driven prediction.
Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
Supervised learning
In a supervised learning model, the algorithm learns on a labelled dataset, to generate reasonable predictions for the response to new data. (Forecasting outcome of new data).
  • Regression
  • Classification
Unsupervised learning
An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features, co-occurrence and underlying patterns on its own. We use unsupervised learning for
  • Clustering
  • Anomaly detection
  • Association
  • Autoencoders
Reinforcement Learning
Reinforcement learning is less supervised and depends on the learning agent in determining the output solutions by arriving at different possible ways to achieve the best possible solution.

Question 6. What is Mean Square Error?

Mean Square Error
Mean Square Error
Answer: It is a type of evaluation metric which tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them.

Question 7: What is the difference between logistic and linear regression?

logistic and linear regression
logistic and linear regression
Answer:  Linear and Logistic regression are the most basic form of regression which are commonly used. The essential difference between these two is that Logistic regression is used when the dependent variable is binary. In contrast, Linear regression is used when the dependent variable is continuous, and the nature of the regression line is linear.
Key Differences between Linear and Logistic Regression
Linear regression models data using continuous numeric value. As against, logistic regression models the data in the binary values.
Linear regression requires to establish the linear relationship among dependent and independent variables, whereas it is not necessary for logistic regression.
In linear regression, the independent variable can be correlated with each other. On the contrary, in the logistic regression, the variable must not be correlated with each other.

Question 8. How to handle a decision tree for numerical and categorical data?

decision tree for numerical and categorical data
decision tree for numerical and categorical data
Answer: Decision trees can handle both categorical and numerical variables at the same time as features. There is not any problem in doing that.
Every split in a decision tree is based on a feature.
1. If the feature is categorical, the split is done with the elements belonging to a particular class.
2. If the feature is continuous, the split is done with the elements higher than a threshold.
At every split, the decision tree will take the best variable at that moment. This will be done according to an impurity measure with the split branches. And the fact that the variable used to do split is categorical or continuous is irrelevant (in fact, decision trees categorize continuous variables by creating binary regions with the threshold).
At last, the good approach is to always convert your categoricals to continuous using LabelEncoder or OneHotEncoding.

Question 9: During analysis, how do you treat missing values?

treating missing values
treating missing values
Answer: The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights. If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored. There are various factors to be considered when answering this question-
Understand the problem statement, understand the data and then give the
answer. Assigning a default value which can be mean, minimum or maximum
value. Getting into the data is important.
If it is a categorical variable, the default value is assigned. The missing value is assigned a default value.
If you have a distribution of data coming, for normal distribution give the mean value.
Should we even treat missing values is another important point to consider? If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.

Question 10: Why data cleaning plays a vital role in the analysis?

Cleaning data from multiple sources
Cleaning data from multiple sources
Answer: Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.
These are some of the most common interviews questions and answers which is being asked most frequently by an interviewer. But there are lots of area where an interviewer may ask question. So, it’s very important for you to be well prepared before facing an interview round.
I hope after you enjoyed reading this article and finally, later I will try to bring some more interesting and important questions of data science interviews.
For more such blogs/courses on data science, machine learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…

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