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Noodle Analytics Associate Data Scientist Interview Questions and Answers

Updated 5 Dec 2016

9 Interview questions

An Associate Data Scientist was asked
Q. How can a string be reversed without affecting memory size?
Ans. 

A string can be reversed without affecting memory size by swapping characters from both ends.

  • Iterate through half of the string length

  • Swap the characters at the corresponding positions from both ends

An Associate Data Scientist was asked
Q. Explain the concept of hypothesis testing intuitively using distribution curves for null and alternate hypotheses.
Ans. 

Hypothesis testing is a statistical method to determine if there is enough evidence to support or reject a claim.

  • Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

  • The null hypothesis assumes that there is no significant difference or relationship between variables.

  • The alternative hypothesis suggests that there is a significant difference or relationship between variables.

  • Distr...

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An Associate Data Scientist was asked
Q. What is gradient boosting?
Ans. 

Gradient boosting is a machine learning technique that combines multiple weak models to create a strong predictive model.

  • Gradient boosting is an ensemble method that iteratively adds new models to correct the errors made by previous models.

  • It is a type of boosting algorithm that focuses on reducing the residual errors in predictions.

  • Gradient boosting uses a loss function and gradient descent to optimize the model'...

An Associate Data Scientist was asked
Q. What is the difference between XGBoost and AdaBoost algorithms?
Ans. 

XGBoost and AdaBoost are both boosting algorithms, but XGBoost is an optimized version of AdaBoost.

  • XGBoost is an optimized version of AdaBoost that uses gradient boosting.

  • AdaBoost combines weak learners into a strong learner by adjusting weights.

  • XGBoost uses a more advanced regularization technique called 'gradient boosting'.

  • XGBoost is known for its speed and performance in large-scale machine learning tasks.

  • Both ...

An Associate Data Scientist was asked
Q. What would you do if the training data is skewed?
Ans. 

Addressing skewed training data in data science

  • Analyze the extent of skewness in the data

  • Consider resampling techniques like oversampling or undersampling

  • Apply appropriate evaluation metrics that are robust to class imbalance

  • Explore ensemble methods like bagging or boosting

  • Use synthetic data generation techniques like SMOTE

  • Consider feature engineering to improve model performance

  • Regularize the model to avoid overf...

An Associate Data Scientist was asked
Q. What is the cost function for linear and logistic regression?
Ans. 

The cost function for linear regression is mean squared error (MSE) and for logistic regression is log loss.

  • The cost function for linear regression is calculated by taking the average of the squared differences between the predicted and actual values.

  • The cost function for logistic regression is calculated using the logarithm of the predicted probabilities.

  • The goal of the cost function is to minimize the error betw...

An Associate Data Scientist was asked
Q. What is regularization? Why is it used?
Ans. 

Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.

  • Regularization helps to reduce the complexity of a model by discouraging large parameter values.

  • It prevents overfitting by adding a penalty for complex models, encouraging simpler and more generalizable models.

  • Common regularization techniques include L1 regularization (Lasso), L2 regularizati...

Are these interview questions helpful?
An Associate Data Scientist was asked
Q. Why do you think the objective of predictive modeling is minimizing the cost function? How would you define a cost function after all?
Ans. 

The objective of predictive modeling is to minimize the cost function as it helps in optimizing the model's performance.

  • Predictive modeling aims to make accurate predictions by minimizing the cost function.

  • The cost function quantifies the discrepancy between predicted and actual values.

  • By minimizing the cost function, the model can improve its ability to make accurate predictions.

  • The cost function can be defined d...

An Associate Data Scientist was asked
Q. What is principal component analysis? When would you use it?
Ans. 

Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.

  • PCA is used to identify patterns and relationships in data by reducing the number of variables.

  • It helps in visualizing and interpreting complex data by representing it in a simpler form.

  • PCA is commonly used in fields like image processing, genetics, finance, and social s...

Noodle Analytics Associate Data Scientist Interview Experiences

2 interviews found

Associate Data Scientist Interview Questions & Answers

user image Shyam Krishna Sannapaneni

posted on 4 Dec 2016

I applied via Campus Placement and was interviewed in Jan 2016. There were 5 interview rounds.

Interview Questionnaire 

2 Questions

  • Q1. What is your previous experience in Data Analytics?
  • Ans. 

    I have 2 years of experience in data analytics, including working with large datasets and creating data visualizations.

    • Worked with large datasets to extract meaningful insights

    • Created data visualizations using tools like Tableau and Power BI

    • Utilized statistical analysis techniques to identify trends and patterns

    • Collaborated with cross-functional teams to drive data-driven decision making

  • Answered by AI
  • Q2. Technical questions about Data analytics

Interview Preparation Tips

Round: Resume Shortlist
Experience: CG shortlist

College Name: IIT Madras

I applied via Campus Placement and was interviewed in Dec 2016. There were 6 interview rounds.

Interview Questionnaire 

14 Questions

  • Q1. Walk us through your resume
  • Ans. 

    I have a strong background in data analysis and machine learning with experience in various industries.

    • Bachelor's degree in Statistics with a focus on machine learning

    • Worked as a data analyst at XYZ company, where I developed predictive models to optimize marketing strategies

    • Internship at ABC company, where I analyzed customer data to improve retention rates

    • Proficient in programming languages such as Python and R

  • Answered by AI
  • Q2. Why analytics?
  • Ans. 

    Analytics helps uncover insights from data to drive informed decision-making and improve business outcomes.

    • Analytics allows for data-driven decision-making

    • Helps identify trends and patterns in data

    • Enables businesses to optimize processes and strategies

    • Can lead to improved efficiency and effectiveness

    • Allows for predictive modeling and forecasting

    • Examples: using customer data to personalize marketing campaigns, analyzing...

  • Answered by AI
  • Q3. Explain the concept of hypothesis testing intuitively using distribution curves for null and alternate hypotheses
  • Ans. 

    Hypothesis testing is a statistical method to determine if there is enough evidence to support or reject a claim.

    • Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

    • The null hypothesis assumes that there is no significant difference or relationship between variables.

    • The alternative hypothesis suggests that there is a significant difference or relationship between variables.

    • Distributi...

  • Answered by AI
  • Q4. A simple probability puzzle was asked
  • Q5. How can a string be reversed without affecting memory size?
  • Ans. 

    A string can be reversed without affecting memory size by swapping characters from both ends.

    • Iterate through half of the string length

    • Swap the characters at the corresponding positions from both ends

  • Answered by AI
  • Q6. What is gradient boosting?
  • Ans. 

    Gradient boosting is a machine learning technique that combines multiple weak models to create a strong predictive model.

    • Gradient boosting is an ensemble method that iteratively adds new models to correct the errors made by previous models.

    • It is a type of boosting algorithm that focuses on reducing the residual errors in predictions.

    • Gradient boosting uses a loss function and gradient descent to optimize the model's per...

  • Answered by AI
  • Q7. What is the difference between XGBoost and AdaBoost algorithms?
  • Ans. 

    XGBoost and AdaBoost are both boosting algorithms, but XGBoost is an optimized version of AdaBoost.

    • XGBoost is an optimized version of AdaBoost that uses gradient boosting.

    • AdaBoost combines weak learners into a strong learner by adjusting weights.

    • XGBoost uses a more advanced regularization technique called 'gradient boosting'.

    • XGBoost is known for its speed and performance in large-scale machine learning tasks.

    • Both algor...

  • Answered by AI
  • Q8. Explain one interesting project on your resume which is relevant to the profile
  • Ans. 

    Developed a machine learning model to predict customer churn for a telecom company

    • Collected and cleaned customer data including usage patterns and demographics

    • Used classification algorithms like Random Forest and Logistic Regression to build the model

    • Evaluated model performance using metrics like accuracy, precision, and recall

    • Implemented the model in a production environment for real-time predictions

  • Answered by AI
  • Q9. What would you do if the training data is skewed?
  • Ans. 

    Addressing skewed training data in data science

    • Analyze the extent of skewness in the data

    • Consider resampling techniques like oversampling or undersampling

    • Apply appropriate evaluation metrics that are robust to class imbalance

    • Explore ensemble methods like bagging or boosting

    • Use synthetic data generation techniques like SMOTE

    • Consider feature engineering to improve model performance

    • Regularize the model to avoid overfittin...

  • Answered by AI
  • Q10. What is principal component analysis? When would you use it?
  • Ans. 

    Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.

    • PCA is used to identify patterns and relationships in data by reducing the number of variables.

    • It helps in visualizing and interpreting complex data by representing it in a simpler form.

    • PCA is commonly used in fields like image processing, genetics, finance, and social scienc...

  • Answered by AI
  • Q11. What is the cost function for linear and logistic regression?
  • Ans. 

    The cost function for linear regression is mean squared error (MSE) and for logistic regression is log loss.

    • The cost function for linear regression is calculated by taking the average of the squared differences between the predicted and actual values.

    • The cost function for logistic regression is calculated using the logarithm of the predicted probabilities.

    • The goal of the cost function is to minimize the error between t...

  • Answered by AI
  • Q12. What is regularization? Why is it used?
  • Ans. 

    Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.

    • Regularization helps to reduce the complexity of a model by discouraging large parameter values.

    • It prevents overfitting by adding a penalty for complex models, encouraging simpler and more generalizable models.

    • Common regularization techniques include L1 regularization (Lasso), L2 regularization (R...

  • Answered by AI
  • Q13. Why do you think the objective of predictive modeling is minimizing the cost function? How would you define a cost function after all?
  • Ans. 

    The objective of predictive modeling is to minimize the cost function as it helps in optimizing the model's performance.

    • Predictive modeling aims to make accurate predictions by minimizing the cost function.

    • The cost function quantifies the discrepancy between predicted and actual values.

    • By minimizing the cost function, the model can improve its ability to make accurate predictions.

    • The cost function can be defined differ...

  • Answered by AI
  • Q14. Why our company and why not masters in data science?
  • Ans. 

    I chose your company because of its strong reputation and the opportunity to work on diverse projects.

    • Your company has a strong reputation in the industry.

    • I am impressed by the diverse range of projects your company is involved in.

    • Your company offers a collaborative and innovative work environment.

    • I believe working at your company will provide me with valuable hands-on experience.

    • Your company's commitment to profession...

  • Answered by AI

Interview Preparation Tips

Round: Resume Shortlist
Experience: CGPA and background in data science were considered

Round: Test
Experience: We did not have a test as the company registered late. But from next time, they are definitely going to conduct a test. One can expect questions on probability and machine learning.
Duration: 1 hour

Round: Technical Interview
Experience: Any technical interview would mostly start with "walk us through your resume". The rest of the interview depends on how you drive their focus on to your spikes in resume. I was the winner/finalist in three pan-India data science competitons and authored a techinal paper on machine learning/ predictive modeling. I was asked to explain my approaches and the math/principle behind the working of Random forests, differences between XG Boost and Adaboost algorithms. You can expect these kind of questions if you mention such algorithms.


Tips: Know your resume inside-out. If you mention courses like machine learning or time-series analysis, you will be asked to explain algorithms using math equations. If you mention programming languages you will be asked to write a code.

Do not forget to ask questions at the end of the interview. It is the golden opportunity to gain brownie points.




Round: Technical + HR Interview
Experience: They asked me to start off by explaining any one of the projects that are relevant to the profile. While I was explaining, they asked many questions on data preprocessing, model building and model validation techniques. Later, I was asked three puzzles on probability and number theory. Then I was asked typical HR questions like "Why our company?". They asked me if I would consider pursuing masters in data science.




Tips: The amount of grilling is directly proportional to the stuff on your resume. Since I had competitions and a publication, I was properly grilled on basics of ML. But, this is not the case with other selected candidates. However, you would be definitely asked many questions/puzzles on probability.

Confidence and attitude are the major qualities you need to carry with you while attending an interview. Be firm with your answers. They should be simple and to the point. Make sure that your answers to questions like "tell us about yourself" and "walk us through your resume" are open-ended. You should leave hints about your spikes and then the interviewer comes in your way asking questions on your spikes.

Skills: Probability And Statistics, Machine Learning, Basic Coding
College Name: IIT Madras

Skills evaluated in this interview

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Can speak English, can’t deliver in interviews
I feel like I can't speak fluently during interviews. I do know english well and use it daily to communicate, but the moment I'm in an interview, I just get stuck. since it's not my first language, I struggle to express what I actually feel. I know the answer in my head, but I just can’t deliver it properly at that moment. Please guide me
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Interview questions from similar companies

I applied via Naukri.com and was interviewed in Oct 2020. There was 1 interview round.

Interview Questionnaire 

2 Questions

  • Q1. Basic Introduction, Why u want to join this company, why u want to do this work?
  • Q2. Strengths and weekness, Where do u see urself in x year?

Interview Preparation Tips

Interview preparation tips for other job seekers - Nothing to worry, Chill interview session, Main focus should be on Written test that include Apti, Resoning, and Company related task(easy).
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
  • Q1. Excel, Pivot and conditioning format
  • Q2. Other tool skills
  • Q3. Previous experience
Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - HR 

(4 Questions)

  • Q1. What are your salary expectations?
  • Q2. Share details of your previous job.
  • Q3. Why are you looking for a change?
  • Q4. Tell me about yourself.
Round 3 - One-on-one 

Interview Preparation Tips

Interview preparation tips for other job seekers - Okay not bad. Interesting
Interview experience
2
Poor
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Apr 2024. There were 3 interview rounds.

Round 1 - HR 

(1 Question)

  • Q1. Details query specifically my last salary
Round 2 - Technical 

(1 Question)

  • Q1. Excel test, typing test
Round 3 - Group Discussion 

Business process and overview

Interview Preparation Tips

Interview preparation tips for other job seekers - Third class company. Don't go.
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I appeared for an interview before Apr 2024, where I was asked the following questions.

  • Q1. Dax questions and basic question
  • Q2. Sql question and queries

Interview Preparation Tips

Interview preparation tips for other job seekers - Managers and HR personnel often lack effectiveness; they manipulate situations, compromising job security.
Are these interview questions helpful?
Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
4-6 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed in Sep 2023. There were 2 interview rounds.

Round 1 - One-on-one 

(1 Question)

  • Q1. Tableau difference between blend &join
  • Ans. 

    Blend combines data from different data sources in a single view, while join combines data from the same data source.

    • Blend is used when data comes from different data sources, join is used when data comes from the same data source

    • Blend creates a relationship between data sources without actually merging the data, join merges the data based on a common field

    • Blend can be used to combine data from different databases like...

  • Answered by AI
Round 2 - Technical 

(1 Question)

  • Q1. SQL-related questions working of self join and what will be used in case of 2nd max

Interview Preparation Tips

Topics to prepare for ScatterPie Analytics Data Analyst interview:
  • SQL
  • Tableau
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Job Portal and was interviewed before Mar 2023. There were 4 interview rounds.

Round 1 - HR 

(1 Question)

  • Q1. Basic details as to why looking for a change
Round 2 - Technical 

(1 Question)

  • Q1. Intermediate questions for tableau, SQL
Round 3 - Technical 

(1 Question)

  • Q1. Managerial interview
Round 4 - HR 

(1 Question)

  • Q1. Salary negotiation
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed before Feb 2022. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Don’t add your photo or details such as gender, age, and address in your resume. These details do not add any value.
View all tips
Round 2 - Aptitude Test 

Basic aptitude questions of quant and reasoning and general english

Round 3 - Case Study 

Case study of varies products asked around 15-20 which were easy to attempt

Round 4 - HR 

(2 Questions)

  • Q1. About yourself, practical knowledge, family, data related,etc.
  • Q2. What is sql. Excel formulas
  • Ans. 

    SQL is a programming language used for managing and manipulating relational databases. Excel formulas are used for performing calculations and data analysis in Microsoft Excel.

    • SQL is used to retrieve, insert, update, and delete data from databases.

    • Excel formulas are written using functions and operators to perform calculations and manipulate data.

    • SQL example: SELECT * FROM customers WHERE age > 30;

    • Excel formula example...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - The interview process is pretty simple and quick. Basics of data and research knowledge is must.

Skills evaluated in this interview

Noodle Analytics Interview FAQs

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  1. Why do you think the objective of predictive modeling is minimizing the cost fu...read more
  2. How can a string be reversed without affecting memory si...read more
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