Python Project for Data Engineering Coursera Quiz Answers

All Weeks Python Project for Data Engineering Coursera Quiz Answers

This mini-course is intended to apply foundational Python skills by implementing different techniques to collect and work with data. Assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific data types, and then load it into a single source for analysis.

Continue with the course and test your knowledge by implementing web scraping and extracting data with APIs all with the help of multiple hands-on labs. After completing this course you will have acquired the confidence to begin collecting large datasets from multiple sources and transform them into one primary source, or begin web scraping to gain valuable business insights all with the use of Python.

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Data Engineering Capstone Project Week 01 Quiz Answers

Quiz 01 : Webscraping

Q1. From the lab you were asked to print out the output of the following line, and remember it as it will be a quiz question:

  • ‘List of largest banks -‘
  • ‘List of largest banks ‘
  • ‘<!DOCTYPE html>\n<html cla’

Q2. Using the contents and beautiful soup load the data from the By market capitalization table into a pandas dataframe. The dataframe should have the country Name and Market Cap (US$ Billion) as column names. Display the first five rows using head.

Q3. True or False If the website changes your output will remain the same

  • False
  • True

Quiz 02 :Extracting Data using API

Q1. Using the requests library call the endpoint given in the preceding lab, remember the first few characters of the output:

  • “rates”:
  • “CAD”
  • “KRW”

Q2.  In the lab you used the data gathered and turned it into a pandas dataframe. The dataframe should have the Currency as the index and Rate as the columns. Make sure to drop unnecessary columns. What is the correct format of the index and column names

Q3. In the lab, you converted the data into a csv, but the data at the endpoint itself is best describes as:

  • JSON
  • CSV
  • table
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This course is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisite courses.

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