Most Asked Data Science Interview Questions with Answers.
Data Scientist is a crucial and in-demand role as they work on technologies like Python, R, SAS, Big Data on Hadoop and execute concepts such as data exploration, regression models, hypothesis testing, and Spark.
Data Science Interview Questions and Answers are not only beneficial for the fresher but also to any experienced person who is looking for a new stimulating job from a reputable company.
Top 50 Data Science Interview Questions and Answers
We have listed below Top 50 Data Science Interview Questions and Answers which are very simple to understand and will help you grow in your career.
1. What is Data Science?
This is the basic Data Science Interview Questions,
Data science helps you to study data. It includes developing techniques of recording, storing, and analyzing data to efficiently valuable general information. Data Science is used to adapt knowledge and insights from any available data.
2. Who is a Data Scientist?
This answer should be known to every person reading Data Science interview questions and answers,
A Data Scientist is a person who is qualified in mathematics, statistics and computer science. Data Scientists are experts in obtaining data from different sources and know-how to clean and process data, analyze and visualize data, make predictions about data and represent the output in the form of a conclusive story to the client.
3. What is the role of a Data Scientist?
Often asked in Data Science Interview Questions about the Role of Data Scientist.
Data scientists help the organizations to understand and manage data and resolve complex problems using proficiency in a variety of data fields. Data Scientists analyze and visualize data and make complicated things easier to convey it to the client. They have knowledge and experience in computer science, data modelling, statistics, analytics, and math – coupled with sharp business intelligence.
4. What technical skills should a Data Scientist have?
Data Scientist should possess the following technical skills-
- Mathematical Skills – College Arithmetic, Linear Algebra, Calculus
- Statistical Skills – Data Types, Summary Statistics, Correlation, Regression, Central Limit Theorem, T-test, ANOVA
- Programming Skills – ETL tools like Informatica, querying in SQL, Data Analysis in R & Python
5. List the main components of a Data Science project.
This knowledge is important in Data Science Interview Questions
- Understanding Business Requirement
- Data acquisition and preparation
- Data Analysis, Visualization & inference
- Project Management
6. What is Selection Bias and what are its types?
You should possess the knowledge of selection bias in Data Science Interview Questions.
Selection Bias is a type of error which arises when the data scientist decides what is going to be studied. It is typically related to research where the selection of participants isn’t random. It is also known as the selection effect. It is the distortion of statistical analysis, occurring due to the method of collecting samples. If the selection bias is not considered, then some of the results may not be accurate.
The types of selection bias include:
- Sampling Bias: It is a dynamic error occurring due to a non-random sample.
- Time Interval: The time interval bias happens when a trial may be terminated early at an extreme value with the most significant variance.
- Data: This error occurs either due to rejection of bad data on random grounds or due to selection of specific subcategories of data to support any conclusion.
- Attrition: Attrition bias is a type of selection bias caused due to the loss of participants overlooking trial tests that did not run to completion.
7. Why is Normal Distribution used in Data Science?
The Normal Distribution is crucial when it comes to Data Science. The Normal Distribution is the most widely used distribution. These are because it approximates a vast range of variables and decisions depending on its insights and has an excellent track record.
8. List the Properties of Normal Distribution.
This list is always asked in Data Science Interview Questions.
The properties of a Normal Distribution are as follows-
- Unimodal – one mode
- Symmetrical – left and right halves are mirror images
- Bell-shaped – mode at the mean
- Mean, Mode, and Median are all located in the center
9. What is A/B Testing?
if you are appearing for Data Science Interview Questions you are ought to know this answer.
A/B testing is a statistical hypothesis testing for a randomized experiment with two variables A and B. A/B Testing is carried out to recognize any changes made to the web page to maximize the result of interest. A/B testing is an excellent method for checking out the top online advertising and marketing strategies for your business. A/B testing is used to test everything from website copy to sales emails.
10. What is the difference between data science and big data?
The top favourite of the Data Science Interview Questions;
Big Data is the assembly of data which cannot be stored or processed using the conventional database system within the given period time. Big data does not only refer to the data that is either in gigabytes or terabytes or petabytes, or any data is more significant. Big data can be even a small amount of data depending on the context in which it is being used.
Big data can be classified depending on the below parameters:
- Volume – Currently, most of the data being generated has a very high volume. This is due to the evolution of technology since the past decade.
- Velocity – Due to the excess use of many web-based applications, the data produced by them is also extensive and rapid.
- Variety – Multiple kinds of data are being produced from different sources. We deal with this variety of data files, all at once.
- Veracity – Most of the data is messy and inaccurate. At times, we all have data which is incomplete or not up to the mark.
- Value – The information which is generated should have some value. It should not be rubbish.
Big data is the fuel required by Data Science to reach meaningful insights. Data Science requires skillsets like statistics, mathematics, and business domain knowledge.
Data science has the following advantages:
- It can reduce costs
- It helps to get into a new market
- Data Science can gauge the effectiveness of a marketing campaign
- It can launch a new product or service
11. List the difference between overfitting and underfitting in Data Science.
A data scientist should know this difference and that’s why included in Data Science Interview Questions. In statistics and Data Science, it is a common task to fit a model to a set of training data, to make steadfast estimations on usual untrained data.
Overfitting occurs when a model is hugely compound, like having multiple parameters comparative to the number of observations. A model that has been overfitted has weak predictive performance.
Underfitting occurs when a model or machine learning algorithm is not able to hold the underlying trend of the data. Underfitting may occur in case of fitting a linear model to a non-linear data. Such a model also would have weak predictive performance.
12. State the difference between univariate, bivariate and multivariate analysis.
- Univariate analysis is graphic statistical analysis techniques which can be distinguished depending on the number of variables present at a given point of time.
- Bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot.
- Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.
13. Why Cluster Sampling is used by Data Scientist?
Clustering is a technique of unsupervised learning and is a standard method for statistical data analysis used in various fields. Data Scientists use Cluster Sampling to obtain valuable insights from their data by studying what groups the data points fall into when a clustering algorithm is applied.
14. When does a Data Scientist use Systematic Sampling?
Systematic sampling comprises selecting items using a skip or sampling interval. The use of systematic sampling is more suitable compared to simple random sampling when a project’s budget is tight and needs ease of execution and understanding the outcomes of a study.
15. Define Eigenvectors and Eigenvalues in Data Science.
Eigenvectors are the guidelines along which a specific linear transformation acts by reversing, squeezing or extending. Eigenvectors are used for studying linear transformations. In data science, the eigenvectors are calculated for a correlation or covariance matrix.
Eigenvalues are also called as the strength of the transformation in the direction of eigenvector or the aspect by which the compression arises.
16. Differentiate between a Validation Set and a Test Set.
A Validation set can be considered as a part of the training set as it is used for parameter selection and to avoid overfitting of the model being built.
Alternatively, a Test Set is used for testing or estimating the act of a trained machine learning model.
In other words, the differences can be stated as; training set is to fit the parameters, i.e. weights and test set is to assess the performance of the model, i.e. evaluating the predictive power and generalization.
17. What is Cross-Validation and why it is used by Data Scientist?
Cross-Validation is a model validation method used for estimating how the results of statistical analysis will simplify an individual dataset. It is mostly used in backgrounds where the objective is to forecast or predict how precisely a model will accomplish in practice.
Cross-Validation is used by Data Scientist to term a data set to test the model in the training phase to limit problems like overfitting and get an insight into how the model will simplify an independent data set.
18. What do you understand by Supervised and Unsupervised Learning?
Supervised learning is the machine learning task of gathering a function from labeled training data.
Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks
Unsupervised learning is a type of machine learning algorithm used to draw readings from datasets containing the input data without labeled responses.
Algorithms: Clustering, Anomaly Detection, Neural Networks, and Latent Variable Models
19. Why do Data Scientists use Logistic Regression?
Logistic Regression is usually known as a logic model. It is a method to forecast the binary result from a linear combination of predictor variables. The vital representation in Logistic Regression is the co-efficients, just like linear Regression. The co-efficient in Logistic Regression is calculated using a method called maximum-likelihood estimation. It is effortless to make estimations using Logistic Regression. Also, the data preparation for Logistic Regression is much to that of Linear Regression.
20. Explain Recommender Systems in Data Science.
Recommender Systems are a subdivision of data filtering systems that are intended to forecast the ratings that a user would give to a product. Recommender systems are broadly used in different areas like movies, news, research articles, products, social tags, music, etc.
21. What is Linear Regression and why is it used in Data Science?
Linear Regression is a statistical method where the score of a variable Y is forecasted using the score of another variable X. Where variable X is denoted to as the predictor variable and variable Y is indicated as the criterion variable. It is used in Data Science for finding a linear relationship between the target and one more interpreter.
22. Define Collaborative filtering.
The Data Science Interview Questions are never complete without this question.
Collaborative filtering is the method of filtering adopted by most of the Recommender Systems to discover patterns or data by collaborating viewpoints, several data sources, and agents.
23. How does a Data Scientist treat outlier values?
Data Scientists can find out Outlier values by using graphical analysis techniques like univariate. If the count of outlier values is limited, then they can be calculated individually, but if there are a large number of outliers, the benefits can be replaced with either the 99th or the 1st percentile values.
24. Explain the number of clusters in a clustering algorithm.
Though the Clustering Algorithm is not specified, this question is mostly about K-Means clustering where “K” defines the number of clusters. Clustering aims to collect similar entities in a pattern that the objects within a group are identical to each other, but the groups are dissimilar from each other.
25. List and define the types of Machine Learning.
- Supervised learning
- Unsupervised learning
- Reinforcement Learning
26. What methods are used for Missing Value Treatments?
Central Imputation – This method acts more like central tendencies. All the missing values will be filed with mean and median mode respective to numerical and categorical data types.
KNN – K Nearest Neighbor imputation.
Distance between two or multiple attributes are calculated using Euclidian’s range, and the same will be used to treat the missing values. Mean, and mode will again be used as in CI.
27. What do you understand by Pearson correlation?
A Pearson correlation is a figure between -1 and 1 that specifies the degree to which two variables are linearly connected. The Pearson correlation is also called as the “product-moment correlation coefficient” or just “correlation.” Correlation between forecast and actual data can be observed and understood using this technique.
28. How and by what means Data Visualizations can be efficiently used in Data Science?
Data visualization can be used in Data Science in such a manner that it is not only limited to bar, line, or some stereotypic graphs. Data can be visualized in a much more attractive way. One thing should be kept in mind is to convey the projected insight or finding correctly to the audience. Delicate and artistic part can help you stand up with better viewing and functional dashboards. There is a vast difference between the simple, insightful dashboard and awesome looking fruitful insight dashboards.
29. How does a Data Scientist understand the problems faced during Data Analysis?
Most of the problem faced during data analysis is due to a lack of understanding of the problem in hand and focusing more on tools, results, and other parts of the project. A well experienced Data Scientist breaks the problem down to a granular level and understands it by considering each level of difficulty individually.
30. What are the advantages of Tableau Prep?
Tableau Prep saves a lot of time like the leading Tableau software and creates attractive visualizations. This software has a lot of abilities in taking professionals from data cleaning to creating final operational data which can be connected to Tableau desktop for getting visualizations and business insights. Therefore, the number of manual tasks are reduced, and the time is utilized to make better findings and insights. Check out Tableau Interview Questions and Answers.
31. What is the role of time series algorithms in Data Science?
Time series algorithms are fascinating to learn, use, and also to solve a lot of compound problems for businesses. Data preparation for time series analysis plays an important role. The stationarity, seasonality, cycles, and noises require much time and attention. You can take as much time as you would like to make the data right. With the help of this, you can run any model on top of it.
32. How can a Data Scientist achieve accuracy in his first model?
A good Data Scientist should have this knowledge and is often asked in Data Science Interview Questions.
Creating machine learning models includes a lot of hard work. Nearly 90% of accuracy models are not built in the very first attempt. To achieve accuracy Data Scientists spend a lot of time in the course of collecting, cleaning, and modifying data, because data is never clean. The method will help you to learn new designs in statistics, math, and probability.
33. What is the main responsibility of a Data Scientist?
Data Scientists have the primary responsibility to make complicated things simple so that anyone can understand them. It sometimes happens that simple data also seems to look complex when we try to present it. This frequently happens while doing Data Visualization. Therefore, Data Scientists offer data quickly with the help of a dashboard or a chart.
34. Why does SAS stand out to be the best over other Data Analytics tools?
Reasons behind SAS being the best Data Analytics tool are –
- Easy to understand: The requirements involved in SAS usually are easy to learn.
- Data Handling Capacities: SAS is the most leading tool as it also includes R & Python. It comes with functional graphical capacities and requires less knowledge field.
- Better tool management: It assists in releasing the updates concerning the controlled conditions.
35. Explain RUN-Group processing in Data Science.
To use RUN-group processing in Data Science, you should start the system and then submit many RUN-groups. A RUN-group is a group of records that has at least one product group containing ends with a RUN statement. It can include different SAS statements such as AXIS, BY, GOPTIONS, LEGEND, Power, or WHERE.
36. What you mean by ‘Definitions for BY-Group processing’?
‘Definitions for BY-Group Processing’ is a technique of preparing observations from one or more SAS data sets that are organized or ordered by the importance of individual or more shared variables. All data sets that are being connected should contain one or more BY variables.
37. What are the benefits of validating a SAS program?
- Less Rollouts – Every time a program is rolled out, it is usually followed by patches. This is done to fix bugs that were left out during validation.
- Prevents Data Corruption – Bugs can be drawn back to programs that have not been completely validated. Using the SAS program, you can prevent data corruption.
- Facilitates Communication – The requirements and useful specifications along with the test scripts can be developed in teamwork with the end-user. This leads to transparency between the user and the developer.
38. What is meant by precision and Recall?
The knowledge of precision & Recall is important and that is why always asked in Data Science Interview Questions.
- Recall: The recall is called as a true real rate. It is defined as the number of positives that your model has requested associated to the original specified number of positives available during this data.
- Precision: Precision is also called a positive predicted value. This relies on the prediction that specifies a time like a number of accurate positives that the model needs when compared to the number of positives it actually claims.
39. How does a Data Scientist use the F1 score?
The F1 score measure is nothing but the average of Precision and Recall of a model. Depending on the results, if the F1 score is 1, then it is classified as best, and if the F1 score is 0, then it is considered the worst.
40. State the difference between Machine learning vs Data Mining.
Data mining is about working on unlimited data and then removing it to a level anywhere the unusual and unknown patterns are identified. Machine learning is a process that provides specific computers with the capacity to learn.
41. Define Confounding Variables.
Confounding Variables are manifest variables in a scientific model that correlates directly or indirectly with both the subject and the objective variable.
42. List the types of biases that can occur during sampling.
Listed below are the types of biases that can occur during sampling –
- Selection bias
- Under coverage bias
- Survivorship bias
43. Which Python library is used for Data Visualization?
Plot Ly also called as Plot.ly, is used as a python library for Data Visualization. It is a collaborative online visualization tool that is being used for data analytics, scientific graphs, and other visualization.
44. Why is an import statement used in Python?
To make use of the functions in a module, we need to import the module with an import statement in Python. An import statement comprises of the import keyword along with the name of the module.
45. What is alias in the import statement? Why is it used by Data Scientist?
Aliases are used in import statements for simplifying the work. If the imported module has a lengthy name as import multiprocessing. Every time we want to access any script present in the multiprocessing module, we need to use the word multiprocessing. Therefore, if an alias is used, we can easily replace the words multiprocessing with mp.
46. Are the aliases used for a module fixed/static?
Aliases are not at all pre-fixed. You can name the alias as per your convenience. Nevertheless, the documentation of a respective module sometimes requires the alias to be used for ease of understanding.
47. When is a nonparametric test used by Data Scientist?
Non-parametric tests do not consider that the data follow a specific distribution. They are used whenever the data does not satisfy the assumptions of a parametric test.
48. Mention the steps in exploratory Data Analysis.
- Make a summary of observations
- Define central tendencies of the dataset
- Describe the shape of data
- Identify potential associations
- Develop insight into errors, missing values and major deviations
49. List the types of data available in Enterprises.
- Structured data
- Unstructured data
- Big data from different sources social media, surveys, etc.
- Machine-generated data from instruments
- Real-time data feeds
50. State difference between Primary Data and Secondary Data.
- Primary Data: Data collected by self is primary data. This data is collected afresh and for the first time.
- Secondary Data: Someone else has collected the data and is used by you is called secondary data.
This Data Science Interview Questions will help you boost the confidence required to crack the interview. We have included all the necessary Data Science Interview Questions in this article, so keep this handy when you are about to face an interview.