Become An Interview Ready Data Scientist from Scratch

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☑️25+Guided Assignments for Hands-on Practical Learning

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☑️We provide 1-1 Skype Course Support to resolve all the doubts between 3PM to 11:59PM, everyday including weekends

☑️You will get 300+ data science interview questions and answers PDF collection

☑️Course Completion Certificate

☑️Additionally we provide job resources for reference like how to apply for jobs, resume samples, how to choose good ML projects, etc.


👉Anybody from any background can learn from our course. No prior coding Experience required.

👉Anyone who wants to switch their career into data science can join our course.

👉It is a Self-Paced course with lifetime access

Trusted by 1900+ Learners, It is the best affordable Data Science course.

Original Price: Rs. 3499/-
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Company Name - Accenture
Role - Data Scientist

Today's Interview Quest N Ans


Q1. Detect outliers, what are quartiles?

Ans. The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR. Quartiles divide the entire set into four equal parts. So, there are three quartiles, first, second and third represented by Q 1 , Q 2 and Q 3 , respectively. Q 2 is nothing but the median.


Q2. Clustering, methods. Density based approach? Why?

Ans. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. It is basically a type of unsupervised technique.
Types of Clustering Methods:
1. Hierarchical based clustering
2. K-means clustering
3. Density based clustering: Density-based clustering methods provide a safety valve. Instead of assuming that every point is part of some cluster, we only look at points that are tightly packed and assume everything else is noise. This approach requires two parameters: a radius 𝜖 and a neighborhood density 𝛳. The idea of density-based is that we need to compare the density around an object with the density around its local neighbors.

It is a better approach because it does not require a-priori specification of number of clusters and is able to identify noise data while clustering.


Q3. Do classification for 2 classes if Classes has 95: 5 ratio of records.

Ans. This is called the case of imbalanced classification dataset. You can follow these techniques:
Use your own right evaluation metrics according to problem statement.
*Use K-fold Cross-Validation in the right way.
*Ensemble different resampled datasets.
*Resample with different ratios.
*Cluster the abundant class.
*Design your own models.


Q4. Model Pipeline? Benefits

Ans. Automating the machine learning workflow by enabling data to be transformed and correlated into a model that can then be analyzed to achieve outputs is done with ML pipelines. This type of ML pipeline makes the process of inputting data into the ML model fully automated. The main objective of having a proper pipeline for any ML model is to exercise control over it. A well-organised pipeline makes the implementation more flexible.


Q5. Multicollinearity? Deal?

Ans. Multicollinearity occurs when two or more independent variables(also known as predictor) are highly correlated with one another in a regression model. This means that an independent variable can be predicted from another independent variable in a regression model.

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

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#hiring #jobs

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Today Interview Quest

Company Name - Nokia
Role - Data Scientist

Q1. Difference between LSTM and Simple RNN?

Ans. The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN.


Q2. How do you know if you have enough data for your model?

Ans. Right kind of data is much more important than amount of data for your model, and should be prioritize first. If your model has diverse features, then surely you'll require more data as possible in order to understand it better. Having as much data possible is always preferable though one can always check with plotting learning curve in order to determine enough data.


Q3. P-value and confidence interval?

Ans. The P-value is known as the level of marginal significance within the hypothesis testing that represents the probability of occurrence of the given event. A confidence interval is a kind of interval calculation, obtained from the observed data that holds the actual value of the unknown parameter. It displays the probability that a parameter will fall between a pair of values around the mean.


Q4. ANOVA vs Chi-Square test

Ans. Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples. A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.



Q5. How model behaves when you have multicollinearity?

Ans. Multicollinearity causes the following two basic types of problems: The coefficient estimates can swing wildly based on which other independent variables are in the model. The coefficients become very sensitive to small changes in the model.



Q6. Name algorithms which does not need scaling?

Ans. CART, Random Forests, Gradient Boosted Decision Trees. Algorithms that do not require normalization/scaling are the ones that rely on rules. They would not be affected by any monotonic transformations of the variables. Scaling is a monotonic transformation

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Key Highlights of this Course:

🤩We provide Assignment based Data Science course for complete hands-on practical learning experience. You will get 25+ Guided Assignments

🥳You will get 300+ data science interview questions and answers PDF collection

😎We have 10+ capstone end-to-end projects in our course that will enhance learners' resume

😇We provide 1-1 Skype Course Support to resolve all the doubts

😬Anybody from any background can learn from our course. No prior coding Experience required.

🤓Anyone who wanna switch their career into data science can join this amazing course

🤠It is a Self-Paced course with lifetime access

😍You will get course completion certificate

🤗Additionally we provide job resources for reference like how to apply for jobs, resume samples, how to choose good ML projects, etc.
____________________

💯Trusted by 1900+ Learners with more than 550 success stories, It is the best affordable Data Science course available.

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Interview QnAs

Company: Tiger Analytics

Role: Data Scientist

Q7. What is a sensitivity analysis in the decision making process?

Ans: Sensitivity analysis is a method for predicting the outcome of a decision if a situation turns out to be different compared to the key predictions. It helps in assessing the riskiness of a strategy. Helps in identifying how dependent the output is on a particular input value.

Q1. Filter and wrapper feature selection methods?

Ans: Wrapper methods measure the “usefulness” of features based on the classifier performance. In contrast, the filter methods pick up the intrinsic properties of the features (i.e., the “relevance” of the features) measured via univariate statistics instead of cross-validation performance. So, wrapper methods are essentially solving the “real” problem (optimizing the classifier performance), but they are also computationally more expensive compared to filter methods due to the repeated learning steps and cross-validation.

Filter methods: information gain, chi-square test, fisher score, correlation coefficient, variance threshold

Wrapper methods: recursive feature elimination, sequential feature selection algorithms, genetic algorithms

Q5. How to decide imputation method if there are null values in a dataset?

Ans: Mean/Median Imputation:- In a mean or median substitution, the mean or a median value of a variable is used in place of the missing data value for that same variable. Median over mean when the data column has any outliers.
Mode substitution:- In mode substitution, the highest occurring value for categorical value is used in place of the missing data value of the same variable.
Deleting Column: If the number of null values are more than 70%, then prefer deleting the column as it doesn't contribute to the model. If it does, try other methods such as regression or k-means imputation.

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Today's Interview QnAs

Company Name - Genpact

Role - Data Scientist
___________________

Q1. Inter quartile ranges?

Ans. The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR. Quartiles divide the entire set into four equal parts. So, there are three quartiles, first, second and third represented by Q 1 , Q 2 and Q 3 , respectively. Q 2 is nothing but the median.

Q2. Imputation methods?

Ans. They are:
*List wise or case deletion
*Pairwise deletion
*Mean substitution
*Regression imputation
*Maximum likelihood.


Q3. Deal overfittting?

Ans. Techniques to reduce overfitting:
*Increase training data.
*Reduce model complexity.
*Early stopping during the training phase.
*Ridge Regularization and Lasso Regularization.


Q3. Gridsearch vs Random search?

Ans. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.

Q4. Hyperparameters in SVM?

Ans. kernel: It maps the observations into some feature space. Ideally the observations are more easily (linearly) separable after this transformation. There are multiple standard kernels for this transformations, e.g. the linear kernel, the polynomial kernel and the radial kernel.

*C: It is a hypermeter in SVM to control error. The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly.

*gamma: Gamma is used when we use the Gaussian RBF kernel. if you use linear or polynomial kernel then you do not need gamma only you need C hypermeter.


Q5. Ridge vs lasso?

Ans. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
______________________

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#Quiz

Study the following program:

class Teacher:
def init(name, id_no, age):
name.id_no = id_no
name.age = age
teac = Teacher(5, 25)

Quiz Explanation

Def init proves the first option is correct which is called constructor of the class.

The parameters passed to it are idno and age which proves 2nd option is correct.

Self passed as first is standard way of defining any method in class.

To get access to it's parameters and methods we defined a variable teac which proves 3rd option is also correct.

Today's Interview QnAs

Company Name - Course5i

Role - Data Scientist
___________________

Q1. How to improve model performance?

Ans. Follow these techniques:
1. Use Validation methods
2. Add more data
3. Apply feature engineering techniques(Normalization, Imputation etc)
4. Compare Multiple algorithms
5. Hyperparameter Tuning


Q2. Standardization vs log transformation?

Ans. Standardization is the process of putting different variables on the same scale. This process allows you to compare scores between different types of variables. Typically, to standardize variables, you calculate the mean and standard deviation for a variable. Log transformation is a data transformation method in which it replaces each variable x with a log(x). Log-transform decreases skew in some distributions, especially with large outliers. But, it may not be useful as well if the original distributed is not skewed. Also, log transform may not be applied to some cases (negative values), but standardization is always applicable (except σ=0).

Q3. Object detection?

Ans. Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results.


Q4. Data cleaning steps?

Ans. Step 1: Remove duplicate or irrelevant observations. Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations.
*Step 2: Fix structural errors.
*Step 3: Filter unwanted outliers.
*Step 4: Handle missing data.
*Step 5: Validate your data if it's appropriate according to problem statement


Q5. Upsampling and downsampling methods

Ans. In a classification task, there is a high chance for the algorithm to be biased if the dataset is imbalanced. An imbalanced dataset is one in which the number of samples in one class is very higher or lesser than the number of samples in the other class.

To counter such imbalanced datasets, we use a technique called up-sampling and down-sampling.

In up-sampling, we randomly duplicate the observations from the minority class in order to reinforce its signal. The most common way is to resample with replacement.

In down-sampling, we randomly remove the observations from the majority class. Thus after up-sampling or down-sampling, the dataset becomes balanced with same number of observations in each class.


Q6. hypothesis testing?

Ans. Hypothesis testing is defined as the process of choosing hypotheses for a particular probability distribution, based on observed data. We use this to test whether a hypothesis can be accepted or not.

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