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Today's Interview QnAs

Company Name - Mindtree

Role: Data Scientist



Q. What is central tendency ?

A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.




Q. Which central tendency method is used If there exists any outliers ?

A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.



Q. Explain Central limit theorem ?

A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.



Q. What is Chi-Square test ?

A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.

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Today's Interview QnAs

Company Name - IBM

Role - Data Scientist



Q1. What is P-value?

Ans. The p-value is the probability that the null hypothesis is true. (1 – the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.


Q2. Forward and Backward selection? It's working?

Ans. Forward Selection chooses a subset of the predictor variables for the final model. Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time. forward selection starts with a null model (with no predictors) and proceeds to add variables one at a time, and so unlike backward selection, it DOES NOT have to consider the full model (which includes all the predictors).


Q3. Fix multicollinearity?

Ans. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove those features from our data.


Q4. Difference between Gradient boosting and random forest?

Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.

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Today's Interview QnAs

Company Name: Mindtree

Role: Data Scientist


1. Explain the difference between supervised and unsupervised machine learning?

Ans: Supervised learning algorithms are trained using labeled data. An unsupervised learning model finds the hidden patterns in data. In supervised learning, input data is provided to the model alongside the output. In unsupervised learning, only input data is provided to the model.


2. Explain the difference between KNN and k.means clustering?

Ans: KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.


3. What is the difference between classification and regression?

Ans: Classification is about predicting a label and regression is about predicting a quantity. That classification is the problem of predicting a discrete class label output for example. That regression is the problem of predicting a continuous quantity output for example.


4. How to ensure that your model is not overfitting?

Ans: To ensure that your model is not overfitting are:
• Keep the model simpler: remove some of the noise in the training data.
• Use cross-validation techniques such as k-folds cross-validation.
• Use regularization techniques such as LASSO.

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Today's Interview QnAs

Company Name: IBM

Role: Data Scientist


1. What relationships exist between a logistic regression’s coefficient and the Odds Ratio?

The coefficients and the odds ratios then represent the effect of each independent variable controlling for all of the other independent variables in the model and each coefficient can be tested for significance.

2. What’s the relationship between Principal Component Analysis (PCA) and Linear & Quadratic Discriminant Analysis (LDA & QDA)

LDA focuses on finding a feature subspace that maximizes the separability between the groups. While Principal component analysis is an unsupervised Dimensionality reduction technique, it ignores the class label. PCA focuses on capturing the direction of maximum variation in the data set.The PC1 the first principal component formed by PCA will account for maximum variation in the data.PC2 does the second-best job in capturing maximum variation and so on.

The LD1 the first new axes created by Linear Discriminant Analysis will account for capturing most variation between the groups or categories and then comes LD2 and so on.


3. What’s the difference between logistic and linear regression? How do you avoid local minima?


Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems.
Linear regression provides a continuous output but Logistic regression provides discreet output.
The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve.
The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.
We can try to prevent our loss function from getting stuck in a local minima by providing a momentum value. So, it provides a basic impulse to the loss function in a specific direction and helps the function avoid narrow or small local minima. Use stochastic gradient descent.

4. Explain the difference between type 1 and type 2 errors.

Type 1 error is a false positive error that ‘claims’ that an incident has occurred when, in fact, nothing has occurred. The best example of a false positive error is a false fire alarm – the alarm starts ringing when there’s no fire. Contrary to this, a Type 2 error is a false negative error that ‘claims’ nothing has occurred when something has definitely happened. It would be a Type 2 error to tell a pregnant lady that she isn’t carrying a baby.

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Today's Interview QnAs

Company: TheMathCompany

Role: Data Scientist


1. How to find the positions of numbers that are multiples of 4 from a series in python?
Ans.
For finding the multples of 4, we will use the argwhere() function. First, we will create a list of 10 numbers –
s1 = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) np.argwhere(s1 % 4==0)
Output > [3], [7]

2. What is the difference between recall and precision?
Ans.
While calculating the Precision of a model, we should consider both Positive as well as Negative samples that are classified.
While calculating the Recall of a model, we only need all positive samples while all negative samples will be neglected.
Hence Precision quantifies the number of positive class predictions that actually belong to the positive class. Recall quantifies the number of positive class predictions made out of all positive examples in the dataset.

3. How can you select k for k-means?
Ans.
There are two methods to calculate the optimal value of k in k-means are:
* Elbow method
*Silhouette score method
Silhouette score is the most prevalent while determining the optimal value of k.

4. What is a ROC Curve? Explain how a ROC Curve works?
Ans.
AUC – ROC curve is a performance measurement for the classification problem at various thresholds settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much model is capable of distinguishing between classes. Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s.

5. How will you get second highest salary of an employee emp from employee_table?
Ans.
SELECT TOP 1 salary FROM( SELECT TOP 2 salary FROM employee_table ORDER BY salary DESC) AS emp ORDER BY salary ASC;

6. What is the central limit theorem?
Ans.
The central limit theorem states that the distribution of an average will tend to be Normal as the sample size increases, regardless of the distribution from which the average is taken except when the moments of the parent distribution do not exist.

7. Difference between Normalisation and Standardization?
Ans.
Both Normalisation and Standardization are methods of Features Conversion. However, the methods are different in terms of the conversions. The data after Normalisation scales in the range of 0-1. While in case of Standardization the data is scaled such that it means comes out to be 0.

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Today's Interview QnAs

Company Name - IBM

Role: Data Scientist


1. How do you analyse the performance of the predictions generated by regression models versus classification models?

In the regression model, the most commonly known evaluation metrics include:
• R-squared (R2)
• Root Mean Squared Error (RMSE)
• Residual Standard Error (RSE)
• Mean Absolute Error (MAE)

Classification is the problem of identifying to which of a set of categories/classes a new observation belongs, based on the training set of data containing records whose class label is known. Following are the performance metrics used for evaluating a classification model:
• Accuracy
• Precision and Recall
• Specificity
• F1-score
• AUC-ROC

2. How do you assess logistic regression versus simple linear regression models?

• Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems.
• Linear regression provides a continuous output but Logistic regression provides discreet output. • The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve.
• The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.

3. What is cross-validation and why would you use it?

Cross-validation is a statistical method used to estimate the performance (or accuracy) of machine learning models. It is used to protect against overfitting in a predictive model, particularly in a case where the amount of data may be limited. In cross-validation, you make a fixed number of folds (or partitions) of the data, run the analysis on each fold, and then average the overall error estimate.

4. What’s the name of the matrix used to evaluate predictive models?

Confusion Matrix: A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. It can be used to evaluate the performance of a classification model through the calculation of performance metrics like accuracy, precision, recall, and F1-score.

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Today's Interview QnAs

Company: TCS

Role: Data Scientist

1. You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them?
Ans.
If the data set is large, we can just simply remove the rows with missing data values. It is the quickest way, we use the rest of the data to predict the values.
For smaller data sets, we can substitute missing values with values like mean, mode, forward or backward fill. There are different ways to do so, such as df.mean(), df.fillna(mean).

2.What do you understand about true positive rate and false-positive rate?
Ans.
The True Positive Rate (TPR) defines the probability that an actual positive will turn out to be positive.
The True Positive Rate (TPR) is calculated by taking the ratio of the [True Positives (TP)] and [True Positive (TP) & False Negatives (FN) ].
Formula: TPR=TP/TP+FN
The False Positive Rate (FPR) defines the probability that an actual negative result will be shown as a positive one i.e the probability that a model will generate a false alarm.
The False Positive Rate (FPR) is calculated by taking the ratio of the [False Positives (FP)] and [True Negatives (TN) & False Positives(FP)].
Formula: FPR=FP/TN+FP


3. What are the feature selection methods you use to select important variables?

Ans.
1. Filter Methods
There are various filter methods such as the Chi-Square test, Fisher’s Score method, Correlation Coefficient, Variance Threshold, Mean Absolute Difference (MAD) method, Dispersion Ratios, etc.
2. Wrapper Methods
There are three types of wrapper methods, they are:
Forward Selection: Here, one feature is tested at a time and new features are added until a good fit is obtained.
Backward Selection: Here, all the features are tested and the non-fitting ones are eliminated one by one to see while checking which works better.
Recursive Feature Elimination: The features are recursively checked and evaluated how well they perform.
3. Embedded Method
Examples of embedded methods: LASSO Regularization (L1), Random Forest Importance.

4. What is a lambda expression in Python?
Ans.
With the help of lambda expression, you can create an anonymous function. Unlike conventional functions, lambda functions occupy a single line of code. The basic syntax of a lambda function is –
lambda arguments: expression
Eg
x = lambda a : a * 4
print(x(5))
Output of 20.

5. What is root cause analysis?
Ans. Root cause analysis (RCA) is the process of discovering the root causes of problems in order to identify appropriate solutions. It assumes that it is much more effective to systematically prevent and solve for underlying issues rather than just treating ad hoc symptoms and putting out fires.

6. Write the code to find the length of a string without using the string functions.
Ans. string=raw_input("Enter string:")
count=0
for i in string:
count=count+1
print("Length of the string is:")
print(count)

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Today's Interview QnAs

Company: Adobe

Role: Data Scientist



1.What are the conditions for Overfitting and Underfitting?

Ans:
• In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.

• Overfitting – Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.

• Underfitting – High bias and Low Variance. Such a model doesn’t perform well on test data also. For example – Linear Regression is more prone to Underfitting.


2. Which models are more prone to Overfitting?

Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too – this typically happens when there are more features than the number of instances in the training data.


3. When does feature scaling should be done?

Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.


4.Which model is not affected by outliers?

Ans: Tree-based models are generally not affected by outliers, while regression-based models are.


5. What does the word ‘Naive’ mean in Naive Bayes?

Ans: Naive Bayes is a Data Science algorithm. It has the word ‘Bayes’ in it because it is based on the Bayes theorem, which deals with the probability of an event occurring given that another event has already occurred.

It has ‘naive’ in it because it makes the assumption that each variable in the dataset is independent of the other. This kind of assumption is unrealistic for real-world data. However, even with this assumption, it is very useful for solving a range of complicated problems, e.g., spam email classification, etc.

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Today's Interview QnAs

Company: Fractal Analytics

Role: Data Scientist


1. Why is dimension reduction important?
Ans
This is important mainly in the case when you want to reduce variance in your model (overfitting).

There are four advantages of dimensionality reduction :

*It reduces the time and storage space required

*Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model

*It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D

*It avoids the curse of dimensionality


2. What are the drawbacks of a linear model?
Ans.
There are a couple of drawbacks of a linear model:

A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model can’t be used for discrete or binary outcomes.
You can’t vary the model flexibility of a linear model.


3. Is mean imputation of missing data acceptable practice? Why or why not
Ans.
Mean imputation is the practice of replacing null values in a data set with the mean of the data.

Mean imputation is generally bad practice because it doesn’t take into account feature correlation. For example, imagine we have a table showing age and fitness score and imagine that an eighty-year-old has a missing fitness score. If we took the average fitness score from an age range of 15 to 80, then the eighty-year-old will appear to have a much higher fitness score that he actually should.

Second, mean imputation reduces the variance of the data and increases bias in our data. This leads to a less accurate model and a narrower confidence interval due to a smaller variance.


4. Give examples of data that does not have a Gaussian distribution, nor log-normal.

Ans.
Any type of categorical data won’t have a gaussian distribution or lognormal distribution.

Exponential distributions — eg. the amount of time that a car battery lasts or the amount of time until an earthquake occurs.


5. Q: The covid spread rate in India fell last year to 99 from 115 the year before. Is this reported change really noteworthy?

Ans.
Since this is a Poisson distribution question, mean = lambda = variance, which also means that standard deviation = square root of the mean

a 95% confidence interval implies a z score of 1.96

one standard deviation = sqrt(115) = 10.724

Therefore the confidence interval = 115+/- 21.45 = [93.55, 136.45]. Since 99 is within this confidence interval, we can assume that this change is not very noteworthy.

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