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.

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