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Noodle Analytics Associate Data Scientist Interview Questions and Answers
9 Interview questions
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...
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
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'...
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 ...
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...
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...
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...
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...
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
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
- Q2. Technical questions about Data analytics
Interview Preparation Tips
Experience: CG shortlist
College Name: IIT Madras
Kiran Sripada
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
- Q2. Why analytics?
- 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...
- Q4. A simple probability puzzle was asked
- Q5. How can a string be reversed without affecting memory size?
- 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...
- 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...
- 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
- 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...
- 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...
- 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...
- 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...
- 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...
- 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...
Interview Preparation Tips
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
Top trending discussions
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Crif High Mark Credit Information Services
Anonymous
posted on 2 Jan 2021
I applied via Referral and was interviewed before Jan 2020. There were 6 interview rounds.
Interview Questionnaire
1 Question
- Q1. SQL and plsql related questions
Interview Preparation Tips
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
Senior Associate Interview Questions & Answers
Allied Market Research
Anonymous
posted on 16 Oct 2024
Selected
I applied via LinkedIn and was interviewed in Sep 2024. There were 2 interview rounds.
Report writing- you have to present a market research overview on any of industry you can visit on their website for same
(2 Questions)
- Q1. Analyse market trends
- Ans.
Market trends can be analyzed by examining consumer behavior, industry reports, and economic indicators.
Examine consumer behavior through surveys and focus groups
Review industry reports and forecasts for insights
Monitor economic indicators such as GDP growth and inflation rates
Look for emerging technologies or innovations that could impact the market
Consider geopolitical events or regulatory changes that may influence ...
- Q2. Guest estimate for any industry
- Ans.
Guest estimate for any industry refers to the practice of predicting the number of guests or customers a business may expect to receive within a certain period of time.
Consider historical data and trends to make an educated guess
Take into account external factors such as seasonality, holidays, and economic conditions
Utilize forecasting models or software to help with the estimation process
Data Analyst Interview Questions & Answers
Crif High Mark Credit Information Services
Anonymous
posted on 18 Sep 2024
Selected
I applied via LinkedIn and was interviewed before Sep 2023. There was 1 interview round.
(2 Questions)
- Q1. What's is work experience.
- Ans.
Work experience refers to the practical knowledge and skills gained from employment in a specific field or industry.
Work experience includes tasks, responsibilities, and projects completed in a professional setting.
It can be gained through internships, part-time jobs, full-time positions, or volunteer work.
Work experience helps individuals develop industry-specific skills, build a professional network, and gain a bette...
- Q2. Whats is sql and power bi.
- Ans.
SQL is a programming language used for managing and querying databases. Power BI is a business analytics tool for visualizing and analyzing data.
SQL stands for Structured Query Language and is used to communicate with databases.
SQL can be used to retrieve, update, and manipulate data in databases.
Power BI is a data visualization tool that allows users to create interactive reports and dashboards.
Power BI can connect to...
Skills evaluated in this interview
Senior Executive Interview Questions & Answers
Nyx-medical Solutions
Anonymous
posted on 11 May 2022
I applied via Recruitment Consulltant and was interviewed in Apr 2022. There were 2 interview rounds.
(1 Question)
- Q1. All related to Medical billing q/a
Interview Preparation Tips
Selected
I applied via Referral and was interviewed before Aug 2022. There were 3 interview rounds.
Normal Group discussion
(1 Question)
- Q1. Cosing, project management etc
Interview Preparation Tips
Selected
I appeared for an interview before Feb 2024.
Interviewer himself did not knew python or any other data analysis language
Excel, questions that were open ended
(4 Questions)
- Q1. How will you work under female manager
- Ans.
I will work under a female manager just like I would under any other manager - with professionalism, respect, and open communication.
I will communicate openly and honestly with my female manager, just as I would with any other manager.
I will respect her authority and expertise, and follow her guidance and instructions.
I will collaborate effectively with my female manager and the rest of the team to achieve our goals.
I ...
- Q2. Are you willing to work for lower salary than previous
- Ans.
I am open to discussing salary based on the overall compensation package and opportunities for growth.
I am open to discussing salary based on the overall compensation package and opportunities for growth.
I would like to understand the reasons for the lower salary offer and how it aligns with the company's budget and market standards.
I am willing to negotiate other benefits such as flexible work hours, additional vacati...
- Q3. Will your accomodation or salary structure needed to be validated in case you got married
- Q4. Will you work till 1 am in the morning as bjp politicians client can ask anything anytime
(2 Questions)
- Q1. Why ai and machine learning is only marketing term and not a real technology
- Ans.
AI and machine learning are real technologies with practical applications in various industries.
AI and machine learning have been successfully used in various industries such as healthcare, finance, and transportation to improve efficiency and accuracy.
Companies like Google, Amazon, and Facebook heavily rely on AI and machine learning algorithms to enhance their products and services.
AI and machine learning technologie...
- Q2. Why python is an awful language and would you be willing to work as a back end developer using spring or Node
- Ans.
Python is not an awful language, but I am open to working with other technologies like Spring or Node.
Python is a versatile and widely-used language in data analysis and machine learning.
It has a large community and extensive libraries like Pandas and NumPy.
Spring and Node are also popular choices for backend development, offering different strengths and capabilities.
I am open to learning and working with new technolog...
(2 Questions)
- Q1. Will you work under bond of 2 yrs ?
- Ans.
I am open to discussing the terms of the bond and considering the overall opportunity.
I am willing to consider a bond if the terms are reasonable and the opportunity aligns with my career goals.
I would like to discuss the details of the bond such as its purpose, consequences of breaking it, and any potential benefits for me.
I may negotiate the duration of the bond or explore alternative options to ensure a mutually ben...
- Q2. We will give 30% of your salary as Variable pay after completion of 1 yr but we will decide when to give it to you. Would you still be willing to work for us as working for our company is a prestigious opp...
- Ans.
I would consider the offer based on the overall compensation package and career growth opportunities.
I would need more information on the overall compensation package to make a decision.
I would also consider the potential career growth opportunities and learning experiences at the company.
It would be important to understand the criteria and timing for receiving the variable pay.
I would evaluate the offer based on my lo...
Interview Preparation Tips
Aptitude test of 30 min is conducted
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