Explain the three main types of business analytics: descriptive, predictive, and prescriptive, and provide an example of each.
Discuss the potential future trends and challenges in data-driven decision making as technology continues to evolve. Provide examples to support your points.
Discuss the four major types of data measurement scales: nominal, ordinal, interval, and ratio. Provide examples for each.
Compare and contrast the merits and limitations of using mean, median, and mode as measures of central tendency. Provide examples to illustrate your points.
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Differentiate between qualitative and quantitative data types, providing an example of each.
Discuss need of Skewness and Kurtosis. Explain its types with example.
What is Probability Distribution function? Explain Uniform Distribution, Normal Distribution, and Exponential Distribution in brief.
Define descriptive analytics and explain its primary purpose in data analysis.
Explain the Chi-Square distribution, Student's t-distribution and F- Distribution in brief.
Explain Bayes' Theorem and provide an example of how it is used to update probabilities in real-life situations.
Provide a detailed history of regression modeling, highlighting key milestones and developments from Francis Galton's work to modern regression techniques.
Explain why Central Limit Theorem is called as a heart of the Data Science.
List out steps involved in building a Simple Linear Regression model, from data collection to model interpretation.
Discuss decision tree algorithm for classification of data with example.
Define sensitivity and specificity in the context of Logistic Regression and their relevance in classification problems.
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Official Gujarat Technological University (GTU) examination paper and step-by-step solutions for Data Science (Winter 2025, B.E. · IT Engineering, Sem 5). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.
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