Foundations: Data, Data Everywhere Course Challenge Answers (Part 1: Q1–10)
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In this post, I provide accurate answers and detailed explanations for Module 5: *Course challenge*
of Course 1: Foundations: Data, Data, Everywhere – Google Data Analytics Professional Certificate.
Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.
Here, we’ll walk through questions 1 to 10 with detailed explanations to support your learning.
To find answers to the remaining questions, check out the full module breakdown below:
*Course challenge*
Graded Quiz
1. 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).
You know that spreadsheets work well for processing and analyzing a small dataset, like the one you’re using. To get the data from the database into a spreadsheet, what should you do?
- Email a copy of the dataset to your company email address.
- Use Tableau to convert the data into a spreadsheet.
- Copy and paste the data into a spreadsheet.
- Download the data as a .CSV file, then import it into a spreadsheet. ✅
Explanation:
A .CSV (Comma Separated Values) file is a standard format for exporting and importing data between systems. It keeps the structure of rows and columns, making it ideal for spreadsheet tools like Excel or Google Sheets. This is more reliable and professional than copy-pasting or emailing data.
2. 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.”
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 one of the rows. You are unsure of how to proceed, so the best course of action is to ask your supervisor for guidance.
- True ✅
- False
Explanation:
During the “process” step in the data analysis process, data cleaning is crucial. If you find missing or suspicious values and you’re not sure what to do with them, consulting your supervisor is the best approach. Making assumptions can lead to incorrect conclusions.
3. Scenario 1 continued
Once you’ve found the missing information, you analyze your dataset. During analysis, you create a new column F. At the top of the column, you add the attribute Average Percentage of Total Sales - Splashtastic.
Fill in the blank: An attribute is a _______ or quality of data used to label a column.
- number
- characteristic ✅
- headline
- response
Explanation:
In data analysis, an attribute is a feature or quality of data used to describe it. For example, “Average Percentage of Total Sales – Splashtastic” is a characteristic that describes a data column.
4. Scenario 1 continued
Next, you determine the average total daily sales over the past 12 months at all stores. The range that contains these sales is E2:E39. To do this, you use a function. You input =AVERAGE(E2-E39), but this returns an error. What is the correct command?
- =AVERAGE(E2;E39)
- =AVERAGE(E2+E39)
- =AVERAGE(E2:E39) ✅
- =AVERAGE(E2,E39)
Explanation:
To calculate the average in Excel or Google Sheets, the correct syntax is to use a colon : to specify a range (not a hyphen - which would try to subtract values). So, =AVERAGE(E2:E39) computes the average of all cells from E2 through E39.
5. Scenario 1 continued
You’ve reached the share phase of the data analysis process. What can you do in this phase to highlight the Splashtastic sales insights you've discovered?
- Mission statement
- Hypothesis
- Complaint
- Business task ✅
Explanation:
In the “share” phase of data analysis, clearly stating the business task helps stakeholders understand the context and significance of your insights. It gives your data meaning and direction in terms of decision-making.
6. 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, but get an error. What statement will correct the problem?
SELECT *
FROM dental_data_table
WHERE zip code = 81137
- zip_code = 81137
- WHERE zip_code = 81137 ✅
- WHERE 81137
- WHERE_zip code = 81137
Explanation:
In SQL, column names with underscores (e.g., zip_code) should be written as-is. You can’t write zip code with a space unless you use brackets or quotes, which is more advanced. The correct WHERE clause targets the correct column and value.
7. 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.”
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. The fact that the dataset includes people who all live in the same zip code might get in the way of what?
- Fairness ✅
- Spreadsheet formulas or functions
- Data visualization
- Future dental procedures
Explanation:
If your dataset is composed of data from a single zip code, your results may be biased and not representative of the general population. This brings up a fairness issue, especially if you’re using this data to make broader conclusions.
8. 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.
Fill in the blank: Changing the business task involves _____ a new question or problem to be solved.
- recording
- defining ✅
- sharing
- analyzing
Explanation:
When new insights emerge (like older adults missing more appointments), you may need to redefine the business task to better align with real-world needs. In this case, the focus shifts to addressing elderly access to dental care.
9. 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 professionally and help verify the results of data analysis include customers and competitors.
- True
- False ✅
Explanation:
Customers and competitors are not the ones who verify data findings. That’s the job of subject-matter experts (SMEs)—people with deep knowledge of the field (e.g., dentists, healthcare administrators). They help validate your analysis.
10. 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.
Fill in the blank: The type of chart that would be most effective in visualizing this is a _____.
- line chart ✅
- pie chart
- bar chart
- doughnut chart
Explanation:
A line chart is ideal for showing trends over time or across age ranges. Since the data relates to how follow-up attendance changes as patients get older, this type of chart effectively visualizes the relationship between age and behavior.
That’s it for Part 1! Continue your learning journey with the next set of answers.
Next Part: Foundations: Data, Data Everywhere Course Challenge (Part 2: Q11–20)