Foundations: Data, Data Everywhere Course Challenge Answers (Part 3: Q21–30)
This is Part 3 of the Module 5 quiz answers for “Foundations: Data, Data Everywhere Course Challenge” from the Google Data Analytics Professional Certificate on Coursera.
Here, we’ll walk through questions 21 to 30 with detailed explanations to support your learning.
To find answers to the remaining questions, check out the full module breakdown below:
21. Scenario 2, questions 6-10
You’ve been working for the nonprofit National Dental Society (NDS) as a junior data analyst for about two months. The mission of the NDS is to help its members advance the oral health of their patients. NDS members include dentists, hygienists, and dental office support staff.
The NDS is passionate about patient health. Part of this involves automatically scheduling follow-up appointments after crown replacement, emergency dental surgery, and extraction procedures. NDS believes the follow-up is an important step to ensure patient recovery and minimize infection.
Unfortunately, many patients don’t show up for these appointments, so the NDS wants to create a campaign to help its members learn how to encourage their patients to take follow-up appointments seriously. If successful, this will help the NDS achieve its mission of advancing the oral health of all patients.
Your supervisor has just sent you an email saying that you’re doing very well on the team, and he wants to give you some additional responsibility. He describes the issue of many missed follow-up appointments. You are tasked with analyzing data about this problem and presenting your findings using data visualizations.
An NDS member with three dental offices in Colorado offers to share its data on missed appointments. So, your supervisor uses a database query to access the dataset from the dental group. The query instructs the database to retrieve all patient information from the member’s three dental offices, located in zip code 81137.
The table is dental_data_table, and the column name is zip_code. You have written the following query, but received an error when it ran.
SELECT *
FROM dental_data_table
WHERE dental_data_table = 81137
Given the objective of the query, where is the mistake in this query?
- In line 3, dental_data_table should be replaced with zip_code. ✅
- SELECT, FROM, and WHERE should not be capitalized.
- The third line should be WHERE = 81137
- In line 2, dental_data_table should be replaced with zip_code 81137.
Explanation:
In a WHERE clause, you’re specifying a condition that filters rows. The query currently says: WHERE dental_data_table = 81137
This is incorrect because you’re comparing a table name to a value. Instead, you should compare a column (zip_code) to the value: WHERE zip_code = 81137
22. Scenario 2 continued
The dataset your supervisor retrieved and imported into a spreadsheet includes a list of patients, their demographic information, dental procedure types, and whether they attended their follow-up appointment. To use the dataset for this scenario, click the link below and select “Use Template.”
Link to template: Course Challenge - Scenario 2
OR
If you don’t have a Google account, you can download the template directly from the attachment below.
The patient demographic information includes data such as age and gender. As you’re learning, it’s your responsibility as a data analyst to make sure your analysis is fair. The fact that the dataset includes people who all live in the same zip code might get in the way of fairness.
- True ✅
- False
Explanation:
If all the data comes from one geographic area, especially a unique one (like a rural area with a large elderly population), your results may not generalize well to broader populations. This can introduce bias into the analysis.
23. Scenario 2 continued
As you’re reviewing the dataset, you notice that there are a disproportionate number of senior citizens. So, you investigate further and find out that this zip code represents a rural community in Colorado with about 800 residents. In addition, there’s a large assisted-living facility in the area. Nearly 300 of the residents in the 81137 zip code live in the facility.
You recognize that’s a sizable number, so you want to find out if age has an effect on a patient’s likelihood to attend a follow-up dental appointment. You analyze the data, and your analysis reveals that older people tend to miss follow-ups more than younger people.
So, you do some research online and discover that people over the age 60 are 50% more likely to miss dentist appointments. Sometimes this is because they’re on a fixed income. Also, many senior citizens lack transportation to get to and from appointments.
With this new knowledge, you write an email to your supervisor expressing your concerns about the dataset. He agrees with your concerns, but he’s also impressed with what you’ve learned and thinks your findings could be very important to the project. He asks you to change the business task. Now, the NDS campaign will be about educating dental offices on the challenges faced by senior citizens and finding ways to help them access quality dental care.
Changing the business task involves which of the following?
- Defining the new question or problem to be solved ✅
- Conducting a gap analysis
- Using a database instead of a spreadsheet
- Creating a graphical representation of the data
Explanation:
Changing the business task involves redefining the problem based on new insights.
24. Scenario 2 continued
You continue with your analysis. In the end, your findings support what you discovered during your online research: As people get older, they’re less likely to attend follow-up dental visits.
But you’re not done yet. You know that data should be combined with human insights in order to lead to true data-driven decision-making. So, your next step is to share this information with people who are familiar with the problem professionally. They’ll help verify the results of your data analysis.
The people who are familiar with a problem and help verify the results of data analysis are called subject-matter experts. What are their roles in the process? Select all that apply.
- Collect, transform, and organize data
- Validate the choices being made ✅
- Offer insights into the business problem ✅
- Identify inconsistencies in the analysis ✅
Explanation:
SMEs bring domain knowledge and help ensure the analysis makes sense in the real world. They don’t usually handle the technical data tasks like transforming or organizing data—that’s the analyst’s job.
25. Scenario 2 continued
The subject-matter experts are impressed by your analysis. The team agrees to move to the next step: data visualization. You know it’s important that stakeholders at NDS can quickly and easily understand that older people are less likely to attend important follow-up dental appointments than younger people. This will help them create an effective campaign for members.
It’s time to create your presentation to stakeholders. It will include a data visualization that demonstrates the lifetime trend of people being less likely to attend follow-up appointments as they get older.
You recognize that this data is given in series. What type of data visualization is most effective to visualize this data?
- A line chart ✅
- A doughnut chart
- A table
- A box plot
Explanation:
Line charts are best when showing continuous data over time or age. They help visualize trends clearly, making them perfect for this kind of insight.
26. Scenario 1 continued
You’ve downloaded the data from your company database and imported it into a spreadsheet. To use the dataset for this scenario, click the link below and select “Use Template.”
Link to template: Course Challenge - Scenario 1
OR
If you don’t have a Google account, you can download the template directly from the attachment below.
Now, it’s time to process the data. As you know, this step involves finding and eliminating errors and inaccuracies that can get in the way of your results. While cleaning the data, you notice that information about Splashtastic is missing in row 16. Which of the following would be an appropriate response?
- Delete the row with the missing data point.
- Ask a colleague on your team how they’ve handled similar issues in the past. ✅
- Replace the row with the average values of the other data points.
- Sort the spreadsheet so the row with missing data is at the bottom.
Explanation:
This ensures consistency in how data is treated across the organization. Automatically deleting or replacing values can cause issues unless you know the standard protocol.
27. Scenario 1 continued
You’ve reached the share phase of the data analysis process. What can you do in this phase to share the Splashtastic sales insights you've discovered?
- Revisit the analyze phase.
- Present your findings to customers.
- Establish a repository for the data.
- Present your findings to stakeholders. ✅
Explanation:
The share phase is where you communicate results to those who will act on them—usually stakeholders, not customers.
28. Scenario 2 continued
The dataset your supervisor retrieved and imported into a spreadsheet includes a list of patients, their demographic information, dental procedure types, and whether they attended their follow-up appointment. To use the dataset for this scenario, click the link below and select “Use Template.”
Link to template: Course Challenge - Scenario 2
OR
If you don’t have a Google account, you can download the template directly from the attachment below.
The patient demographic information includes data such as age and gender. As you’re learning, it’s your responsibility as a data analyst to make sure your analysis is fair. Which aspect of patient demographics might get in the way of fairness?
- The dataset indicates which dental procedure the patients had performed.
- The dataset contains patient identification numbers.
- The dataset includes people who all live in the same zip code. ✅
- The dataset represents people who are single.
Explanation:
Again, this can skew results due to lack of diversity in the dataset (e.g., if it’s a rural or high-senior area), leading to bias in findings.
29. Scenario 2 continued
You continue with your analysis. In the end, your findings support what you discovered during your online research: As people get older, they’re less likely to attend follow-up dental visits.
But you’re not done yet. You know that data should be combined with human insights in order to lead to true data-driven decision-making. So, your next step is to share this information with people who are familiar with the problem professionally. They’ll help verify the results of your data analysis.
Fill in the blank: Subject matter experts are people who are familiar with a problem. They can help by identifying inconsistencies in the analysis, _____, and validating the choices being made.
- offering insights into the business problem ✅
- creating a presentation with the data
- collecting data relevant to the business problem
- redefining the business problem
Explanation:
Subject matter experts provide valuable insights and validate the decisions made based on data analysis.
30. Scenario 1, question 1-5
You’ve just started a new job as a data analyst for a midsized pharmacy chain with 38 stores in the American Southwest. Your supervisor shares a new data analysis project with you.
She explains that the pharmacy is considering discontinuing a bubble bath product called Splashtastic. Your supervisor wants you to analyze sales data and determine what percentage of each store’s total daily sales come from that product. Then, you’ll present your findings to leadership.
You know that it's important to follow each step of the data analysis process: ask, prepare, process, analyze, share, and act. So, you begin by defining the problem and making sure you fully understand stakeholder expectations.
One of the questions you ask is where to find the dataset you’ll be working with. Your supervisor explains that the company database has all the information you need.
Next, you continue to the prepare step. You access the database and write a query to retrieve data about Splashtastic. You notice that there are only 38 rows of data, representing the company’s 38 stores. In addition, your dataset contains five columns: Store Number, Average Daily Customers, Average Daily Splashtastic Sales (Units), Average Daily Splashtastic Sales (Dollars), and Average Total Daily Sales (All Products). You decide to use a spreadsheet to work with the data because you know that spreadsheets work well for processing and analyzing a small dataset, like the one you’re using.
Fill in the blank: To get the data from the database into a spreadsheet, you would first _____ the data as a .CSV file, then import it into a spreadsheet.
- download ✅
- copy and paste
Explanation:
The standard method for exporting data from a database is to download it as a CSV file, which can then be opened in tools like Excel or Google Sheets.
Hope this helped! Use the buttons below to move to the previous or next part.