MS-DS Master of Data science Study Guide 2026
Everything you need to pass the MS-DS Master of Data science exam in one place: the exam format, every topic to study, real practice questions with explanations, flashcards, and full-length practice tests. Free, no sign-up needed.
📋 MS-DS Master of Data science Exam Format at a Glance
📚 MS-DS Master of Data science Topics to Study (57)
✍️ Sample MS-DS Master of Data science Questions & Answers
1. What is the purpose of A/B testing in a data-driven organization?
A/B testing is a controlled experiment where two variants are compared to measure which one produces a better outcome on a specific metric.
2. Which of the following suggests there is no association between any of the relationships?
The correlation coefficient, Cor(X, Y), measures the strength and direction of the linear relationship between two random variables X and Y. A value of 0 indicates that there is no linear association between the variables. It's important to note that a correlation of 0 does not necessarily imply independence, as non-linear relationships might still exist.
3. The problems with big data veracity go beyond volume, diversity, and velocity.
The '3 V's' (Volume, Velocity, Variety) are commonly associated with Big Data, but 'Veracity' is a fourth crucial dimension. Veracity refers to the trustworthiness, accuracy, and quality of the data, addressing issues like bias, noise, and abnormalities that can significantly impact analysis and decision-making.
4. In maximum likelihood estimation, what is the Fisher information used to approximate?
The inverse of the Fisher information provides the asymptotic variance of the MLE, forming the basis of the Cramér-Rao lower bound.
5. The degrees of freedom in the Chi-squared distribution are twice as many.
For a Chi-squared distribution, the degrees of freedom (often denoted as 'k') define its shape and properties. The mean of a Chi-squared distribution is equal to its degrees of freedom (k), and its variance is equal to twice its degrees of freedom (2k). Therefore, the degrees of freedom are directly related to, and half of, the variance.
6. Which unsupervised learning technique reduces dimensionality by finding orthogonal axes that maximize variance in the data?
PCA identifies principal components as orthogonal directions of maximum variance, enabling dimensionality reduction while preserving the most information.
🎯 Free MS-DS Master of Data science Practice Tests
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