Introduction To BIG DATA Midterm Exam Solution

QUESTION 1

What are the three characteristics of Big Data, and what are the main considerations in processing Big Data?

QUESTION 2
Explain the differences between BI and Data Science.

QUESTION 3
Briefly describe each of the four classifications of Big Data structure types. (i.e. Structured to Unstructured)

QUESTION 4
List and briefly describe each of the phases in the Data Analytics Lifecycle.

QUESTION 5
In which phase would the team expect to invest most of the project time? Why? Where would the team expect to spend the least time?

QUESTION 6
Which R command would create a scatterplot for the dataframe “df”, assuming df contains values for x and y?

QUESTION 7
What is a rug plot used for in a density plot?

QUESTION 8
What is a type I error? What is a type II error? Is one always more serious than the other? Why?

QUESTION 9
Why do we consider K-means clustering as a unsupervised machine learning algorithm?

QUESTION 10
Detail the four steps in the K-means clustering algorithm.

QUESTION 11
List three popular use cases of the Association Rules mining algorithms.

QUESTION 12
Define Support and Confidence

QUESTION 13
How do you use a “hold-out” dataset to evaluate the effectiveness of the rules generated?

QUESTION 14
List two use cases of linear regression models.

QUESTION 15
Compare and contrast linear and logistic regression methods.