Introduction to Data Analytics Coursera Quiz Answers – Meta

All Weeks Introduction to Data Analytics Coursera Quiz Answers

This course equips you with a practical understanding and a framework to guide the execution of basic analytics tasks such as pulling, cleaning, manipulating, and analyzing data by introducing you to the OSEMN cycle for analytics projects. You’ll learn to perform data analytics tasks using spreadsheets and SQL queries.

You will also be introduced to using the Python programming language to manipulate datasets as an alternative to spreadsheets. You will learn foundational programming concepts and how they apply to the market. You will also learn how to use Tableau to create data visualizations and dashboards.

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Introduction to Data Analytics Week 01 Quiz Answers

Practice Quiz: Goals and Key Performance Indicators

Q1. Which of the following is true about having access to data? Choose all that apply.

  • It allows you to track key performance indicators.
  • It helps marketers evaluate the success of advertising.
  • It helps to plan and forecast the results of marketing action.
  • It is difficult to come by. 

Q2. Before obtaining data (the “O” in the OSEMN process), which should be the very first step in the cycle?

  • Come up with a marketing plan.
  • Seek out investors.
  • Gather your sample of 1,000 people. 
  • Determine the objective of the analysis.

Q3. What does the R in the SMART acronym stand for?

  • Relevant
  • Renovate
  • Relocate
  • Relax

Q4. True or false: KPIs are measurable values that can help you track your progress towards your goal.

  • False
  • True

Practice Quiz: The OSEMN Process

Q1. Another popular framework for data analytics projects is the Cross Industry Standard Process for Data Mining (CRISP-DM). It has six stages: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation and Deployment. The CRISP-DM phase of Evaluation is likely closest to which stage of the OSEMN framework?

  • Obtaining
  • Scrubbing
  • Exploring
  • Modeling
  • iNterpret

Q2. An online bookstore wants to analyze what book titles they should advertise to drive more sales. They’ve already pulled their historic sales and web traffic data as well as book title details. What is the MOST likely best next step based on the OSEMN framework?

  • Ensure that the data gathered is generally “clean” & well-formatted and make changes as necessary.
  • Look for more data about what past customers loved about their book purchases. Possibly even surveying the customers since the business doesn’t have this information already available.
  • Start creating models that predict the amount of sales if different book titles were promoted.
  • Begin digging into the details of the data and start looking at statistics such as which book genres were most popular.

Q3. Looking at some data about user profiles for a website, we notice that a relatively large portion of the users (25%) have their age listed as over 100 years old. Is this dirty data? (Select the most appropriate answer.)

  • It’s NOT dirty data because people can be over 100 years old.
  • It’s PROBABLY dirty data but we need to know more about the data before deciding what to do with it.
  • It IS dirty data because there’s no way that many users are over 100 so it can’t be accurate.

Graded Quiz: An Analytics Case Study

Q1. Select the best match to complete the following statement:

After modeling, the next stage of the OSEMN process is ________ .

  • Exploring
  • iNterpreting
  • Naming
  • Scrubbing

Q2. Which stage of the OSEMN process works to generate predictions and insights from the data?

  • Exploring
  • Modeling
  • iNterpreting
  • Scrubbing

Q3. An analyst notices that the data they are working with contains many duplicate records. In which stage of the OSEMN process would they fix these duplicate records?

  • iNterpreting
  • Scrubbing
  • Obtaining
  • Exploring

Q4. An analyst has been working with their clients to define business goals and identified questions to answer. They are now ready to dive into the OSEMN process. Which stage of the OSEMN process would likely be the next step for this analyst?

  • Scrubbing
  • Exploring
  • iNterpreting
  • Obtaining

Q5. True or False: Creating visualizations of your data is another way to explore your data.

  • T​rue
  • F​alse

Q6. During the iNterpret stage of the OSEMN framework, you translate your analytical findings back to a business context. Which of the following would you likely do in this stage?

  • Create visualizations to show variable relationships. 
  • Recommend actionable insights to your stakeholders based on what you’ve done during your analysis.
  • Collect more data to check that your analysis is correct.
  • Create a statistical model to make predictions about our data.

Q7. What does the M in the SMART acronym stand for?

  • Measurable
  • Modest
  • Money
  • Meticulous

Q8. KPIs are measurable values that are suggested targets but don’t usually align with your goals.

  • F​alse
  • T​rue

Introduction to Data Analytics Week 02 Quiz Answers

Practice Quiz: Python

Q1. Which are legitimate Python variable names?

  • my_cool_variable
  • 1_is_the_lonliest_number
  • MyCoolVariable
  • in
  • dog_8_my_homework

Q2. Which of the following are built-in types?

  • Boolean
  • Array
  • Float
  • Dictionary
  • Integer
  • Number
  • Text

Q3. What is the output of this code?

for count in range(1,5):
    print(count)
    if count > 1:
        print("Hello")
    elif count <= 3:
        print("Hi")
    else:
        print("*")

print("All Done")
  • 1
  • Hi
  • 2
  • Hi
  • 3
  • Hi
  • 4
  • Hi
  • All Done
  • 1
  • Hi
  • 2
  • Hello
  • 3
  • Hello
  • 4
  • Hello
  • All Done
  • 1
  • Hi
  • 2
  • Hello
  • 3
  • Hello
  • 4
  • Hello
  • 5
  • Hello
  • All Done
  • 1
  • Hi
  • All Done
  • 2
  • Hello
  • All Done
  • 3
  • Hello
  • All Done
  • 4
  • Hello
  • All Done

Q4. Which of the following restrictions do Python dictionaries have?

  • Dictionary values cannot be lists or dictionaries
  • Dictionary values must be unique.
  • Dictionary keys cannot be lists or dictionaries
  • Dictionary keys must be unique.

Q5. What does the following code output?

my_list = [10,30,40]

if my_list[1] < 20:
   print(“first”)
elif len(my_list) == 2:
   print(“second”)
elif my_list[0] + 25 > my_list[1]:
   print(“third”)
elif len(my_list) > 1:
   print(“fourth”)
  • “third”
  • “fourth”
  • “second”
  • “first”

Graded Quiz: Python for Data Analysis

Q1. Your boss gives you a large chunk of data saved in a single string variable. To make it useful, you have to break it up.

Which built-in function would you use to turn a string into a list?

  • listify()
  • sum()
  • split()
  • break()

Q2. Which built-in function would you use to add a list of numbers together and give the result?

  • add()
  • sum()
  • together()
  • total()

Q3. When you put a `return` statement with no value after it, it’ll return the same thing as if you didn’t have a `return` statement. True or False?

  • False
  • True

Q4.

Consider the following code:

If we run the code, what will the output of the above code be?

  • 0
  • 5
  • 10
  • 15

Q5. What would the conditional statement evaluate to? `3 == 3.0`

  • True
  • False

Q6. What would the conditional statement evaluate to? `not (1 != 1)`

  • False
  • True

Q7. Variables in Python can have underscores ( _ ) in the variable names.

  • False
  • True

Q8. You are given a string variable defined like so, `my_str = “5”`. How would you cast (change the variable type) the variable to a number?

  • float(my_str)
  • int(my_str)
  • my_str + 0.0
  • my_str.int()

Introduction to Data Analytics Week 03 Quiz Answers

Practice Quiz: Filtering Data with Google Sheets

Q1. If you have a list of numbers, and you’d like to arrange them from lowest to highest, you would:

  • Filter them
  • Visualize them
  • Sort them
  • All of the above

Q2. True or false: Filtering data in a spreadsheet deletes the information that is no longer shown.

  • F​alse
  • T​rue

Q3. Which function would you use to find the mean of a set of data in a spreadsheet?

  • =AVERAGE()
  • =MEAN()
  • =MEDIAN()

Q4. True or false: Once a spreadsheet has determined an appropriate visualization for your data, you can’t make changes to it.

  • T​rue
  • F​alse

Practice Quiz: SQL Select Statements

Q1. Use the table of advertising expenditures below to answer ALL the questions in this quiz.

Table: AdvertExpend

CompanyQuarterInternetTVPrint
FocusVu13,00050003000
Paola Snaps115005000500
Calla & Ivy125009000400
Inu + Neko120005000700
FocusVu2300070001000
Paola Snaps2150011000600
Calla & Ivy223007000400
Inu + Neko218005000200
FocusVu330005500550
Paola Snaps3500015000400
Calla & Ivy322009000500
Inu + Neko330006000700
FocusVu4350010000900
Paola Snaps4630050001100
Calla & Ivy420008500250
Inu + Neko434008000300

Which query will return the company, internet expenditure, and quarter in descending order of internet expenditure?

Q2. Which of the following queries will return the unique companies found in the table and arrange them in alphabetical order?

Q3. What will the following query have in its return-set?1234SELECT Company, Quarter, TVFROM AdvertExpendORDER BY TV LIMIT 4

  • The return-set will contain the company, quarter, and TV values for the four rows that have the highest values in the TV column.
  • The return-set will have company, quarter, and TV expenditure values for the four rows that have the least TV advertising values.
  • The return-set will contain all the values found in the Company, Quarter, and TV columns.
  • The return-set will contain only the four companies that have spent the least amount of TV advertising.

Practice Quiz: SQL Filtering + Grouping

Q1. For the ALL the following questions use the table of advertising expenditures given below.

Table: AdvertExpend

CompanyQuarterInternetTVPrint
FocusVu13,00050003000
Paola Snaps115005000500
Calla & Ivy125009000400
Inu + Neko120005000700
FocusVu2300070001000
Paola Snaps2150011000600
Calla & Ivy223007000400
Inu + Neko218005000200
FocusVu330005500550
Paola Snaps3500015000400
Calla & Ivy322009000500
Inu + Neko330006000700
FocusVu4350010000900
Paola Snaps4630050001100
Calla & Ivy420008500250
Inu + Neko434008000300

Which query will return the quarters for which Calla & Ivy had a print advertising expenditure less than $400?

  • SELECT Quarter
  • FROM AdvertExpend
  • WHERE Print < 400
  • SELECT Quarter
  • FROM AdvertExpend
  • WHERE Print = 400 AND Company = ‘Calla & Ivy’
  • SELECT Print
  • FROM AdvertExpend
  • WHERE Quarter < 400 AND Company = ‘Calla & Ivy’
  • SELECT Quarter
  • FROM AdvertExpend
  • WHERE Print < 400 AND Company = ‘Calla & Ivy’

Q2. Which query will get us the total TV expenditure in each quarter across all companies?

  • SELECT Quarter, SUM(TV)
  • FROM AdvertExpend
  • GROUP BY TV
  • SELECT Quarter, SUM(TV)
  • FROM AdvertExpend
  • GROUP BY Quarter
  • SELECT Quarter, TV
  • FROM AdvertExpend
  • GROUP BY Quarter
  • SELECT Quarter, MAX(TV)
  • FROM AdvertExpend
  • GROUP BY Quarter

Q3. What will the following query have returned?

SELECT AVG(Internet)

FROM AdvertExpend

WHERE Company = ‘Calla & Ivy’

GROUP BY Quarter

  • The query will return Calla & Ivy’s average internet expenditure for each quarter.
  • The query will return Calla & Ivy’s total internet expenditure for each quarter.
  • The query will return the internet expenditure for an average quarter at Calla & Ivy.
  • The query will return Calla & Ivy’s average internet expenditure across all quarters.

Graded Quiz: Pandas and SQL Assessment

Q1. Say I have the given data loaded as a DataFrame `df` using Pandas. We know that the `customerID` column has a unique value (representing a different customer).

What code would I write to find out how many customers there are in each subscription tier?

  • df.groupby(‘customerID’).count()
  • df.groupby(‘customerID’)[‘subscriptionTier’].count()
  • df[‘customerID’].groupby(‘subscriptionTier’).count()
  • df.groupby(‘subscriptionTier’)[‘customerID’].count()

Q2. You are given the following data loaded as a DataFrame `df` using Pandas. We know that the `customerID` column has a unique value (representing a different customer).

What code would I write to find out how many customers there are in each industry?1 point

len(df[‘industry’])

df[‘industry’].value_counts()

df.value_counts()[‘industry’]

df[‘industry’].values

Q3. You are given the following data loaded as a DataFrame `df` using Pandas. We know that the `customerID` column has a unique value (representing a different customer).

How would you interpret this output?12df[ 'totalCosts' ].quantile(0.8)8.99

  • 20% of the data has a totalCost of exactly 8.99
  • 20% of the data has a totalCost of 8.99 or less
  • 80% of the data has a totalCost of 8.99 or more
  • 80% of the data has a value of 8.99 or less for the totalCost column

Q4. You are given the following data loaded as a DataFrame `df` using Pandas.

What code would you write to get just the single column `totalCost`?

  • df.column[‘totalCost’]
  • df[‘totalCost’]
  • df(‘totalCost’)
  • df.column(‘totalCost’)

Q5. You can find the list of columns with `df.columns`

  • False
  • True

Q6. You are given the following data loaded as a DataFrame `df` using Pandas.

What code would you write to get all the rows where `monthlyBasePrice` is greater than $4?

  • df[‘monthlyBasePrice’].greater_than(4.00)
  • df[‘monthlyBasePrice’] > 4.00
  • df[ df[‘monthlyBasePrice’].greater_than(4.00) ]
  • df[ df[‘monthlyBasePrice’] > 4.00 ]

Q7. You are given the following data loaded as a DataFrame `df` using Pandas.

What code would you write to get all the rows where `industry` starts with the letter “R”?

  • df[‘industry’].str.starts(“R”)
  • df[ df[‘industry’].startswith(“R”) ]
  • df[ df[‘industry’].str.startswith(“R”) ]
  • df[ df[‘industry’].starts(“R”) ]

Q8. Which code would you write to filter a DataFrame `df` so that you only have values that had no duplicate rows?

  • df[ ~df.duplicated(keep=’last’) ]
  • df[ df.duplicated(keep=’False’) ]
  • df[ df.duplicated(keep=’last’) ]
  • df[ ~df.duplicated(keep=False) ]

Introduction to Data Analytics Week 04 Quiz Answers

Practice Quiz: Chart Types and Basic Tableau Graphs

Q1. Which chart type best describes this chart:

  • Line Chart
  • Pie Chart
  • Scatter Plot
  • Bar Chart

Q2. Which type of chart is usually best suited for visualizing the relationship of measurements to another ordered series of data, such as time?

  • A Scatter Plot
  • A Line Chart
  • A Bar Chart
  • A Map

Q3. What does the trend line in this scatter plot suggest about the relationship between the duration of Old Faithful’s eruptions and the wait-time between eruptions?

  • That the average duration of an eruption is approximately 39 minutes
  • That the average wait time between eruptions is approximately 64 minutes
  • The longer you wait between eruptions the longer an eruption is likely to last
  • The longer an eruption lasts the shorter the time you’re likely to have waited between eruptions

Q4. In Tableau, charts are created in a worksheet. Which of the following actions will create a new worksheet?

  • Clicking a “New Worksheet” button at the bottom of an open worksheet screen
  • Connecting to a data source from the Tableau Start Page, when you start a new project
  • Clicking a “New Worksheet” button at the bottom of the Data Source screen
  • A, B, and C, above, are all correct
  • None of the above are correct

Q5. True or false: The order in which you double-click a data field may affect the chart that Tableau generates?

  • True
  • False

Q6. True or false: Tableau can only connect to data from an Excel file or CSV file?

  • True
  • False

Q7. On the Tableau start page, which of the following connection types would you select if you want to connect to data in a CSV file?

  • OData
  •  Microsoft Excel
  • Text file
  • JSON file
  • Spatial file
  • Microsoft Access

Q8. True or false: If you change the name of a column in Tableau’s data preview pane, this will change the data in your Excel or CSV file.

  • True
  • False

Practice Quiz: Building Charts in Tableau II Practice

Q1. Of all the chart-making options we’ve covered, which of these is “easiest” (i.e., requires the least input from you)?

  • The “double-click” method.
  • The “drag-and-drop” method.
  • The “Show Me” tool.

Q2. True or False: you can use the “Show Me” tool even after you’ve created a chart using other methods?

  • True
  • False

Q3. In Tableau, for charts that are drawn on a pair of axes, how do you specify what data you want represented on the vertical axis (or Y-axis)?

  • By dragging the target data field from the Data Pane to the “Row Shelf.”
  • By dragging the target data field from the Data Pane to the “Label” button on the Marks Shelf.
  • By dragging the target data field from the Data Pane to the “Detail” button on the Marks Shelf.
  • By dragging the target data field from the Data Pane to the “Column Shelf.”

Q4. According to the chart above, which U.S. State hired workers for 17,342,587 jobs in 2013?

  • California.
  • Texas.
  • Wyoming.
  • Alaska.

Graded Quiz: Introduction to Data Visualization

Q1. True or False: It’s important to craft a story for your audience to show people how much you know.

  • F​alse
  • T​rue

Q2. True or False: When creating a story around your data for your audience, making the key takeaways clearly identified is one of the most important requirements.

  • T​rue
  • F​alse

Q3. True or False: When strengthening your story, you should include an explanation of the investigation steps and describe the analysis. 

  • F​alse
  • T​rue

Q4. Considering you have qualitative data, select all visualization types that usually do best for this kind of data. (Select all that apply)

  • Pie charts
  • Bar graphs
  • Line plots
  • S​catter plots

Q5. True or False: Swapping colors from their normal usage such as using red for “cold” and blue for “hot” are great ways to keep your audience engaged.

  • F​alse
  • T​rue

Q6. True or False: Colorblindness is so rare that you really don’t need to worry about picking an accessible palette.

  • T​rue
  • F​alse

Q7. From what we discussed, what are the different parts of a story structure that can lead to an audience’s understanding and persuasion? (Select all that apply)

  • Charisma
  • Narrative
  • D​ata
  • Visualization

Q8. True or False: In discussing how to build a good and compelling story, the four parts were the set-up, the build-up, the climax, and the conclusion.

  • F​alse
  • T​rue
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