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.