Data Science for Humanity — Summer 2022

What are various types of data measurement scales? Which operations can be performed on ration scale?

What is Web Scrapping? How is it used? Give one example of it.

Mention at least three applications of Data Science. Explain a case study in any one of the domains.

Differentiate between Parameter and Statistic. What is the role of inferential statistics?

What is the relationship between PDF and CDF of a continuous random variable?

For what purpose analytics process is used? Explain various types of analytical techniques with examples.

Demonstrate the Logistic Regression Model’s diagnostics in terms of Classification Table, Sensitivity, and Specificity.

How do you find the standard error of a point estimate?

What does the central limit theorem say? Where is it applicable?

What are various means for measures of position and measures of variability. Explain three of each.

How to estimate population variance from multiple samples?

How can the shape of a data distribution be measured? Explain them.

Explain any three of the following: 1. Exponential Distribution 2. Chi-Square Distribution 3. Student’s t-Distribution 4. F-Distribution

What is the use of maximum likelihood estimation?

What is pruning? What are prepruning and postpruning techniques?

Explain a case study for classification using a decision tree.

How are the correlations and relationships related to each other?

What are the advantages and disadvantages of regression models?

Differentiate the three popular decision tress ID3, CHAID, and CART based on various parameters.

What is sampling distribution and why is it important?

In which scenario, logistic regression is preferred? How it works?

What is the difference between population and sample? What are various sampling methods? Explain them with example.

Tickets numbered 1 to 20 are mixed up and then a ticket is drawn at random. What is the probability that the ticket drawn has a number which is a multiple of 3 or 5?

explain the least squares and the fitted model in concern of a linear regression.

In what ways is NB better than other classification techniques? Compare with decision trees.