Accenture Data Scientist Interview Questions and Answers

The choice of ML model depends on the problem, data, and desired outcome.

• Consider the problem type: classification, regression, clustering, etc.

• Analyze the data: size, quality, features, and target variable.

• Evaluate model performance: accuracy, precision, recall, F1-score.

• Consider interpretability, scalability, and computational requirements.

• Experiment with multiple models: decision trees, SVM, neural networks, et...read more

A distribution describes how values of a variable are spread or arranged, often represented graphically or mathematically.

• Normal Distribution: A bell-shaped curve where most values cluster around the mean (e.g., heights of people).

• Binomial Distribution: Represents the number of successes in a fixed number of trials (e.g., flipping a coin).

• Poisson Distribution: Models the number of events occurring in a fixed inter...read more

Different splitting criteria in decision trees include Gini impurity, entropy, and misclassification error. Random forest works better due to ensemble learning and reducing overfitting.

• Splitting criteria in decision trees: Gini impurity, entropy, misclassification error

• Random forest works better due to ensemble learning and reducing overfitting

• Random forest combines multiple decision trees to improve accuracy and ...read more

Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.

• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')

The question asks about the last 2 projects, ML algorithms, the difference between random forest and xgboost, hyperparameter tuning, NLP questions, and chi-sq test.

• Discuss the details of the last 2 projects you worked on

• Explain various machine learning algorithms you are familiar with

• Highlight the differences between random forest and xgboost

• Describe your experience with hyperparameter tuning

• Answer NLP-related que...read more

Mission learning is used for data analysis and prediction with additional algorithms for AI.

• Mission learning is a subset of machine learning that focuses on predicting outcomes based on data analysis.

• It involves using algorithms to learn patterns and make predictions based on new data.

• Examples include image recognition, natural language processing, and recommendation systems.