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Introduction to big data midterm exam solution

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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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