1. How would you check if the model is suffering from multi Collinearity?
2. What is transfer learning? Steps you would take to perform transfer learning.
3. Why is CNN architecture suitable for image classification? Not an RNN?
4. What are the approaches for solving class imbalance problem?
5. When sampling what types of biases can be inflected? How to control the biases?
6. Explain concepts of epoch, batch, iteration in machine learning.
7. What type of performance metrics would you choose to evaluate the different classification models and why?
8. What are some of the types of activation functions and specifically when to use them?
9. What are the conditions that should be satisfied for a time series to be stationary?
10. What is the difference between Batch and Stochastic Gradient Descent?
11. What is difference between K-NN and K-Means clustering?

Date: 04/07/21
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1. What happens when neural nets are too small? What happens when they are large enough?
2. Why do we need pooling layer in CNN? Common pooling methods?
3. Are ensemble models better than individual models? Why/why – not?
4. Use Case – Consider you are working for pen manufacturing company. How would you help sales team with leads using Data analysis?
5. Assume you were given access to a website google analytics data.
6. In order to increase conversions, how do you perform A/B testing to identify best page design.
7. How is random forest different from Gradient boosting algorithm, given both are tree-based algorithm?
8. Describe steps involved in creating a neural network?
9. In brief, how would you perform the task of sentiment analysis?

Date: 03/07/21
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1. Why do we select validation data other than test data?
2. Difference between linear logistic regression?
3. Why do we take such a complex cost function for logistic?
4. Difference between random forest and decision tree?
5. How would you decide when to stop splitting the tree?
6. Measures of central tendency
7. What is the requirement of k means algorithm
8. Which clustering technique uses combining of clusters
9. Which is the oldest probability distribution
10. What all values does a random variable can take
11. Types of random variables
12. Normality of residuals

Date: 04/07/21
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1. Telecom Customer Churn Prediction. Explain the project end to end?
2. Data Pre-Processing Steps used.
3. Sales forecasting how is it done using Statistical vs DL models – Efficiency.
4. Logistic Regression – How much percent of Customer has churned and how much have not churned?
5. What are the Evaluation Metric parameters for testing Logistic Regression?
6. What packages in Python can be used for ML? Why do we prefer one over another?
7. Numpy vs Pandas basic difference.
8. Feature on which this Imputation was done, and which method did we use there?
9. Tuple vs Dictionary. Where do we use them?
10. What is NER – Named Entity Recognition?

Date: 02/07/21
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1. SQL question on inner join and cross join
2. SQL question on group-by
3. Case study question on customer optimization of records for different marketing promotional offers
4. Tuple and list
5. Linear regression
6. Logistic regression steps and process
7. Tell me about your passion for data science? Or What brought you to this field?
8. What is the most common problems you face whilst working on data science projects?
9. Describe the steps to take to forecast quarterly sales trends. What specific models are most appropriate in this case?
10. What is the difference between gradient and slope, differentiation and integration?
11. When to use deep learning instead of machine learning. Advantages, Disadvantages of using deep learning?
12. What are vanishing and exploding gradients in neural networks?

Date: 02/07/21
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1. Central limit theorem
2. Hypotheses testing
3. P value
4. T-test
5. Assumptions of linear regression.
6. Correlation and covariance.
7. How to identify & treat outliers and missing values.
8. Explain Box and whisker plot.
9. Explain any unsupervised learning algorithm.
10. Explain Random forest.
12. Business and technical questions related to your project.
13. Explain any scope of improvement in your project.
14. Questions based on case studies.
16. Write SQL query to find employee with highest salary in each department.
17. Write SQL query to find unique email domain name & their respective count
18. Solve question (17) using Python.

Rounds:
1. Technical Test (Python, SQL, Statistics) (Coding+MCQ) (90 min).
2. Telephonic interview (10 min).
3. Technical interview (45 min).
4. Fitment interview (25 min).
5. HR interview (30 min).

Date: 03/07/21
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1) Measures of central tendency
2) What is the requirement of k means algorithm
3) Which clustering technique uses combining of clusters
4) Which is the oldest probability distribution
5) What all values does a random variable can take
6) Types of random variables
7) Normality of residuals
8) Probability questions
9) Sensitivity and specificity etc.
10) Explain bias – variance trade off. How does this affect the model?
11) What is multi collinearity? How to identify and remove it.

Date: 30/06/21
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1. Difference between WHERE and HAVING in SQL
2. Basics of Logistics Regression
3. How do you treat outliers ?
4. Explain confusion matrix ?
5. Explain PCA (Wanted me to explain the co-variance matrix and eigen vectors and values and the mathematical expression and mathematical derivation for co-variance matrix)
6. How do you cut a cake into 8 equal parts using only 3 straight cuts ?
7. Explain kmeans clustering
8. How is KNN different from k-means clustering?
9. What would be your strategy to handle a situation indicating an imbalanced dataset?
10. Stock market prediction: You would like to predict whether or not a certain company will declare bankruptcy within the next 7 days (by training on data of similar companies that had previously been at risk of bankruptcy). Would you treat this as a classification or a regression problem?

Date: 30/06/21
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1. What is a Python Package, and Have you created your own Python Package?
2. Explain about Time series models you have used?
3. SQL Questions – Group by Top 2 Salaries for Employees – use Row num and Partition
4. Pandas find Numeric and Categorical Columns. For Numeric columns in Data frame, find the mean of the entire column and add that mean value to each row of those numeric columns.
5. What is Gradient Descent? What is Learning Rate and Why we need to reduce or increase? Why Global minimum is reached and Why it doesn’t improve when increasing the LR after that point?
6. Two Logistic Regression Models – Which one will you choose – One is trained on 70% and other on 80% data. Accuracy is almost same.
8. What is Log-Loss and ROC-AUC?
9.  Do you know to use Amazon SageMaker for MLOPS?
10.  Explain your Projects end to end (15-20mins).

Date: 01/07/21
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1. What makes you feel that you would be suitable for this role, since you come from a different background?
2. What is an imbalanced data set??
3. What are the factors you will consider in order to predict the population of a city in the future?
4. Basic statistics questions?
5. What are the approaches for treating the missing values?
6. Evaluation metrics for Classification?
7. Bagging vs Boosting with examples
8. Handling of imbalanced datasets
9. What are your career aspirations?
10.What’s the graph of y = |x|-2
11. Estimate on no. Of petrol cars in Delhi
12.Case study on opening a retail store
13.Order of execution of SQL

Date: 28/06/21
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1. What are the projects done by you.
2. Suppose there is a client who wants to know if giving discounts is beneficial or not. How would you approach this problem?
3. The same client want to know how much discount he should give in the next month for maximum profits.
4. Can you have a modeling approach to say in last year what mistakes client did in giving discounts. Meaning if they should have have a different discount and increased sales.
5. What feature engineering techniques you used in past projects.
6. What models you used and selected the final model.

Date: 29/06/21
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1) curse of dimensionality? How would you handle it?
2) How to find the multi collinearity in the data set
3)Explain the difference ways to treat multi collinearity!
4) How you decide which feature to keep and which feature to eliminate after performing multi collinearity test?
5)Explain logistic regression
6)we have sigmoid function which gives us the probability between 0-1 then what is the need of logloss in logistic regression?
7) P value and its significance in statistical testing?
8) How do you split the time series data and evaluation metrics for time series data
9) How did you deploy your model in production? How often do you retrain it?

Date: 29/06/21
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1.     SQL question on inner join and cross join
2.     SQL question on group-by
3.     Case study question on customer optimization of records for different marketing promotional offers
4.     Tuple and list
5.     Linear regression
6.     Logistic regression steps and process
7.     Tell me about your passion for data science? Or What brought you to this field?
8.     What is the most common problems you face whilst working on data science projects?
9.      Describe the steps to take to forecast quarterly sales trends. What specific models are most appropriate in this case?
10.   What is the difference between gradient and slope, differentiation and integration?
11.   When to use deep learning instead of machine learning. Advantages, Disadvantages of using deep learning?
12.   What are vanishing and exploding gradients in neural networks?
13.   What happens when neural nets are too small? What happens when they are large enough?
14.   Why do we need pooling layer in CNN? Common pooling methods?
15.   Are ensemble models better than individual models? Why/why – not?

Date: 21/06/21
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1. How would you check if the model is suffering from multi Collinearity?
2. What is transfer learning? Steps you would take to perform transfer learning.
3. Why is CNN architecture suitable for image classification? Not an RNN?
4. What are the approaches for solving class imbalance problem?
5. When sampling what types of biases can be inflected? How to control the biases?
6. Explain concepts of epoch, batch, iteration in machine learning.
7. What type of performance metrics would you choose to evaluate the different classification models and why?
8. What are some of the types of activation functions and specifically when to use them?
9. What is the difference between Batch and Stochastic Gradient Descent?
10. What is difference between K-NN and K-Means clustering?
11. How to handle missing data? What imputation techniques can be used?

Date: 27/06/21
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1. What is difference between K-NN and K-Means clustering?
2. How to handle missing data? What imputation techniques can be used?
3. Explain topic modelling in NLP and various methods in performing topic modeling.
4. Explain how you would find and tackle an outlier in the dataset.
5. Follow up: What about inlier?
6. Explain back propagation in few words and its variants?
7. Is interpretability important for machine learning model? If so, ways to achieve interpretability for a machine learning models?
8. Is interpretability important for machine learning model? If so, ways to achieve interpretability for a machine learning models?
9. How would you design a data science pipeline?
10. Explain bias – variance trade off. How does this affect the model?
11. What does a statistical test do?
12. How to determine if a coin is biased? Hint: Hypothesis testing

Date: 28/06/21
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1. Explain about Time series models you have used?
2. SQL Questions – Group by Top 2 Salaries for Employees – use Row num and Partition
3. Pandas find Numeric and Categorical Columns. For Numeric columns in Data frame, find the mean of the entire column and add that mean value to each row of those numeric columns.
4. What is Gradient Descent? What is Learning Rate and Why we need to reduce or increase? Why Global minimum is reached and Why it doesn’t improve when increasing the LR after that point?
5. Two Logistic Regression Models – Which one will you choose – One is trained on 70% and other on 80% data. Accuracy is almost same.
6. What is Log-Loss and ROC-AUC?
7. What is Multi-collinearity? How will you choose one features if there are 2 highly correlated features? Give Examples with the techniques used.
8. VIF – Variance Inflation Factor – Explain.
9. Do you know to use Amazon SageMaker for MLOPS?
10. Explain your Projects end to end (15-20mins).

Date: 23/06/21
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1)What is the difference between Cluster and Systematic Sampling?
2)    Differentiate between a multi-label classification problem and a multi-class classification problem.
3)    How can you iterate over a list and also retrieve element indices at the same time?
4)    What is Regularization and what kind of problems does regularization solve?
5)    If the training loss of your model is high and almost equal to the validation loss, what does it mean? What should you do?
6)    Explain evaluation protocols for testing your models? Compare hold-out vs k-fold cross validation vs iterated k-fold cross-validation methods of testing.
7)    Can you cite some examples where a false positive is important than a false negative?
8)    What is the advantage of performing dimensionality reduction before fitting an SVM?
9)    How will you find the correlation between a categorical variable and a continuous variable ?
10)   How will you calculate the accuracy of a model using a confusion matrix?
11)   You are given a dataset with 1500 observations and 15 features. How many observations you will select in each decision tree in a random forest?
12)   Given that you let the models run long enough, will all gradient descent algorithms lead to the same model when working with Logistic or Linear regression problems?
13)    What do you understand by statistical power of sensitivity and how do you calculate it?
14)    What is pruning, entropy and information gain in decision tree algorithm?
15)    What are the types of biases that can occur during sampling?

Date: 23/06/21
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1. Use Case – Consider you are working for pen manufacturing company. How would you help sales team with leads using Data analysis?
2. Interviewers ask about scenarios or use-case based questions to know interviewee thought process and problem-solving skills.
3. Assume you were given access to a website google analytics data.
4. In order to increase conversions, how do you perform A/B testing to identify best page design.
5. How is random forest different from Gradient boosting algorithm, given both are tree-based algorithm?
6. Describe steps involved in creating a neural network?
7. LSTM solves the vanishing gradient problem, that RNN primarily have. How?
8. In brief, how would you perform the task of sentiment analysis?

Date: 27/06/21
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ROUND 1 :

Introduction

– Started with Classification particularly Imbalance , oversampling.
Which class should i oversample etc.
Telecom Churn Case Study Questions like Evaluation metric for imbalance data
what threshold to choose to diving the classes (0.5 in case of balanced else sensitivity / Specifivity etc.
What if i don’t use SMOTE() for handling imbalance how should i select the threshold now (messed up by me, roc , auc etc) Ans = Presion – Recall Curve

– NLP Questions
Sentiment analysis, preprocessing like (TFID, BOW), Embeddings, stemming, Lemmatization
libraries in know : nltk, spacy

– Regression Preprocessing
answered outlier, missing value imputation, Distribution, dummies, multicolinearity etc
You have two highy co-related columns which one will you drop? : “Based on Business Problem i will see accordingly.”, Answered

– Naive Bayes Explanation , Drawback of Naive Bayes(couln’t answer drawback of Naive Bayes, ‘Assume all are independent’, him)
– Hand Gesture Recognition Techniques (End to End)
– Resource Timesheet Forecasting . (What is it?? what you do on this?, ” Explained with a story based on what i do in TCS”.

– Do you know any Boosting Algorithms : YES
where have you used?? in Telecom Churn and Healthcare Analytics by AV

– Gradient Descent (How it works)

– KNN related. How do we choose value of K ??

– Satirical Computing:
Type 1 and Type 2 error
Alternate name of Type 1 error (couldn’t answer alternate name of Type 1 error, ‘False +ive, him)
What is p-Value (Explained with the example of Linear Regression from statsmodel)

– Do you have exposure of TimeSeires analysis : NO (didn’t ask anything and seems fine with him)

– Exp in DS/Analytics in TCS etc.

Initial they had asked for the explaining the project which I had done. I explained the Customer prediction case . Then I was asked with python questions by sharing my screen.

1. How do you handle the correlated variables without removing them
2. Explain the SMOTE, ADAYSN technique
3. What is stratified sampling technique
4. Explain the working of random forest and xgboost
5. How do you optimise the Recall of your output
6. What are chisquare and ANOVA test
7. In python they asked for LOC,ILOC, how do you remove duplicate,How to unique values in column,
8. In SQL they asked for the query for having matches between different teams

Date:25/05/21
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    Company Name : Tata IQ
         Role: Data Analyst

• Why data science as a career?

         Stats:

• What is p value?
• What is histograms?
• What is confidence interval?
• You are a Sr data analyst at a new Online Cab booking Startups
• How you will do data collection and how you will leverage the data to give useful insights to the Company?

         Guestimate:

• No Of cabs booking per day in Ranchi
• You are product head manager(not remember exactly) at a NBFC which gives a Secured loans what factors will you consider giving loan to ?
• Inventory Database based on that have to do basic pandas/sql query? Joins / merge to get avg sales, its chart?
• You have a list of 3 numbers return the min diff. Can use any python/sql
• What is Big Data?

1.Difference between array and list
2.Map function
3. Scenario,
if coupon distributed randomly to customers of swiggy, how to check there buying behaviour.
Use segmenting customers
Compare customers who got coupon and who did not
4. Which is faster dictionary or list for look up
5. How to merge two arrays
6. How much time svm takes to complete if 1 iteration takes 10sec for 1st class.
And there are 4 classes.
7. Kernals in svm, there difference

           Round 2:

• Complete ML technical stack used in project?
• Different activation function?
• How do you handle imbalance data ?
• Difference between sigmoid and softmax ?
• Explain about optimisers ?
• Precision-Recall Trade off ?
• How do you handle False Positives ?
• Explain LSTM architecture by taking example of 2 sentences and how it will be processed?
• Decision Tree Parameters?
• Bagging and boosting ?
• Explain bagging internals
• Write a program by taking an url and give a rough code approach how you will pass payload and make a post request?
• Different modules used in python ?
• Another coding problem of checking balanced parentheses?

   

• Date:31/05/21
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1.RNN, NN and CNN difference.
2. Supervised, unsupervised and reinforcement learning with there algo example.
3. Difference between ai, ml and dl
4. How u do dimentionality reduction.
5. What is Multicollinearity
6. Parameters of random forest
7 . Parametera of deep learning algos
8. Different feature selection methods
9. Confusion matrix

Date: 18/05/21
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How to select alternate rows in SQL ?
How to modify Sql row value ?
How to rename multiple column names in pandas?
How to add a new column name with all lower case letters in pandas?
Difference between Where and Having ?
Difference between Union and Union all?
How many Joins in SQL ?
How to drop column name in to read json file ?
List compression ?
Mutable and immutable ?
List vs tuple ?
How to handle missing values in a dataset?
How to handle imbalance in a dataset ?
What is hyperparameter tuning ?
Naïve Bayes Algorithm explained?
Overfitting and underfitting ?
Bias variance trade off?

Date: 19/05/21
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• Previous job role and responsibilities
• Problem statement of your project and How do you overcome challenges
• How do you handle feature which had many categories.
• When to use precision and recall.
• What are outliers & how do you handle them
• Joins, self joins, said me to write sql queries on self joins
• How good your with python
• Logic for reverse string.
• Data collection- how do you collect data and data preprocessing.
• Focused on EDA part
• Have you deveployed any project in cloud if you which cloud you had used and how do you do that.
• How do you interact with domain expertise and business analytics people.
• How do you replace Missing values for continuous variables.
• What is your largest data set you have handled till now and tell me size of dataset.
• Overall Mostly focused on SQL, DATA COLLECTION, EDA, feature engineering and selection.
  
  Date: 17/05/21

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Python :
1. Is python a language that follows pass by value, or pass by reference or pass by object reference
2. What are lambda functions and how to use them
3. Difference between mutable and immutable objects with example.
4. What are Python decorators? Why do we use them

SQL :
1. What is the difference between Inner join and left inner join ?
2. What are window functions ?
3. What is the use of groupby ?

SQL Round
3 tables given as below:

TRIPS
trip_id
vehicle_id
start_time
stop_time

VEHICLE_MAKE
vehicle_id
make_id

MAKES
make_id
make_name

There is a table which contains vehicle trips. Trips are not necessarily in order.

There is a table which contains vehicle makes. Makes are not necessarily known.

PROBLEM: Write SQL code that provides the number of trips that started on September 1st, 2020 for each vehicle with a KNOWN make.
Order the results by the trip count.

op
vehicle_id | trip_count
4 |     2
1 |     1
2 |     1

Date: 20/05/21
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Company Name : Tata IQ
         Role: Data Analyst

• Why data science as a career?

         Stats:

• What is p value?
• What is histograms?
• What is confidence interval?
• You are a Sr data analyst at a new Online Cab booking Startups
• How you will do data collection and how you will leverage the data to give useful insights to the Company?

         Guestimate:

• No Of cabs booking per day in Ranchi
• You are product head manager(not remember exactly) at a NBFC which gives a Secured loans what factors will you consider giving loan to ?
• Inventory Database based on that have to do basic pandas/sql query? Joins / merge to get avg sales, its chart?
• You have a list of 3 numbers return the min diff. Can use any python/sql
• What is Big Data?

Company: Cerence
Role: NLU Developer

Question1 :
Write a function that take two strings as inputs and return true if they are anagrams of each other and false otherwise
e.g.
(hello, hlleo) –> true
(hello, helo) –> false

Question 2 :
Write a function that take an array of strings “A” and an integer “n”,
that return the list of all strings of length “n” from the array “A” that can be constructed
as the concatenation of two strings from the same array “A”
e.g.
A = [dog, tail, sky, or, hotdog, tailor, hot] and n=6
output should be “hotdog” and “tailor”

Question 3 :
Given an array “arr” of numbers and a starting number “x”,
Find “x” such that the running sums of “x” and the elements of the array “arr” are never lower than 1.
e.g.
arr = [-2, 3, 1, -5].
The running sums will be x-2, x-2+3, x-2+3+1 and x-2+3+1-5.
So, the output should be 4.

Date:20/05/21
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A continuous variable is having missing values, so how will you decide that the missing values should be imputed by mean or median?
What is PCA and what each component means? Also, what is the maximum value for number of components?
What is test of independence? How do you calculate Chi-square value?
When precision is preferred over recall or vice-versa?
Advantages and disadvantages of Random forest over Decision Tree?
What is the c hyperparameter in SVM algorithm and how it affects bias variance tradeoff?
What are the assumptions of linear regression?
Difference between Stemming and Lemmatization?
Difference between Correlation and Regression?
What is p-value and confidence interval?
What is multicollinearity and how do you deal with multicollinearity? What is VIF?
What is the difference between apply, apply map and map function in python?

Date:21/05/21
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My 16th Data Interview experience

Role: Data Analyst

Introduce yourself.

One complex sql query- 2 table are there, Table1(cust_id,Name) Table2(cust_id,Transaction_amt)
Write a query to return the name of customers with 8th highest lifetime purchase.

Achieve the same using python.

ML questions:

What’s the problem in having multi collinearity in data set.
If there is business requirement to keep two corelated features in model, what would you do.
How would you deal with feature of 4 categories and 20% null values.

Some questions based on my project.

Date:22/05/21
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I was asked to explain my project.
Have you not done any classification problem as your Resume only mentions regression tasks.

Explain the working of Gradient boosting.
Difference between boosting and bagging.

What would you do when output is imbalanced.
What is more preferred over sampling or under sampling.
In what case under sampling a non harmful approach.

How would you measure the performance of models built on imbalanced dataset.
What’s the meaning of precision and recall.

Tell me about a task you did and are very proud of.
Do you have any questions for me

Post interview I was given a ML assignment to solve and submit within next 2 weeks.

Date:26/05/21
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1) Explain the architecture of CNN
2)If we put a 3×3 filter over 6×6 image what will be the size of the output image
3) What will you do to reduce overfitting In deep learning models
4) Can you write a program for inverted star program in python
5)Write a program to create a data frame and remove elements from it
6) I have 2 guns with 6 holes in each, and I load a single bullet In each gun, what is the probability that if I fire the guns simultaneously at least 1 gun will fire (at least means one or more than one)
7) There are 2 groups g1 and g2, g1 will ask g2 members to give them 1 member so that they both will be equal in number, g2 will ask g1 members to give them 1 member so that they will be double of g1, how many members are there in the groups (I’m not sure of this question as I tried to solve but didn’t get correct answer)

Date:26/05/21
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Round 2:
Complete ML technical stack used in project?
Different activation function?
How do you handle imbalance data ?
Difference between sigmoid and SoftMax ?
Explain about optimizers ?
Precision-Recall Trade off ?
How do you handle False Positives ?
Explain LSTM architecture by taking example of 2 sentences and how it will be processed?
Decision Tree Parameters?
Bagging and boosting ?
Explain bagging internals
Write a program by taking an url and give a rough code approach how you will pass payload and make a post request?
Different modules used in python ?
Another coding problem of checking balanced parentheses?

Date:23/05/21
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How to reverse a linked list

Give a logistic regression model in production, how would you find out the coefficients of different input features.

What is the p- value in OLS regression

What’s the reason for high bias or variance

Which models are generally high biased or high variance

Write code to find the 8 highest value in the Data Frame

What’s difference between array and list

What’s the difference between Gradient boosting and Xgboost

Is XOR data linearly separable

How do we classify XOR data using logistic regression

Some questions from my previous projects

Given a sand timer of 4 and 7 mins how would you calculate 10 mins duration.

What’s the angle between hour and minute hand in clock as 3:15

Date:24/05/21
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1. What is central tendency
2. Which central tendency method is used If there exists any outliers
3. Central limit theorem
4. Chi-Square test
5. A/B testing
6. Difference between Z and t distribution (Linked to A/B testing)
7. Outlier treatment method
8. ANOVA test
9. Cross validation
10. How will you work in a machine learning project if there is a huge imbalance in the data.
11. Formula of sigmoid function
12. Can we use sigmoid function in case of multiple classification (I said no)
13. Then which function is used
14. What is Area under the curve
15. Which metric is used to split a node in Decision Tree
16. What is ensemble learning
17. 3 situation based questions

Date: 27/05/21
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1.     What are the ML techniques you’ve used in projects?
2.     Very first question was PCA? Why use PCA?
3.     Types of Clustering techniques (Not algorithms)? Which Clustering techniques will you use in which Scenario – example with a Program?
4.     OCR – What type of OCR did you use in your project – Graphical or Non – Graphical?
5.     OCR – What is a Noise? What types of noise will you face when performing OCR? Handwritten can give more than 70% accuracy when I wrote in 2012 but you’re saying 40%.
6.     Logistic Regression vs Linear Regression with a real-life example – explain?
7.     Is Decision tree Binary or multiple why use them?
8.     Do you know Map Reduce and ETL concepts?
9.     What is a Dictionary or Corpus in NLP and how do you build it?
10.  How do you basically build a Dictionary, Semantic Engine, Processing Engine in a NLP project, where does all the Synonyms (Thesaurus words go).
11.  What are the Types of Forecasting? What are the ML and DL models for forecasting (He said Fast-forwarding models as example) other than Statistical (ARIMA) models you’ve used in your projects?
12.  What is a Neural Network? Types of Neural Networks you know?
13.  Write a Decision Tree model with a Python Program.
14.  How do you build an AZURE ML model? What are all the Azure products you’ve used? I said Azure ML Studio.
15.  Cibil score is an example for Fuzzy model and not a Classification model.
16.  What is an outlier give a real life example? how do you find them and eliminate them? I gave an example of calculating Average salary of an IT employee.

Date: 19/06/21
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1.     G values, P values, T values
2.     Conditional Probability
3.     Central Values of Tendency
4.     Can Linear Regression be used for Classification? If Yes, why if No why?
5.     Hypothesis Testing. Null and Alternate hypothesis
6.     Derivation of Formula for Linear and logistic Regression
7.     Where to start a Decision Tree. Why use Decision Trees?
8.     PCA Advantages and Disadvantages?
9.     Why Bayes theorem? DB Bayes and Naïve Bayes Theorem?
10.  Central Limit Theorem?
11.  R packages in and out? For us it’s Python Packages in and out.
12.  Scenario based question on when to use which ML model?
13.  Over Sampling and Under Sampling
14.  Over Fitting and Under Fitting
15.  Core Concepts behind Each ML model.
16.  Genie Index Vs Entropy
17.  how to deal with imbalance data in classification modelling? SMOTHE techniques

Date: 18/06/21
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How many cars are there in Chennai? How do u structurally approach coming up with that number?
2.     Multiple Linear Regression?
3.     OLS vs MLE?
4.     R2 vs Adjusted R2? During Model Development which one do we consider?
5.     Lift chart, drift chart
6.     Sigmoid Function in Logistic regression
7.     ROC what is it? AUC and Differentiation?
8.     Linear Regression from Multiple Linear Regression
9.     P-Value what is it and its significance? What does P in P-Value stand for? What is Hypothesis Testing? Null hypothesis vs Alternate Hypothesis?
10.  Bias Variance Trade off?
11.  Over fitting vs Underfitting in Machine learning?
12.  Estimation of Multiple Linear Regression
13.  Forecasting vs Prediction difference? Regression vs Time Series?
14.  p,d,q values in ARIMA models
1.     What will happen if d=0
2.     What is the meaning of p,d,q values?
15.  Is your data for Forecasting Uni or multi-dimensional?
16.  How to find the nose to start with in a Decision tree.
17.  TYPES of Decision trees – CART vs C4.5 vs ID3
18.  Genie index vs entropy
19.  Linear vs Logistic Regression
20.  Decision Trees vs Random Forests

Date: 17/06/21
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1. Explain your Projects
2. Assumptions in Multiple linear regression
3. Decision tree algorithm
4. Gini index
5. Entropy
6. Formulas of gini and entropy
7. Random forest algorithm
8. XGBoost Algorithm
9. Central Limit theorem
10. R2
11. Adj R2
12. VIF
13. Different Methods to measure Accuracy
14. Explain Bagging and Boosting
15. Difference Between Bagging and Boosting

Date: 16/06/21
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1. Decorators in Python
– Live Example
2. Generators in Python
– Live Example

3. SQL Questions
3.1 Group by Top 2 Salaries for Employees
3.2 use Row num and Partition

4. Pandas find Numeric and Categorical Columns.
4.1 For Numeric columns, find the mean of the entire column and add that value to each row of the column.

5. What is Gradient Descent?
5.1 What is Learning Rate and Why is it reduce sometimes

6. Two Logistic Regression Models – Which one will you choose – One is trained on 70% and other on 80% data. Accuracy is almost same.
7. What is LogLoss ?

8. Explain your Projects end to end.(15-20mins)

Date: 15/06/21
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1. Tell me about your journey as a Data Science aspirant
2. What was the one challenging project or task that you did in this domain and why was it challenging?
3. What model did you use for that? I replied Random Forests
4. What is Random Forest and how is it used?
5. How are Random Forest different from Decision Trees and what problems do they solve that decision trees can’t?
6. Multi class Classification and which metric is preferred for it
7. Given a banking scenario to predict Loan Defaulters, which metric will you use?
8. How will you handle the class imbalance in this case?

Date: 14/06/21
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1. What is mean and median
2. Difference between normal and gaussian
distribution
3. What is central limit theorem
4. What is null hypothesis
5. What is confidence interval
6. What is covariance and correlation and how will u
interpret it.
7. How will you find out the outliers in the dataset
and is it always to remove outliers
8. Explain about Machine Learning
9. Explain the algorithm of your choice
10. Different methods of missing values imputation
11. Explain me your ml project
12. How did you handle imbalance dataset
13. What is stratified samplings
14. Difference between standard scalar and normal
scalar
15. Different type of visualization in Dl project
16. What architecture have you used
17. Why have u not used RNN in your nlp project
18. Why we don’t prefer CNN in nlp based project
19 What is exploding gradient and vanishing gradient and how to rectify it
20. Difference between LSTM and GRU
21. What is precision and recall
22 What is auc metic
23. What if your precision and recall are same

Date: 13/06/21
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1. Naive bayes assumptions
2. What are the approaches for solving class imbalance problem?
3. When sampling what types of biases can be inflected? How to control the biases?
4. GRU is faster compared to LSTM. Why?
5. What is difference between K-NN and K-Means clustering ?
6. How to determine if a coin is biased ? Hint: Hypothesis testing
7. How will u present the statistical inference of a particular numerical column?
8. How would you design a data science pipeline ?
9. Explain back propagation in few words and it’s variants?
10. Explain topic modeling in NLP and various methods in performing topic modeling.Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

Date: 12/06/21
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1.     SQL question on inner join and cross join
2.     SQL question on group-by
3.     Case study question on customer optimization of records for different marketing promotional offers
4.     Tuple and list
5.     Linear regression
6.     Logistic regression steps and process
7.     Tell me about your passion for data science? Or What brought you to this field?
8.     What is the most common problems you face whilst working on data science projects?
9.      Describe the steps to take to forecast quarterly sales trends. What specific models are most appropriate in this case?
10.   What is the difference between gradient and slope, differentiation and integration?
11.   When to use deep learning instead of machine learning. Advantages, Disadvantages of using deep learning?
12.   What are vanishing and exploding gradients in neural networks?
13.   What happens when neural nets are too small? What happens when they are large enough?
14.   Why do we need pooling layer in CNN? Common pooling methods?
15.   Are ensemble models better than individual models? Why/why – not?

Date: 21/06/21
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1.     Telecom Customer Churn Prediction. Explain the project end to end?
2.     Data Pre-Processing Steps used.
3.     Sales forecasting how is it done using Statistical vs DL models – Efficiency.
4.     Logistic Regression – How much percent of Customer has churned and how much have not churned?
5.     What are the Evaluation Metric parameters for testing Logistic Regression?
6.     What packages in Python can be used for ML? Why do we prefer one over another?
7.     Numpy vs Pandas basic difference.
8.     Feature on which this Imputation was done, and which method did we use there?
9.     Tuple vs Dictionary. Where do we use them?
10.  What is NER – Named Entity Recognition?

Date: 22/06/21
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1. How would you build a model to predict credit card fraud?
2. How do you handle missing or bad data?
3. How would you derive new features from features that already exist?
4. If you’re attempting to predict a customer’s gender, and you only have 100 data points, what problems could arise?
5. Suppose you were given two years of transaction history. What features would you use to predict credit risk?
6. Design an AI program for Tic-tac-toe
7. Explain overfitting and what steps you can take to prevent it.
8. Why does SVM need to maximize the margin between support vectors? 

Date: 14/07/21
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1. Pick any product or app that you really like and describe how you would improve it.
2. How would you find an anomaly in a distribution ?
3. How would you go about investigating if a certain trend in a distribution is due to an anomaly?
4. How would you estimate the impact Uber has on traffic and driving conditions?
5. What metrics would you consider using to track if Uber’s paid advertising strategy to acquire new customers actually works? How would you then approach figuring out an ideal customer acquisition cost?

Date: 15/07/21
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1. Why do you use feature selection?
2. What is the effect on the coefficients of logistic regression if two 3. predictors are highly correlated?
4. What are the confidence intervals of the coefficients?
5. What’s the difference between Gaussian Mixture Model and K-Means?
6. How do you pick k for K-Means?
7. How do you know when Gaussian Mixture Model is applicable?
8. Assuming a clustering model’s labels are known, how do you evaluate the performance of the model?

Date: 15/07/21
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1. How do you prevent overfitting?
2. How do you deal with outliers in your data?
3. How do you analyze the performance of the predictions generated by regression models vs classification models?
4. What is cross-validation and why would you use it?
5. What’s the name of the matrix used to evaluate predictive models?
6. What relationships exist between a logistic regression’s coefficient and the Odds Ratio?
7. What’s the relationship between Principal Component Analysis (PCA) and Linear & Quadratic Discriminant Analysis (LDA & QDA)
8. If you had a categorical dependent variable and a mixture of categorical and continuous independent variables, what algorithms or methods or tools would you use for analysis?
9. What’s the difference between logistic regression and linear regression?
10.How do you avoid local minima?

Date: 15/07/21
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1. How do you prevent overfitting?
2. How do you deal with outliers in your data?
3. How do you analyze the performance of the predictions generated by regression models vs classification models?
4. What is cross-validation and why would you use it?
5. What’s the name of the matrix used to evaluate predictive models?
6. What relationships exist between a logistic regression’s coefficient and the Odds Ratio?
7. What’s the relationship between Principal Component Analysis (PCA) and Linear & Quadratic Discriminant Analysis (LDA & QDA)
8. If you had a categorical dependent variable and a mixture of categorical and continuous independent variables, what algorithms or methods or tools would you use for analysis?
9. What’s the difference between logistic regression and linear regression?
10.How do you avoid local minima?

Date: 15/07/21
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Round 1: Asking about why did you chose Data Science as Career? Questions related to your background if you are from non tech background.

Round 2: What is Supervised learning ? Unsupervised learning? Difference between them.

What is Linear regression in layman terms ? 

What is Overfitiing and Underfitting?
Show how will you create a model on any datadet on jupyter notebook. Dataset can be of your choice.
What is activation function and layers in neural networks.
Show how will you fit a model on mnist dataset on jupyter notebook using layers of neural network.
What is dropouts?
Have you integrated SQL with any project?

Round 3 : what are types of Data structures?
Difference between tuple and sets.
How will you sort a list without using sorted function in python?
What is time complexity of inserting an element in list and set.?
How will you count letters of a string in python?

What is a confusion matrix ? When it is used? What is True positive, False positive, True negative, False negative

Date: 26/06/21
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1. What are the metrics used to test an NLP model?
2. How should you maintain a deployed model?
3. How can time-series data be declared as stationery?
4. People who bought this also bought…’ recommendations seen on Amazon are a result of which algorithm?
5. After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study?
6. Your organization has a website where visitors randomly receive one of two coupons. It is also possible that visitors to the website will not receive a coupon. You have been asked to determine if offering a coupon to website visitors has any impact on their purchase decisions. Which analysis method should you use?

Date: 28/07/21
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ques 1 – Lamda function with an implementation 
ques 2 – Anonymous function
ques 3 – write a query to find duplicate values in two tables
ques 4 – p value in hypothesis testing 
ques 5 – NLP – (Names entity recognition) 
ques 6 – Discussion on the projects  (10 Minutes around)
ques 7 – How you will explain machine learning to a 5-year-old child?

Date: 27/06/21
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1. A model your team has built performs 90% accuracy. What do you need to know in order to interpret whether this is good or not?
2. What is the difference between a box plot and a histogram?
3. Tell me about (a job on your resume). Why did you choose to do it and what do you like most about it?
4. What is negative indexing? Can you give an example for the same? Why is it needed? .
5. What is the difference between descriptive, predictive, and prescriptive analytics.
6. What do you mean by imputation? Explain different types of imputation techniques you have used in your project?
7. What is the condition for using a t-test or a z-test?
8. Given an IP address as an input string, validate it and return True/False

Date: 30/07/21
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1. Have you ever used Lambda function and List comprehension in python, if yes, then give an example?
2. What is the way that you would handle an imbalanced data set that’s being used for prediction when mostly there are more negative classes than positive classes?
3. If a Person A and Person B are playing archery together and their abilities to fire the arrow at the target are the same, and the probability of getting the target is 0.5 for both of them. Now there is a situation that A has fired 201 arrows and B has fired 200 arrows, then, what is the probability that A gets more targets than B?
4. Explain what do we mean by making decisions based on comparing p-value with significance level?
5. Is it possible that the t-value same for 90% two tail and 95% one tail test. If yes, then why is it so?

Date: 29/07/21
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1. What is the benefit of dimensionality reduction?
2. How do you select an appropriate value of k in k-means?
3. Explain bagging and boosting in Data Science. Have applied it in any of your project, if yes then which algorithm did you use?
4. What is Regularization and what kind of problems does regularization solve?
5. What is the difference between squared error and absolute error?
6. How will you explain logistic regression to an economist, physican scientist and biologist?
7. During analysis, how do you treat missing values?
8. Do you think 50 small decision trees are better than a large one? Why?
9. How will you assess the statistical significance of an insight whether it is a real insight or just by chance?
10. Explain the method to collect and analyze data to use social media to predict the weather condition.

Date: 01/08/21
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1. How would you do an A/B test on a new metric to see if it truly captures meaningful social interactions?
2. What’s the difference between a left join, a union, and a right join?
3. Given a list, search for consecutive numbers (n) whose sum is equal to a specific number (x).
4. How would you create a model to find bad sellers on marketplace?
5. How can you predict Samsung phone sales?

Date: 31/07/21
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1. What do you think the distribution of time spent per day on Facebook looks like? What metrics would you use to describe that distribution?
2. You’ve been asked to generate a machine learning model that can map the nicknames of people using Facebook. How do you go about designing this model?
3. How would you build the recommendation algorithm for type-ahead search for Netflix?
4. If 70% of Facebook users on iOS use Instagram, but only 35% of Facebook users on Android use Instagram, how would you investigate the discrepancy?
5. Likes/user and minutes spent on a platform are increasing but total number of users are decreasing. What could be the root cause of it?

Date: 02/08/21
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1. What is the statistical power of sensitivity?
2. If you are given an unsorted data set, how will you read the last observation to a new dataset in python?
3. What is the difference between covariance and correlation?
4. Explain how did you handle missing data in one of the projects mentioned in your resume.
5. What is negative indexing? Why is it needed? Can you give an example for.the same in python
6. How will you estimate the number of weddings that take place in a year in India?
7. What is the condition for using a t-test or a z-test?
8. What is the main difference between overfitting and underfitting?
9. What is the formula for R-squared and adjusted R-squared metrics and when do you decide to use them?
10. What is the KNN imputation method?

Date: 03/08/21
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The Hewlett-Packard Company or HP is an famous American multinational company. HP Software provided software as a service (SaaS), cloud computing solutions, and software services, including consulting, education, professional services, and support. Here we are offering Job Interview Experience for HP. Latest interview question to enhance once knowledge in this area. 

1. If through training all the features in the dataset, an accuracy of 100% is obtained but with the validation set, the accuracy score is 75%. What should be looked out for?
2. For a given dataset, you decide to use SVM as the main classifier. You select RBF as your kernel. What would be the optimum gamma value that would allow you to capture the features of the dataset really well??
3. How is skewness different from kurtosis??
4. How to calculate the accuracy of a binary classification algorithm using its confusion matrix?
5. How will you measure the Euclidean distance between the two arrays in numpy?
6. Given two lists [1,2,3,4,5] and [6,7,8], you have to merge the list into a single dimension. How will you achieve this?
7. In a survey conducted, the average height was 164cm with a standard deviation of 15cm. If Alex had a z-score of 1.30, what will be his height?

Date: 06/08/21
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1. Explain list comprehensions and how they’re used in Python with an example.
2. Add a new column named Active to the employees DataFrame with all 1s to indicate each employee’s active employment status.
3. How can you convert timestamps to date time in MySQL?
4. What is a trigger in MySQL? How can we use it?
5. Explain dimensionality reduction, where it’s used, and its benefits?
6. How would you go about doing an exploratory data analysis (eda)?
7. What are the pros and cons of linear regression vs. tree-based models?
8. What is the difference between the ROC curve and the precision-recall curve?
9. Share the business impact (eg. accuracy, revenue etc.) of one of the project mentioned in your resume. Also share how did other people or teams benefit from the project?
10. Have you tried a simpler model (eg. linear regression)? Why is it necessary to use a more complicated model?

Date: 05/08/21
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