MS-DS Master of Data science Cheat Sheet 2026

The 30 highest-yield MS-DS Master of Data science facts, distilled from real exam questions. Print it, save it as a PDF, or study it here — free, no sign-up.

1. What is the primary risk of using a dual-axis chart? → It can mislead viewers by implying a correlation between unrelated variables
2. In Gaussian Mixture Models (GMM), what algorithm is typically used to estimate the model parameters? → Expectation-Maximization (EM)
3. In a Gaussian Mixture Model (GMM), what algorithm is typically used to estimate the model parameters? → Expectation-Maximization (EM)
4. A researcher notices that their linear regression model has residuals that fan out as predicted values increase. What assumption is being violated? → Homoscedasticity
5. What is the main advantage of AdaBoost over a single decision tree? → It sequentially focuses on misclassified samples to improve overall accuracy
6. When making predictions, trees examine each set of data's . → homogeneity
7. Which visualization is most appropriate for examining the relationship between two continuous variables in exploratory data analysis? → Scatter plot
8. Which technique helps address class imbalance in a binary classification dataset? → SMOTE (Synthetic Minority Over-sampling Technique)
9. When applying a pipeline in scikit-learn, why should you fit the preprocessor only on training data and then transform both training and test data? → To prevent data leakage from the test set into the model
10. What is the primary purpose of cross-validation in machine learning model development? → To estimate how well the model generalizes to unseen data
11. What is the key assumption of the Naive Bayes classifier? → All features are conditionally independent given the class label
12. Identify the error in the statement. → Adding squared terms makes it twice continuously differentiable at the knot points
13. In feature engineering, what is the purpose of creating interaction terms between two variables? → To capture the combined effect of two features that may not be represented by either alone
14. In a convolutional neural network (CNN), what is the primary purpose of a pooling layer? → Reduce spatial dimensions and computation
15. Which of the following statements about random forest is accurate? → Random forest are difficult to interpret but often very accurate
16. Which of the subsequent functions can be used to maximize the minimal differences? → sumDiss
17. Which sampling method should a data science researcher use to ensure that subgroups in a population are proportionally represented in the sample? → Stratified random sampling
18. In sentiment analysis, what challenge does sarcasm detection present for standard classifiers? → The literal meaning of words contradicts the intended sentiment
19. In logistic regression, what does the odds ratio represent when an independent variable increases by one unit? → The multiplicative change in the odds of the outcome
20. Which of the following information is entered into a formula to provide findings that are widely accepted? → Processed
21. What does 'feature interaction' refer to in the context of feature engineering? → Creating new features by combining two or more existing features
22. What is the role of the attention mechanism in transformer-based deep learning models? → Allow the model to weigh the relevance of each input token when producing an output
23. What technique is used in recurrent neural networks (RNNs) to address exploding gradients during backpropagation through time? → Gradient clipping
24. The term "_______ deviation" refers to the square root of variance. → standard
25. In gradient boosting, what does each subsequent tree attempt to predict? → The residual errors of the previous model
26. Which of the following methods falls under the category of applied machine learning? → Boosting
27. What does the area under the Precision-Recall curve (AUPRC) measure that AUROC may fail to capture? → Model performance on the minority class in imbalanced settings
28. What does a bimodal distribution suggest about the underlying data? → The data likely contains two distinct subgroups
29. In Bayesian inference, what does the posterior distribution represent? → The updated belief about a parameter after observing data
30. What does a high variance and low bias in a model's predictions typically indicate? → The model is overfitting the training data