10+ Jr. Data Scientist Interview Questions and Answers for Freshers

Left join returns all records from left table and matching records from right table. Right join returns all records from right table and matching records from left table.

• Left join keeps all records from the left table and only matching records from the right table

• Right join keeps all records from the right table and only matching records from the left table

• Left join is denoted by LEFT JOIN keyword in SQL

• Right join is denoted by RIGHT JOIN keyword in SQL

• Left join is useful whe...read more

I have experience deploying machine learning models using cloud services like AWS SageMaker and Azure ML.

• Deployed a sentiment analysis model on AWS SageMaker for real-time predictions

• Deployed a recommendation system model on Azure ML for batch predictions

• Used Docker containers to deploy models in production environments

KPIs of Classification Model

• Accuracy: measures the proportion of correct predictions

• Precision: measures the proportion of true positives among predicted positives

• Recall: measures the proportion of true positives among actual positives

• F1 Score: harmonic mean of precision and recall

• ROC Curve: plots true positive rate against false positive rate

• Confusion Matrix: summarizes the performance of a classification model

Boosting is an ensemble learning technique that combines multiple weak learners to create a strong learner, often using decision trees.

• Boosting is an iterative process where each weak learner is trained to correct the errors of the previous ones.

• Decision trees are commonly used as the base learner in boosting algorithms like AdaBoost and Gradient Boosting.

• Boosting algorithms like XGBoost and LightGBM are popular in machine learning for their high predictive accuracy.

Transformers are models used in natural language processing (NLP) that learn contextual relationships between words.

• Transformers use self-attention mechanisms to weigh the importance of different words in a sentence.

• They have revolutionized NLP tasks such as language translation, sentiment analysis, and text generation.

• Examples of transformer models include BERT, GPT-3, and RoBERTa.

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.).

• It estimates the probability that a given input belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic function (sigmoid function) to map the input to the output.

• Example: Pre...read more

Mean, median, and mode are measures of central tendency on a distribution curve.

• Mean is the average of all the values in the distribution.

• Median is the middle value when the data is arranged in ascending order.

• Mode is the value that appears most frequently in the distribution.

• For example, in a distribution of [2, 3, 3, 4, 5], the mean is 3.4, the median is 3, and the mode is 3.

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that represents the relationship between the variables.

• The equation f...read more

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

• Logistic regression is used when the dependent variable is binary (e.g., 0 or 1, yes or no).

• It estimates the probability that a given input belongs to a certain category.

• It uses the logistic function to model the relationship between the dependent variable and independent variables.

• Coefficients in logistic regression represent the impact of t...read more

Bias-variance tradeoff is the balance between underfitting (high bias) and overfitting (high variance) in machine learning models.

• Bias is error from erroneous assumptions in the learning algorithm, leading to underfitting.

• Variance is error from sensitivity to fluctuations in the training data, leading to overfitting.

• Finding the right balance between bias and variance is crucial for optimal model performance.

• Regularization techniques like Lasso and Ridge regression can help in...read more