introduction to data analytics coursera quiz answers week 3

Gathering Data

Practice Quiz

1. What are the requirements in order for data to be reliable? (Select all that apply)

  • Data should be relevant 

  • Data should be structured
  • Data should be free of all errors

  • Data should be easy to collect 

 

2. What type of data is produced by wearable devices, smart buildings, and medical devices?

  • Census data
  • Survey data
  • Sensor data
  • Observation study data

3. What type of data is semi-structured and has some organizational properties but not a rigid schema?

  • Data from OLTP systems
  • Web logs
  • Online forms 
  • Emails

Graded Quiz

4. What are some of the steps in the process of “Identifying Data”? (Select all that apply)

  • Define the checkpoints 
  • Define a plan for collecting data 

  • Determine the visualization tools that you will use
  • Determine the information you want to collect 

5. What type of data refers to information obtained directly from the source?

  • Sensor data 
  • Secondary data
  • Primary data
  • Third-party data 

6. Web scraping is used to extract what type of data?

  • Text, videos, and images
  • Text, videos, and data from relational databases
  • Data from news sites and NoSQL databases
  • Images, videos, and data from NoSQL databases

7. Data obtained from an organization’s internal CRM, HR, and workflow applications is classified as:

  • Secondary data
  • Third-party data
  • Primary data
  • Copyright-free data

8. Which of the provided options offers simple commands to specify what is to be retrieved from a relational database?

  • Web Scraping
  • SQL
  • API
  • RSS Feed

Wrangling Data

Practice Quiz

9. What is one of the common structural transformations used for combining data from one or more tables?

  • Cleaning
  • Joins
  • Denormalization
  • Normalization 

10. What tool allows you to discover, cleanse, and transform data with built-in operations?

  • Google DataPrep
  • OpenRefine
  • Trifacta Wrangler
  • Watson Studio Refinery

11. What is data called that does not fit within the context of the use case?

  • Duplicate data
  • Relevant data
  • Irrelevant data 
  • Missing data 

Graded Quiz

12. What does a typical data wrangling workflow include?

  • Recognizing patterns
  • Using mathematical techniques to identify correlations in data
  • Predicting probabilities
  • Validating the quality of the transformed data 

13. OpenRefine is an open-source tool that allows you to:

  • Use add-ins such as Microsoft Power Query to identify issues and clean data
  • Enforces applicable data governance policies automatically
  • Automatically detect schemas, data types, and anomalies
  • Transform data into a variety of formats such as TSV, CSV, XLS, XML, and JSON

14. What is one of the steps in a typical data cleaning workflow?

  • Clustering data
  • Establishing relationships between data events
  • Inspecting data to detect issues and errors
  • Building classification models

15. When you’re combining rows of data from multiple source tables into a single table, what kind of data transformation are you performing?

  • Joins
  • Unions
  • Normalization
  • Denormalization

16. When you detect a value in your data set that is vastly different from other observations in the same data set, what would you report that as?

  • Missing value
  • Syntax error
  • Irrelevant data
  • Outlier

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