I need help with the following questions:

1. Which of the following are characteristics of Big Data?

A) Data comes from only top 500 businesses

B) Data is from many sources and consists of many types

C) Data generated at a fast speed

D) Immense amount of data

2. Data science has grown exponentially in the past 15-20 years as a result of: (Choose all that apply)

A) The design of experimental studies

B) The general education requirement of students to take statistics/data science courses in college

C) Our ability to create & store massive amounts of data

D) The development of sophisticated algorithms to handle multi-dimensional data of various types

E) Improved computational power that has increase our ability to process the data

3. What is the importance of a well-defined problem statement?

A) To tell you what the problem is

B) To tell your client or boss every step you will go through in the analysis

C) To create something that it is actionable, can be solved with data, and produces a useful outcome

D) To tell you how to solve the problem

Question 7 (1 point) J Saved 'E II Listen b ' Problem and Background: A bank wants to optimize its loan underwriting procedures. Currently, it applies lters on loan applications that automaticallv reiect the riskier ones. However, the bank is still approving too many applications that run into repayment issues. The bank collects a large amount of data for each approved loan: 146 fields. These elds can be split into a few distinct groups: - Loan demographics. such as the amount, the term, the interest rate, the reason for loan, and whether the person defaulted of their loan repayment. - Applicant demographics, such as age. salary, employment length, and home ownership. - Numerous risk factors, such as the number of public records. credit card delinquency, and bankruptcy. Roughly 70 sparsely populated risk fields cover these risk factors. Select the best problem statement I want to determine the attributes about a person and loan type that are associated with a high probability of default - and use them to build a model to predict whether a loan should be approved or denied by the loan underwriting team 0 I want to determine the attributes about a person and loan type that are associated with a high probability of default - and use them to build a model to predict whether a loan should be approved or denied by the loan underwriting team - within 5 days of receiving the loan application. 0 I want to build a model to determine if a loan will be a bad loan or a good loan, within 5 days of receiving a loan application. 0 I want to know what will cause a person to default on a loan