As per our recent survey, data scientist is one of the most demanding jobs in the 21st Century. It is a multidisciplinary field concerned with scientific methods, processes, and systems for extracting knowledge from diverse types of data and making decisions based on that knowledge.
Data scientists should be judged not only on their machine learning skills but also on their statistical knowledge. So, we’ve prepared an extensive list of Data Science interview questions and answers that help you hire Data Scientists. We start with the basics, which will help you determine their proficiency with basic concepts, and move to the expert status.
Data Scientist Interview Questions are categorized into three main parts. You may jump to the ones that are relevant to you
I) Statistical Data Science Questions
1. Your role will require you to analyze, visualize and maintain data in the first few months, will you be fine with that?
Data analysis, visualization, and maintaining data is one of the primary roles of being a data scientist. Thus, one should understand the key role and take up the work in a similar manner.
2. Do you understand the term regularization, tell me how useful it is?
Regularization is a technique used for tuning the function by adding an additional penalty term in the error function. The additional term controls the excessively fluctuating function such that the coefficients don't take extreme values.
3. How will your role be different from ML or AI (Machine Learning – Artificial Intelligence)?
The role of the data scientist encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis.
4. Which technique will you use to predict categorical responses?
ANOVA, or analysis of variance, is to be used when the target variable is continuous and the dependent variables are categorical.
5. Why is normal distribution important?
The normal distribution is an asymmetrical continuous probability distribution with most observations clustering around the center peak and probabilities for values further away from the mean tapering off equally in both directions.
As normal distribution fits many natural phenomena, it is the most important probability distribution in statistics.
6. Can you talk about Eigenvalue and Eigenvector?
The construction of one vector with one value to represent a big matrix is what eigenvalues and eigenvectors are all about. A square matrix's eigenvector is an array with n elements, where n is the number of rows/columns.
7. Explain the box cox transformation in regression models.
A Box-Cox transformation turns non-normal dependent variables into normal shapes. Many statistical approaches rely on the assumption of normality; if your data isn't normal, using a Box-Cox allows you to conduct a larger number of tests.
8. Can you use machine learning for time series analysis?
Yes, there are several types of models that can be used for time-series forecasting. Thus, the approaches can be different according to applications.
9. Do you know the ways to perform logistic regression with Microsoft Excel?
There are two answers to this:
Use fundamentals of logistic regression and use Excel’s computational power to build a logistic regression
Use Add-ins provided by third parties.
10. Do you know the formula to calculate R-square?
The R-squared formula is calculated by dividing the sum of the first errors by the sum of the second errors and subtracting the derivation from 1.
(Residual Sum of Squares/ Total Sum of Squares) or R-squared = 1 – (First Sum of Errors / Second Sum of Errors).
11. Explain what precision and recall are. How do they relate to the ROC curve?
The answer will have four results:
- TN / True Negative: the case was negative and predicted negative
- TP / True Positive: the case was positive and predicted positive
- FN / False Negative: the case was positive but predicted negative
- FP / False Positive: the case was negative but predicted positive
12. In Machine Learning, what is a perceptron?
In the field of machine learning. Perceptron is a supervised classification technique that converts input into one of several non-binary outputs.
13. Applications of Machine Learning?
- Self Driving Cars
- Image Classification
- Text Classification
- Search Engine Banking,
- Healthcare Domain
14. What is Null Deviance and Residual Deviance (Logistic Regression Concept?)
Null Deviance indicates the response predicted by a model with nothing but an intercept Residual deviance indicates the response predicted by a model on adding independent variables Note: Lower the value, better the model
15. What are the different methods to split the tree into a decision tree?
Information gain and Gini index
16. What is the weakness of the Decision Tree Algorithm?
Not suitable for continuous/Discrete variable
Performs poorly on small data
17. How to ensure we are not overfitting the model?
Keep the attributes/Columns which are really important Use K-Fold cross-validation techniques to make use of drop-out in the case of a neural network.’
18. What is a hyperplane in SVM?
It is a line that splits the input variable space and it is selected to best separate the points in the input variable space by their class(0/1, yes/no).
19. Explain Bigram with an Example?
Eg: I Love Data Science Bigram – (I Love) (Love Data) (Data Science)
20. What are the different activation functions in neural networks?
Relu, Leaky Relu, Softmax, Sigmoid
21. What is Machine Learning?
Machine learning is the process of generating predictive power using past data(memory). It is a one-time process where the predictions can fail in the future (if your data distribution changes).
22. Can you use machine learning for time series analysis?
Yes, it can be used but it depends on the applications.
23. Why do L1 regularizations cause parameter sparsity whereas L2 regularization does not?
Regularizations in statistics or in the field of machine learning are used to include
some extra information in order to solve a problem in a better way. L1 & L2
Regularizations are generally used to add constraints to optimization problems.
24. What is the difference between Regression and classification ML techniques?
Both Regression and classification machine learning techniques come under Supervised machine learning algorithms. In a Supervised machine learning algorithm, we have to train the model using the labeled dataset, while training we have to explicitly provide the correct labels and the algorithm tries to learn the pattern from input to output.
25. What does P-value signify about the statistical data?
P-value is used to determine the significance of results after a hypothesis test in statistics.
P-value helps the readers to draw conclusions and is always between 0 and 1.
- P-Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.
- P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.
- P-value=0.05is the marginal value indicating it is possible to go either way.
26. Is it true that all gradient descent methods converge to the same point?
No, they don't, because it can reach a local minimum or optima point in some circumstances. You don't get to the global optimum. It is dependent on the facts and the starting circumstances.
27. What is the difference between Supervised Learning and Unsupervised Learning?
Supervised Learning occurs when an algorithm learns something from the training data and then applies that knowledge to the test data. Supervised Learning is an example of classification. Unsupervised learning occurs when the algorithm does not learn anything prior since there is no response variable or training data. Clustering is a good example of this.
28. What exactly is the purpose of A/B testing?
It's a statistical hypothesis test for a randomized experiment involving two variables A and B. A/B Testing is used to detect any adjustments to a web page that will maximize or raise the outcome of interest. Identifying the click-through rate for a banner ad is an example of this.
29. What are the many steps that an analytics project entails?
- Recognize the issue at hand.
- Become acquainted with the data by exploring it.
- Clean up the data for modeling by looking for outliers, missing values, and changing variables, among other things.
Use a new data set to test the model.
- Begin implementing the model and track the results to evaluate the model's performance over time.
30. How can you cycle through a list while also retrieving element indices?
This can be done with the enumerate function, which takes every element in a sequence and inserts its location directly before it, similar to a list.
31. What is pruning in a Decision Tree?
When we remove sub-nodes of a decision node, this process is called pruning or the opposite process of splitting.
32. What is the definition of selection bias?
Selection bias is produced when persons, groups, or data are chosen for study in such a way that adequate randomization is not achieved, resulting in a sample that is not representative of the population being studied. The selection effect is a term used to describe this phenomenon. The term "selection bias" usually refers to a statistical analysis that is distorted as a result of the sample collection procedure. If the selection bias is not taken into consideration, some of the study's conclusions may be incorrect.
Also Read: Online Data Science & Analytics Test
II) Technical data science interview questions
Questions Related To NumPy, Django, Python Data Science Questions:
33. What is Data Science?
Formally, It’s the way to Quantify your intuitions. Technically, Data Science is a combination of Machine Learning, Deep Learning & Artificial Intelligence. Where Deep Learning is the subset of AI.
Name a few libraries that are used in python for data analysis?
- Scikit learn
- Matplotlib\ seaborn
34. What are the different types of data?
Data is broadly classified into two types 1) Numerical 2) Categorical
Numerical variables are further classified into discrete and continuous data Categorical variables Systematic Sampling Stratified Sampling Quota Sampling are further classified into Binary, Nominal, and Ordinal data.
35. What is a lambda function in python?
Lambda functions are used to create small, one-time anonymous functions in python. It enables the programmer to create functions without a name and almost instantly.
36. What is monkey patching and is it ever a good idea?
A monkey patch is a piece of runtime programming that extends or modifies other code. That is, while the program is executing, it changes the module or class. It's a good idea to have monkey patching as it’s a solution for a serious problem with obvious consequences.
37. What is the difference between lists and tuples?
In other languages, lists are declared similarly to arrays. In Data Structures, a list is a sort of container that may hold numerous pieces of data at once. Lists are a good way to keep track of a succession of data and iterate over it.
The tuple is another sequence data type that can contain elements of many data types, but it is immutable. A tuple, in other terms, is a set of Python objects separated by commas. Because of its static nature, the tuple is faster than the list.
38. How is memory managed in Python?
Blocks of memory are managed by Python memory management. The "Pool" is made up of a group of identical blocks. Arenas create pools, which are 256kB memory chunks assigned to heap=64 pools. If the objects are destroyed, the memory management creates a new object of the same size to take their place.
39. Explain what Flask is and its benefits?
Flask is an open-source web framework. This means flask gives you the tools, frameworks, and technologies you need to create a web app. This web application can be as simple as a set of web pages, a blog, or a wiki, or as complex as a web-based calendar or a commercial website.
There are several compelling reasons to utilize flask as a web application framework. Like-
- Support for unit testing that is integrated
- A built-in development server and a quick debugger are included.
- Unicode base restful request dispatch
- Cookies are supported.
- Templating WSGI 1.0 compliant jinja2
- Furthermore, the flask provides you with superior control over the development of your project.
- Function for handling HTTP requests
- Flask is flexible and lightweight, making it simple to integrate into a web framework with a few extensions.
- You can connect to your preferred device. The ORM Basic core API is well-designed and well-coordinated.
- Highly adaptable
- The flask is simple to use in production.
40. What advantages do NumPy arrays offer over (nested) Python lists?
NumPy arrays are more compact and faster than Python lists. An array uses less memory and is easier to work with. NumPy stores data in a substantially smaller amount of memory and has a way of specifying data types. This enables even further optimization of the code.
41. What are the most crucial data analysis skills to have in Python?
The abilities listed below are some of the most important to have while performing data analysis with Python.
- Knowledge of the built-in data types, particularly lists, dictionaries, tuples, and sets.
- N-dimensional NumPy Arrays are mastered.
- Dataframes in Pandas is mastered.
- NumPy arrays can conduct element-wise vector and matrix computations.
42. Name a few libraries that are used in python for data analysis?
- Scikit learn
- Matplotlib\ seaborn
43. What if you merge two lists and get only unique values?
List a = [1,2,3,4] List b= [1,2,5,6] A = list(set(a+b))
44. What is ensemble learning, and how does it work?
The art of mixing many models to anticipate the ultimate outcome of an experiment is known as ensemble learning. Bagging, boosting, and stacking are common ensemble approaches.
45. How will you know if you have overfitted?
When you develop a model with a high model accuracy on the train data set but a low prediction accuracy on the test data set, you've overfitted it.
46. What is a Computational Graph?
The foundation of a TensorFlow is the creation of a computational graph. It consists of a network of nodes, each of which performs a function. The nodes represent mathematical operations, while the edges represent tensors. It's also known as a "DataFlow Graph" since data flows in the shape of a graph.
R Data Science Interview Questions -
47. What are the key differences between R and Python?
Some of the common differences between R and Python are:
- R is mostly used for statistical analysis, whereas Python offers a more comprehensive data science approach.
- R's major goal is data analysis and statistics, whereas Python's primary goal is deployment and production.
- Scholars and R&D professionals are the majority of R users, whereas Programmers and Developers constitute the majority of Python users.
- R allows you to leverage pre-existing libraries, but Python allows you to create new models from scratch.
- R is tough to understand at first, but Python is linear and easy to pick up.
- Locally, R is integrated with Run, but Python is well-integrated with applications.
- R and Python are both capable of handling large databases.
- R is supported by the R Studio IDE, while Python is supported by Spyder.
48. What are the different data types in R?
R has 6 basic data types.
- numeric (real or decimal)
49. Why use R?
R is a programming language, not only a statistics package and it is built to work in the same manner that people think about problems. R is a versatile and powerful programming language.
50. Why would you use factor variables?
Factor variables can be utilized in statistical modeling where they will be correctly implemented, i.e., the correct amount of degrees of freedom will be assigned to them. Factor variables are also useful in a variety of graphic kinds.
51. How do you concatenate strings in R?
Use the paste() function in R programming to concatenate strings. The paste() function's syntax for concatenating two or more strings.
52. How many sorting algorithms are available in R?
In the R programming language, data can be sorted in a variety of ways. In the R programming language, there are a variety of algorithms for sorting data. Different types of sorting functions are explained below.
- Bubble Sort
- Insertion Sort
- Selection Sort
- Merge Sort
- Quick Sort
53. Do you know how to make an R decision tree?
Decision Trees are a powerful Machine Learning algorithm that may be used for both classification and regression. They are extremely powerful algorithms that can fit large datasets. Furthermore, decision trees are essential components of random forests, one of the most powerful Machine Learning algorithms currently available.
54. Can you use R to predict data analysis?
Predictive analysis in R is a type of analysis that employs statistical operations to examine previous data in order to forecast future events. In data mining and machine learning, it's a prevalent term. Time series analysis, non-linear least squares, and other techniques are used.
55. How are missing values represented?
Missing values in R are denoted by the letter NA (not available). The symbol NaN is used to signify impossible values (domain errors such as division by 0 and logs of negative numbers) (Not-A-Number). For both numeric and textual data, NA is used.
56. Explain what is transpose.
Reversing rows and columns of a matrix is known as transposing it. Transposing data frames in R and Python is not a problem because they are actually matrices. A SQL Server table has a somewhat different structure, with rows and columns that are not interchangeable and equivalent. You may, however, receive data in SQL Server in the form of a matrix from other systems and need to transpose it.
57. What are the top 2 advantages of R?
Open source: An open-source language is one that can be used without the need for a license or payment. R is a free and open-source programming language. By optimizing our packages, creating new ones, and addressing difficulties, we may contribute to the development of R.
Platform Independent: R is a platform-agnostic or cross-platform programming language, which means that its code runs on any operating system. R allows programmers to create software for a variety of platforms by writing only one program. R is simple to install and use on Windows, Linux, and Mac.
58. The memory limit of R is 3Gb or 8 Gb?
The —max-mem-size command-line parameter specifies the maximum amount of memory that can be used (including a very small amount of housekeeping overhead). On 32-bit Windows, this cannot exceed 3 GB, and most versions are limited to 2 GB. The current limit for 64-bit versions of R running on 64-bit Windows is 8 terabytes.
59. Can you create a new variable in R programming?
In R, creating a new variable or transforming an existing variable into a new one is usually an easy job.
The most commonly used function is the new variable <- old variable. In a data frame, variables are always introduced horizontally. To generate additional variables, the operators * for multiplying, + for addition, - for subtraction, and / for division are commonly employed.
60. What is the use of a coin package in R?
The coin package implements a broad framework for permutation tests, which are a type of conditional inference process.
61. What is logistic regression in R?
In R Programming, logistic regression is a classification algorithm for determining the probability of event success and failure. When the dependent variable is binary (0/1, True/False, Yes/No), logistic regression is utilized.
62. What are iPlots?
IPLOT is a (relatively) basic interactive charting technique for X and Y coordinates. It makes use of the PGPLOT plotting software, which allows the user to select the plot output device: GraphOn terminal, X-window terminal, PostScript file, and so on.
63. What Is Linear Regression?
Modeling the relationship between a scalar variable y and one or more variables denoted X. In linear regression, models of the unknown parameters are estimated from the data using linear functions. polyfit( x,y2,1) %return 2.1667 -1.3333, i.e 2.1667x-1.3333.
64. What Are The Different R Language Advantages?
R is a programming language that comes with a software suite for graphical representation, statistical computation, data management, and calculation.
Among the highlights are:
- A large number of data analysis tools are available.
- Operators for calculating with matrices and arrays
- For graphical representation, use a data analysis technique.
- A highly developed programming language that is also easy and effective.
- It provides a lot of help for machine learning applications.
- It serves as a link between different applications, tools, and datasets.
65. Give An Example Of Inferential Statistics?
Example of Inferential Statistic:
You asked five of your classmates about their height. On the basis of this information, you stated that the average height of all students in your university or college is 67 inches.
Questions related to Tableau -
66. What is the difference between context filters to other filters?
A normal filter operates independently of the other filters and examines the complete dataset at all times. The context filter has a higher rank, he is prioritized when it comes to filtering. This means he filters the entire dataset and passes the filtered output along to the regular filter folks. These conventional filters can now work with the context filter's result dataset.
67. Max no of the tables we can join in Tableau?
Tableau allows its user to join a maximum of 32 tables.
68. What are Dimensions and Facts?
The quantitative metrics or measurable quantities of the data that may be studied by a dimension table are known as facts. Facts are records in the Fact table that contain foreign keys that refer to the dimension tables in a unique way. The fact table enables atomic data storage, allowing for a greater number of records to be inserted at once.
Dimensions are descriptive attribute values for each attribute's various dimensions, defining multiple qualities. The fact table contains a dimension table with a reference to a product key.
69. What is the dual-axis & blended axis?
Dual Axis: When two measurements are utilized in dual lines graphs or charts, this is the axis to use. The first axis indicates one measure, while the second axis represents the second measure.
Blended Axis: When more than two measurements are utilized in multi-line graphs or charts, this axis is employed. Blended Axis is also an option to consider when two metrics must be displayed on the same axis.
70. Are expected values and mean values different?
The concepts are not interchangeable, but they are used in various contexts. When discussing a probability distribution or sample population, the term "mean" is used, whereas "expected value" is used when discussing a random variable.
III) Face to Face Data Science Interview Questions
71. In which libraries for Data Science in Python and R does your strength lie?
Python is widely used for web and software development, task automation, data analysis, and data visualization. Python has been used by many non-programmers, such as accountants and scientists, for a variety of common tasks, such as arranging finances, due to its relative ease of learning.
The R Core Team and the R Foundation for Statistical Computing maintain R, a programming language and free software environment for statistical computing and graphics. For designing statistical software and data analysis, it is commonly utilized by statisticians and data miners.
Thus, answer this question smartly depending upon your role and thinking.
72. Suppose you are given a data set, what will you do with it to find out if it suits the business needs of your project or not.
73. What unique skills do you think you can add to our data science team?
- Math and Statistics.
- Analytics and Modeling.
- Machine Learning Methods.
- Data Visualization.
- Intellectual Curiosity.
- Business Acumen.
74. Is more data always better?
The key reason that data is desirable is that it adds to the dataset's value by providing additional information. However, if the newly created data is identical to or just repeats previous data, there is no benefit to having more data.
75. What are your favorite data visualization tools?
Here are some of the best data visualization tools every Data Scientist must use:
- Microsoft Power BI
- E Charts
76. What do you think is the life cycle of a data science project in our company?
Business Understanding, Data Understanding, Data Preparation, Modeling, Validation, and Deployment are the six phases.
77. Which is better - too many false positives, or too many false negatives? You can give examples.
78. How do you clean up and organize big data sets?
Data cleaning is basically a process of ensuring data is correct, consistent, and usable. You can clean up and organize big data sets by following these steps:-
- Monitor errors
- Standardize your process
- Validate data accuracy
- Scrub for duplicate data
- Analyze your data
- Communicate with your team
79. What opportunities will data science bring in the near future?
According to Tim Burner Lee(the inventor of the world wide web): “Data is a precious thing and will last longer than the systems themselves”
By the above statement, it’s clear that proliferation will never end and because of that, the use of data-related technologies like Big Data and Data Science will increase day by day.
80. Ask industry-specific questions related to data types, domain knowledge, etc.
81. How could you collect and analyze data to use social media to predict the weather?
We can collect social media data using Twitter, Facebook, Instagram API. Then, for example, for Twitter, we can construct features from each tweet, e.g. the tweeted date, number of favorites, retweets, and of course, the features created from the tweeted content itself. Then use a multivariate time series model to predict the weather.
82. How would you design an experiment to determine the impact of latency on user engagement?
The best way I know to quantify the impact of performance is to isolate just that factor using a slowdown experiment, i.e., add a delay in an A/B test.
83. What is the difference between univariate, bivariate, and multivariate analysis?
Univariate analysis is performed on one variable, bivariate on two variables, and multivariate analysis on two or more variables.
84. What is the difference between interpolation and extrapolation?
Extrapolation is the estimation of future values based on the observed trend in the past. Interpolation is the estimation of missing past values within two values in a sequence of values.
85. What employment experience do you have that is relevant?
A clear question with limited room for interpretation. Make sure you've done your homework before the interview. Examine the job description carefully to see how your previous work experience will help you handle the obligations of this new role. Try to be as specific as possible when describing the actions you learned in past positions. Explain how these will help you succeed in this new position.
86. In R, how are missing values and impossible values handled?
When working with real data, one of the most difficult difficulties in dealing with missing values. In R, these are denoted by the letter NA. NAN is used to express impossible values (for example, division by 0). (not a number).
87. In five years, where do you see yourself?
This is a potentially hazardous question. Whether you're searching for a job to tide you over or a career, the interviewer wants to know if the organization can trust you in the long run. Aside from recruiting someone who is qualified and talented, most businesses prefer to hire someone who believes in the company's future. They don't want to spend a lot of time and money recruiting and training someone who will leave in two years.
88. What are your impressions of our organization?
It's difficult to overestimate the value of earlier research. You only have seven seconds to make an excellent first impression! Doing your homework is a surefire method to make the interviewers like you. As soon as the interview begins, it will become apparent.
89. What are your top three personal assets?
This interview question allows you to emphasize three points:
- Your understanding of the job description and how your skills and experience align with it.
- Demonstrate that you've done your homework and are familiar with the job's requirements.
- Then, emphasize your own qualities and prior experience that make you qualified for it.
90. Do you have any flaws?
This question counteracts the previous one.
- Genuineness and authenticity are crucial once again. Don't makeup flaws at random.
- Demonstrate self-awareness and the wisdom and humility to know that you have room for improvement.
- Describe one or two (or three, depending on how many they ask) professional flaws and how you overcame them.
91. What Is Your Expected Salary?
As a general guideline, do not discuss salary in an interview unless it is specifically requested.
If they do, make sure you give them a reasonable figure. When you present an accurate pay, it indicates your knowledge and experience in similar roles; when you present a salary that is too high or low, it does the reverse.
92. Do you have any questions that you'd want to ask us?
In most interviews, this is the last question you'll be asked. Don't simply say "no"! Taking this approach is a missed opportunity to brag. It's another wonderful opportunity to show off your industry (or organization) knowledge by asking a thoughtful inquiry.
93. What is the difference between hard work and smart work?
Every breadwinner nowadays, whether a rickshaw puller or a daily wager, does is hard work. Smart work is what educated people like us are supposed to do, and some of us, like my father, are actually doing it. The secret to success is a well-balanced blend of hard labor and smart work.
94. How soon can you learn new technologies?
I can easily adapt to different situations. I believe I have the capacity to learn quickly and utilize my new information since I am clear about my job function and mentally prepared to take on challenges.
95. What software packages do you have experience with?
Make sure you have a full understanding of each of your abilities. If you are unfamiliar with a particular software product or terminology, do not discuss it with the interviewer.
96. How would you grade yourself as a leader on a scale of 1 to 10?
HR interview questions and responses are a technique of determining whether or not you are qualified for the position. As a result, rate yourself and add pertinent points that add to the worth of this inquiry.
97. Are you willing to take chances? or Do you enjoy trying new things?
It's always a good idea to test new waters and technology. I am a very adaptable individual, and my persistence allows me to quickly pick up new skills.
98. What are your long-term objectives? Tell me about your immediate and long-term objectives.
My short-term goal is to work for a reputable organization, such as yours, where my job function will allow me to apply my skills and knowledge. In the long run, I want to be recognized for my contribution to the organization.
99. What motivates you?
You can draft your answer based on your role. Like for a sales representative, building a connection with the audience is beneficial, inspiring, and exciting.
100. What are some of your interests? or What is it that you are enthusiastic about?
Here you can discuss your own hobby. This is a question that appears in almost every list of "HR interview questions and answers."
I hope that this set of Data Science Interview Questions and Answers will aid you in your preparation for the Data science interview test and as well as while hiring the Data Scientist for your company.
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