Foundations: Data, Data Everywhere Course Challenge Answers (Part 4: Q31–41)
This is Part 4 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 31 to 41 with detailed explanations to support your learning.
To find answers to the remaining questions, check out the full module breakdown below:
31. Scenario 1 continued
Next, you determine the average total daily sales over the past 12 months at all stores. The entire range of cells that contain these sales are E2:E39. The correct syntax is =AVERAGE(E2:E39).
- True ✅
- False
Explanation:
The syntax =AVERAGE(E2:E39) is correctly used to calculate the average of values in a range of cells.
32. Scenario 1 continued
Next, you create a slideshow, which includes a data visualization to highlight the Splashtastic sales insights you've discovered. You’ve reached which phase of the data analysis process?
- Manage
- Analyze
- Act
- Share ✅
Explanation:
The Share phase involves presenting your analysis results to stakeholders. Creating a slideshow and adding visualizations is part of communicating your findings effectively.
33. 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 write the following query.
SELECT *
FROM dental_data_table
WHERE zip code = 81137
This query is incorrect. How could it be fixed?
- Decapitalize SELECT, FROM, and WHERE
- In line 3, replace zip code with zip_code ✅
- Rewrite line 3 as WHERE_zip code = 81137
- Rewrite line 3 as zip_code = 81137
Explanation:
The WHERE clause should filter by the column zip_code, not the table name.
34. 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, gender, and home address. When examining the geographic data, you notice that all the patients live in the same zip code.
Fill in the blank: The fact that the dataset includes people who all live in the same zip code might get in the way of ______.
- accuracy
- spreadsheet formulas or functions
- fairness ✅
- data visualization
Explanation:
If all data is from one geographic area, it may not represent the broader population. This geographic homogeneity could lead to bias, affecting the fairness of your conclusions.
35. 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 defining the new question or problem to be solved.
- True ✅
- False
Explanation:
Yes, changing the focus of the analysis—such as shifting from general missed appointments to challenges faced by seniors—is redefining the business task, which drives the rest of the data analysis process.
36. 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 _____, offering insights into the business problem, and validating the choices being made.
- creating a presentation with the data
- collecting data relevant to the business problem
- identifying inconsistencies in the analysis ✅
- redefining the business problem
Explanation:
Subject matter experts (SMEs) bring real-world knowledge to interpret data insights and validate findings. They can spot inconsistencies that analysts might miss due to lack of domain-specific context.
37. 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.
For this, a pie chart will be most effective.
- True
- False ✅
Explanation:
A line chart or bar chart would be better for showing trends over time or across age groups. Pie charts are used for showing parts of a whole, not trends or progression.
38. Scenario 1, question 1-5
You’ve just started a new job as a data analyst. You’re working 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).
Considering the size of your dataset, you decide a spreadsheet will be the best tool for your project. You proceed by downloading the data from the database. Describe why this is the best choice.
- Spreadsheets are most effective when working with queries.
- Spreadsheets work well for processing and analyzing a small dataset, like the one you’re using. ✅
- Databases can’t be used for analysis.
- Only spreadsheets let you download and upload data.
Explanation:
Spreadsheets are ideal for small datasets, providing easy tools for analysis and visualization.
39. Scenario 1 continued
You’ve downloaded the data from your company database and imported it into a spreadsheet. IMPORTANT: To answer questions using this dataset for the scenario, click the link below and select the “Use Template” button before answering the questions.
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 for Store Number 15 in Row 16. The best course of action is to delete the row with missing data from your dataset so it doesn’t get in the way of your results.
- True
- False ✅
Explanation:
Instead of deleting, it’s best to investigate or correct missing data. Removing rows can distort results, especially in small datasets. Consider filling in data or marking it appropriately.
40. Scenario 1 continued
You’ve reached the share phase of the data analysis process. It involves which of the following? Select all that apply.
- Create a data visualization to highlight the Splashtastic sales insights you’ve discovered. ✅
- Stop selling Splashtastic because it doesn’t represent a large percentage of total sales.
- Prepare a slideshow about Splashtastic’s sales and practice your presentation. ✅
- Present your findings about Splashtastic to stakeholders. ✅
Explanation:
Sharing involves visualizing data, preparing materials, and presenting findings. The option about stopping sales is an action, not part of sharing.
41. 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, gender, and home address. When examining the geographic data, you notice that all the patients live in the same zip code.
Fill in the blank: The fact that the dataset includes people who all live in the same zip code might get in the way of ______.
- fairness ✅
- data visualization
- accuracy
- spreadsheet formulas or functions
Explanation:
If all data comes from one zip code, the dataset lacks geographic diversity, potentially affecting fairness and generalizability.
Congratulations! You’ve completed all 65 questions. Share this post if it helped you, and check out other Coursera quiz answers below.
Related contents:
Module 1: Introducing data analytics and analytical thinking
Module 2: The wonderful world of data
Module 3: Set up your data analytics toolbox
Module 4: Become a fair and impactful data professional
Module 5: Endless career possibilities
You might also like:
Course 2: Ask Questions to Make Data-Driven Decisions
Course 3: Prepare Data for Exploration
Course 4: Process Data from Dirty to Clean
Course 5: Analyze Data to Answer Questions
Course 6: Share Data Through the Art of Visualization
Course 7: Data Analysis with R Programming
Course 8: Google Data Analytics Capstone: Complete a Case Study