Hero FinCorp Data Scientist Interview Questions and Answers

Q1. Machine learning algorithms
Ans. 
Machine learning algorithms are used to analyze data and make predictions or decisions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms learn from data to improve their performance over...read more
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Q2. SQL basics and basic knowledge of security products
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Q1. Very lengthy process and delayed a lot
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Q1. Common ways to evaluate Time Series model
Ans. 
Common ways to evaluate Time Series model include AIC, BIC, RMSE, MAE, ACF, PACF, etc.
• Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare models

• Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to assess model accuracy

• Analyze Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to check for autocorrelation in residuals
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Q2. Best ways to handle multicollinearity
Ans. 
Use techniques like feature selection, regularization, PCA, and VIF to handle multicollinearity.
• Perform feature selection to choose the most relevant variables for the model.

• Apply regularization techniques like Lasso or Ridge regression to penalize high coefficients.

• Utilize Principal Component Analysis (PCA) to reduce dimensionality and decorrelate variables.

• Check for Variance Inflation Factor (VIF) to identify highly
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Q1. Write a function taking input as string and output a dictionary which will give key as characters in these string and values as their frequency of occurrence
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Q2. TF IDF in NLP
Ans. 
TF IDF is a technique used in NLP to measure the importance of a word in a document within a collection of documents.
• TF IDF stands for Term Frequency-Inverse Document Frequency.

• It is used to determine how important a word is in a document relative to a collection of documents.

• TF IDF is calculated by multiplying the term frequency (TF) of a word in a document by the inverse document frequency (IDF) of the word across al...read more
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Q1. Azure Data Lake, Prediction model
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I was a test in our college of about 45min revolving around aptitude.

Few basic coding questions.

Q1. About linear and logistic regression
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Q2. About svm and kernels
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The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.

Normal aptitude questions

Q1. Metrics and related questions
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Q1. Machine learning algorithms.
Ans. 
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
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Q2. Credit risk life cycle
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Q3. Pandas related questions
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Q1. Steps of developing a credit risk model
Ans. 
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
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Q2. Pandas related questions
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Q3. Bagging and Boosting
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Q1. Explain AIC and BIC
Ans. 
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
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Q2. Difference between xgboost and lightgbm
Ans. 
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
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Q3. Bagging and boosting
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