Python Data Science Interview Questions
1. What tools or devices help you succeed in your role as a data scientist?
Answer: This question’s purpose is to learn the programming languages and applications the candidate knows and has experience using. The answer will show the candidate’s need for additional training of basic programming languages and platforms or any transferable skills. This is vital to understand as it can cost more time and money to train if the candidate is not knowledgeable in all of the languages and applications required for the position.
Answers to look for include:
Experience in SAS and R programming
Understanding of Python, PHP or Java programming languages
Experience using data visualization tools
“I believe I can excel in this position with my R, Python, and SQL programming skillset. I enjoy working on the FUSE and Tableau platforms to mine data and draw inferences.”
2. Python or R – Which one would you prefer for text analytics?
Answer: The best possible answer for this would be Python because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.
3. What are an Eigenvalue and Eigenvector?
Answer: Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching. Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.
4. How do you define data science?
Answer: This question allows you to show your interviewer who you are. For example, what’s your favorite part of the process, or what’s the most impactful project you’ve worked on? Focus first on what data science is to everyone – a means of extracting insights from numbers – then explain what makes it personal.
5. Do you prefer Python or R for text analytics?
Answer: Here, you’re being asked to insert your own opinion. However, most data scientists agree that the right opinion is Python. This is because Python has Pandas library which has strong data analysis tools and an easy-to-use structure. What’s more, Python is typically faster for text analytics.
6. What are the various steps involved in an analytics project?
Answer: Understand the business problem
Explore the data and become familiar with it.
Prepare the data for modeling by detecting outliers, treating missing values, transforming variables, etc.
After data preparation, start running the model, analyze the result and tweak the approach. This is an iterative step until the best possible outcome is achieved.
Validate the model using a new data set.
Start implementing the model and track the result to analyze the performance of the model over the period of time. (E learning portal)
7. How will you define the number of clusters in a clustering algorithm?
Answer: Though the Clustering Algorithm is not specified, this question will mostly be asked in reference to K-Means clustering where “K” defines the number of clusters. The objective of clustering is to group similar entities in a way that the entities within a group are similar to each other but the groups are different from each other.
For example, the following image shows three different groups.
K Mean Clustering Machine Learning Algorithm
Within Sum of squares is generally used to explain the homogeneity within a cluster. If you plot WSS for a range of a number of clusters, you will get the plot shown below. The Graph is generally known as Elbow Curve.
Data Science Interview Questions K Mean Clustering
red circled a point in above graph i.e. Number of Cluster =6 is the point after which you don’t see any decrement in WSS. This point is known as bending point and taken as Kin K – Means.
This is the widely used approach but few data scientists also use Hierarchical clustering first to create dendrograms and identify the distinct groups from there.
8. How Do Data Scientists Use Statistics?
Answer: Statistics helps Data Scientists to look into the data for patterns, hidden insights and convert Big Data into Big insights. It helps to get a better idea of what the customers are expecting. Data Scientists can learn about consumer behavior, interest, engagement, retention and finally conversion all through the power of insightful statistics. It helps them to build powerful data models in order to validate certain inferences and predictions. All this can be converted into a powerful business proposition by giving users what they want at precisely when they want it.
9. Describe Univariate, Bivariate And Multivariate Analysis?
Answer: As the name suggests these are analysis methodologies having a single, double or multiple variables.
So a univariate analysis will have one variable and due to this, there are no relationships, causes. The major aspect of the univariate analysis is to summarize the data and find the patterns within it to make actionable decisions.
A Bivariate analysis deals with the relationship between two sets of data. These sets of paired data come from related sources, or samples. There are various tools to analyze such data including the chi-squared tests and t-tests when the data are having a correlation.
If the data can be quantified then it can be analyzed using a graph plot or a scatterplot. The strength of the correlation between the two data sets will be tested in a Bivariate analysis. ( data science online training )
10. What is a Linear Regression?
Answer: Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.
11. What devices or tools help you most as a data scientist?
Answer: By asking this question, recruiters are seeking to learn more about your qualifications. Explain how you utilize every coding language you know, from R to SQL, and how each language helps complete certain tasks. This is also an opportunity to explain more about how your education or methods go above and beyond.
12. How have you overcome a barrier to finding a solution?
Answer: This question directly asks you to draw upon your experiences and your ability to problem-solve. Data scientists are, after all, numbers-based problem-solvers, so, it’s important to determine an example of a problem you’ve solved ahead of time. Whether it’s through re-cleaning data or using a different program, you should be able to explain your process to the recruiter.
13. What do you understand by term hash table collisions?
Answer: Hash table (hash map) is a kind of data structure used to implement an associative array, a structure that can map keys to values. Ideally, the hash function will assign each key to a unique bucket, but sometimes it is possible that two keys will generate an identical hash causing both keys to point to the same bucket. It is known as hash collisions.
14. What is Machine Learning?
Answer: Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. Closely related to computational statistics. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.
15. What are the variants of Back Propagation?
Answer: Stochastic Gradient Descent: We use only a single training example for calculation of gradient and update parameters.
Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is used.
16. What do you understand by the Selection Bias? What are its various types?
Answer: Selection bias is typically associated with research that doesn’t have a random selection of participants. It is a type of error that occurs when a researcher decides who is going to be studied. On some occasions, selection bias is also referred to as the selection effect.
In other words, selection bias is a distortion of statistical analysis that results from the sample collecting method. When selection bias is not taken into account, some conclusions made by a research study might not be accurate. Following are the various types of selection bias:
Sampling Bias – A systematic error resulting due to a non-random sample of a populace causing certain members of the same to be less likely included than others that results in a biased sample.
Time Interval – A trial might be ended at an extreme value, usually due to ethical reasons, but the extreme value is most likely to be reached by the variable with the most variance, even though all variables have a similar mean.
Data – Results when specific data subsets are selected for supporting a conclusion or rejection of bad data arbitrarily.
Attrition – Caused due to attrition, i.e. loss of participants, discounting trial subjects or tests that didn’t run to completion.
17. Could you explain how to define the number of clusters in a clustering algorithm?
Answer: The primary objective of clustering is to group together similar identities in such a way that while entities within a group are similar to each other, the groups remain different from one another.
Generally, Within Sum of Squares is used for explaining the homogeneity within a cluster. For defining the number of clusters in a clustering algorithm, WSS is plotted for a range pertaining to a number of clusters. The resultant graph is known as the Elbow Curve.
The Elbow Curve graph contains a point that represents the point post which there aren’t any decrements in the WSS. This is known as the bending point and represents K in K–Means.
Although the aforementioned is the widely-used approach, another important approach is the Hierarchical clustering. In this approach, dendrograms are created first and then distinct groups are identified from there.
18. Please explain Gradient Descent.
Answer: The degree of change in the output of a function relating to the changes made to the inputs is known as a gradient. It measures the change in all weights with respect to the change in error. A gradient can also be comprehended as the slope of a function.
Gradient Descent refers to escalating down to the bottom of a valley. Simply, consider this something as opposed to climbing up a hill. It is a minimization algorithm meant for minimizing a given activation function.
19. Please explain the concept of a Boltzmann Machine.
Answer: A Boltzmann Machine features a simple learning algorithm that enables the same to discover fascinating features representing complex regularities present in the training data. It is basically used for optimizing the quantity and weight for some given problem.
The simple learning algorithm involved in a Boltzmann Machine is very slow in networks that have many layers of feature detectors.
20. How and by what methods data visualizations can be effectively used?
Answer: In addition to giving insights in a very effective and efficient manner, data visualization can also be used in such a way that it is not only restricted to bar, line or some stereotypic graphs. Data can be represented in a much more visually pleasing manner.
One thing has to be taken care of is to convey the intended insight or finding correctly to the audience. Once the baseline is set. Innovative and creative part can help you come up with better looking and functional dashboards. There is a fine line between the simple insightful dashboard and awesome looking 0 fruitful insight dashboards.
21. What is the basic responsibility of a Data Scientist?
Answer: As a data scientist, we have the responsibility to make complex things simple enough that anyone without context should understand, what we are trying to convey.
The moment, we start explaining even the simple things the mission of making the complex simple goes away. This happens a lot when we are doing data visualization.
Less is more. Rather than pushing too much information on to readers brain, we need to figure out how easily we can help them consume a dashboard or a chart.
The process is simple to say but difficult to implement. You must bring the complex business value out of a self-explanatory chart. It’s a skill every data scientist should strive towards and good to have in their arsenal.
22. What is the best Programming Language to use in Data Science?
Answer: Data Science can be handled by using programming languages like Python or R programming language. These two are the two most popular languages being used by the Data Scientists or Data Analysts. R and Python are open source and are free to use and came into existence during the 1990s.
Python and R have different advantages depending on the applications and required a business goal. Python is better to be used in the cases of repeated tasks or jobs and for data manipulations whereas R programming can be used for querying or retrieving datasets and customized data analysis.
Mostly Python is preferred for all types of data science applications where some time R programming is preferred in the cases of high or complex data applications. Python is easier to learn and has less learning curve whereas R has a deep learning curve.
Python is mostly preferred in all the cases which is a general-purpose programming language and can be found in many applications other than Data Science too. R is mostly seen in Data Science area only where it is used for data analysis in standalone servers or computing separately.
23. What Is A Recommender System?
Answer: A recommender system is a today widely deployed in multiple fields like movie recommendations, music preferences, social tags, research articles, search queries and so on. The recommender systems work as per collaborative and content-based filtering or by deploying a personality-based approach. This type of system works based on a person’s past behavior in order to build a model for the future. This will predict future product buying, movie viewing or book reading by people. It also creates a filtering approach using the discrete characteristics of items while recommending additional items.
24. Explain The Various Benefits Of R Language?
Answer: The R programming language includes a set of a software suite that is used for graphical representation, statistical computing, data manipulation, and calculation.
Some of the highlights of the R programming environment include the following:
An extensive collection of tools for data analysis
Operators for performing calculations on matrix and array
Data analysis technique for graphical representation
A highly developed yet simple and effective programming language
It extensively supports machine learning applications
It acts as a connecting link between various software, tools, and datasets
Create high-quality reproducible analysis that is flexible and powerful
Provides a robust package ecosystem for diverse needs
It is useful when you have to solve a data-oriented problem
25. What Are The Various Aspects Of A Machine Learning Process?
Answer: In this post, I will discuss the components involved in solving a problem using machine learning.
This is the first step wherein we need to understand how to extract the various features from the data and learn more about the data that we are dealing with. It has got more to do with the type of domain that we are dealing with and familiarizing the system to learn more about it.
This step has got more to do with the feature that we are selecting from the set of features that we have. Sometimes it happens that there are a lot of features and we have to make an intelligent decision regarding the type of feature that we want to select to go ahead with our machine learning endeavor.
This is a vital step since the algorithms that we choose will have a very major impact on the entire process of machine learning. You can choose between the linear and nonlinear algorithm. Some of the algorithms used are Support Vector Machines, Decision Trees, Naïve Bayes, K-Means Clustering, etc.
This is the most important part of the machine learning technique and this is where it differs from traditional programming. The training is done based on the data that we have and providing more real-world experiences. With each consequent training step, the machine gets better and smarter and able to take improved decisions.
In this step, we actually evaluate the decisions taken by the machine in order to decide whether it is up to the mark or not. There are various metrics that are involved in this process and we have to closed deploy each of these to decide on the efficacy of the whole machine learning endeavor.
This process involves improving the performance of the machine learning process using various optimization techniques. Optimization of machine learning is one of the most vital components wherein the performance of the algorithm is vastly improved. The best part of optimization techniques is that machine learning is not just a consumer of optimization techniques but it also provides new ideas for optimization too.
Here various tests are carried out and some these are an unseen set of test cases. The data is partitioned into test and training set. There are various testing techniques like cross-validation in order to deal with multiple situations.
26. What is power analysis?
Answer: It is an experimental design technique for expecting the outcome of a given sample size.
27. What is meant by sampling? Explain some sampling methods that you know?
Answer: Data sampling is a statistical analysis method in which a particular portion of data is taken to analyze, identify the hidden trends and patterns in data. With the help of sampling method, a larger set of data being examined. It helps the data scientists to work with the limited portion of data to produce accurate results rather than working on entire data sets.
Types of sampling methods are:
- Simple random sampling method
- Stratified sampling
- Cluster sampling
- Multistage sampling
- Systematic sampling
28. What is a Random Forest?
Answer: Random forest is a versatile method in machine learning that performs both classification and regression tasks. It also helps in areas like treats missing values, dimensionality reduction, and outlier values. It is like gathering the various weak modules comes together to form a robust model.
29. What do you understand by the term Normal Distribution?
Answer: Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. The random variables are distributed in the form of the asymmetrical bell-shaped curve.
30. What does P-value signify about the statistical data?
Answer: 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.
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31. Explain about the box cox transformation in regression models?
Answer: For some reason or the other, the response variable for a regression analysis might not satisfy one or more assumptions of an ordinary least squares regression. The residuals could either curve as the prediction increases or follow the skewed distribution. In such scenarios, it is necessary to transform the response variable so that the data meets the required assumptions. A Box cox transformation is a statistical technique to transform non-morula dependent variables into a normal shape. If the given data is not normal then most of the statistical techniques assume normality. Applying a box cox transformation means that you can run a broader number of tests.
Write a function that takes in two sorted lists and outputs a sorted list that is their union.
The first solution which will come to your mind is to merge two lists and short them afterward
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32. What are Eigenvalue and Eigenvector?
Answer: Eigenvectors are for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvalues are the directions along which a particular linear transformation acts by flipping, compressing or stretching.
33. How do you overcome challenges to your findings?
Answer: The reason for asking this question is to discover how well the candidate approaches solving conflicts in a team environment. Their answer shows the candidate’s problem-solving and interpersonal skills in stressful situations. Understanding these skills is significant because group dynamics and business conditions change. Consider answers that:
Acknowledges recognizing and respecting different opinions
“I would acknowledge the validity of their findings. Then I would describe how I came to my conclusions using my data. I would also invite an open discussion of the results.”
34. Differentiate between univariate, bivariate and multivariate analysis?
Answer: Univariate analyses are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.
The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.
The multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.
35. Can you cite some examples where both false positive and false negatives are equally important?
Answer: In the Banking industry giving loans is the primary source of making money but at the same time if your repayment rate is not good you will not make any profit, rather you will risk huge losses.
Banks don’t want to lose good customers and at the same point in time, they don’t want to acquire bad customers. In this scenario, both the false positives and false negatives become very important to measure.
36. Describe the structure of Artificial Neural Networks?
Answer: Artificial Neural Networks works on the same principle as a biological Neural Network. It consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.
37. Please explain the goal of A/B Testing.
Answer: A/B Testing is a statistical hypothesis testing meant for a randomized experiment with two variables, A and B. The goal of A/B Testing is to maximize the likelihood of an outcome of some interest by identifying any changes to a webpage.
A highly reliable method for finding out the best online marketing and promotional strategies for a business, A/B Testing can be employed for testing everything, ranging from sales emails to search ads and website copy.
38. What do you mean by cluster sampling and systematic sampling?
Answer: When studying the target population spread throughout a wide area becomes difficult and applying simple random sampling becomes ineffective, the technique of cluster sampling is used. A cluster sample is a probability sample, in which each of the sampling units is a collection or cluster of elements.
Following the technique of systematic sampling, elements are chosen from an ordered sampling frame. The list is advanced in a circular fashion. This is done in such a way so that once the end of the list is reached, the same is progressed from the start, or top, again.
39. What is the common perception of visualization?
Answer: People think visualization as just charts and summary information. But they are beyond that and drive business with a lot of underlying principles. Learning design principles can help anyone build effective and efficient visualizations and this Tableau prep tool can drastically increase our time on focusing more important part. The only issue with Tableau is, it is paid and companies need to pay for leveraging that awesome tool.
40. Compare Sas, R, And Python Programming?
Answer: SAS: it is one of the most widely used analytics tools used by some of the biggest companies on earth. It has some of the best statistical functions, graphical user interface, but can come with a price tag and hence it cannot be readily adopted by smaller enterprises
R: The best part about R is that it is an Open Source tool and hence used generously by academia and the research community. It is a robust tool for statistical computation, graphical representation, and reporting. Due to its open source nature, it is always being updated with the latest features and then readily available to everybody.
Python: Python is a powerful open source programming language that is easy to learn, works well with most other tools and technologies. The best part about Python is that it has innumerable libraries and community created modules making it very robust. It has functions for statistical operation, model building and more.
41. How Is Data Modeling Different From Database Design?
Answer: Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying data modeling techniques.
Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database. Strictly speaking, database design includes the detailed logical model of a database but it can also include physical design choices and storage parameters.
42. What is collaborative filtering?
Answer: Filtering is a process used by recommender systems to find patterns and information from numerous data sources, several agents, and collaborating perspectives. In other words, the collaborative method is a process of making automatic predictions from human preferences or interests.
43. What is meant by Linear regression?
Answer: Linear regression is a technique used in statistics where one variable is presented on the Y-axis, and the other one is presented on X-axis. They both depend on each other. In linear Regression, Y is referred to as a creation variable and X as a predictor variable.
44. Explain the role of Activation function?
Answer: The activation function helps in introducing the nonlinearity into the neural network that enables the neural network to learn the complex functions. Without this, it is challenging for the linear function to analyze complex data. An activation function is a function is an artificial neuron which delivers the output based on the input given.
45. Do gradient descent methods always converge to the same point?
Answer: No, they do not because in some cases it reaches a local minimum or a local optima point. You don’t reach the global optima point. It depends on the data and starting conditions.
46. Why is data cleaning important for analysis?
Answer: This is a knowledge-based question with a relatively simple answer. So much of a data scientist’s time goes into cleaning data – and as the data gets bigger, so does the time it takes to clean. Cleaning it right is the foundation of analysis, and the time it takes to clean data, alone, makes it important.
47. What is selection bias and why does it matter?
Answer: Selection bias is a product of inadequately or improperly randomized data leading to data sets that are not representative of the whole. In an interview, you should express the importance of this in terms of its effect on your solution. If your data is not representative, your solutions likely are not either.
48. What is back Propagation?
Answer: Backpropagation is an algorithm used in Deep Learning to train the multilayer neural network. Using this method, we can move an error form an end of a network to the inside of it, and that brings the efficient computation of gradient.
It consists of the below-mentioned steps:
Forward data propagation of data that is being used for training
Derivatives are computed with the help of output and target.
Backpropagation for computing the derivative error.
You can use the output that was previously calculated for output.
Update the weights.
49. What is meant by Deep learning?
Answer: Deep learning is a function of artificial intelligence that inserts the capability to the machines to mimic the human brain tasks such as data processing and analyzing the data for taking valid decisions. Deep learning is a subfield of machine learning and is capable enough to learn from the data that is unsupervised or unstructured.
Even though the existence of Deep learning is there, but it has gotten popular in recent years due to the following two reasons.
Data is the primary source for the effective functioning of deep learning, and the generation of data from various sources has been increased massively over the years.
The development of the hardware resources to process the data models in an advanced manner.
50. Do we have different Selection Biases, if yes, what are they?
Answer: Sampling Bias: This bias arises when you select only particular people or when non-random selection of samples happened. In general terms, it is nothing but a selection of the majority of the people belong to one group.
Time Interval: sometimes a trial may be terminated earlier than actual time (probably due to some ethical reasons) but the extreme value finally taken into consideration is the most significant value even though all other variables have similar Mean.
Data: We can name it as a Data bias when a separate set of data is taken to support a conclusion or eliminates terrible data based on the dormitory grounds, instead of generally relying on generally stated criteria.
Attrition bias: Attrition bias is defined as an error that occurs due to Unequal loss of participants from a randomized controlled trial (RCT). There are some cases in which the participant’s losses due to various reasons is called an Attrition.