In data analytics, what term describes a collection of elements that interact with one another

Q3. In the analyze phase of the data life cycle, what might a data analyst do? Select all that apply.

• Use a formula to perform calculations
• Use spreadsheets to aggregate data
• Create a report from the data
• Choose the format of spreadsheet headings

Q4. Describe how the data life cycle differs from data analysis.

• The data life cycle deals with transforming and verifying data; data analysis is using the insights gained from the data.
• The data life cycle deals with the stages that data goes through during its useful life; data analysis is the process of analyzing data.
• The data life cycle deals with making informed decisions; data analysis is using tools to transform data.
• The data life cycle deals with identifying the best data to solve a problem; data analysis is about asking effective questions. 

Q5. A company takes insights provided by its data analytics team, validates them, and finalizes a strategy. They then implement a plan to solve the original business problem. This describes which step of the data analysis process?

• Analyze
• Process
• Share
• Act

Q6. Fill in the blank: Spreadsheets are _____ that can be used to store, organize, and sort data.

• digital worksheets
• formulas and functions
• interactive dashboards
• visual representations

Q7. Fill in the blank: A formula is a set of instructions used to perform a specified calculation; whereas a function is _____.

• a question written by the user
• a predefined operation
• a computer programming language
• a particular value

Q8. Fill in the blank: A query is used to _____ information from a database. Select all that apply.

• update
• request
• retrieve
• analyze 

Q9. Structured query language (SQL) enables data analysts to communicate with a database.

• True
• False

Q10. The graphical representation of information helps stakeholders understand data insights. Formulas and functions make this possible.

• True
• False

Coursera Google Data Analytics Week 4 Quiz Answers!

> Set up your toolbox.

Question 1

Question 1

The column attributes for rank, name, population, and county are located in which row

of the following spreadsheet?

• 11
• 1
• 2
• 10

Question 3

If a data analyst wants to list the cities in this spreadsheet alphabetically, instead of numerically, what feature can they use in column B?

• Organize range
• Sort range
• Name range
• Randomize range

Question 5

In the following query, what is FROM telling the database to do?

• From which field data should be stored
• From which table to select data
• From which filter data should be selected
• From which field data should be updated

Question 7

A data analyst creates the following visualization to clearly demonstrate how much more populous Charlotte is than the next-largest North Carolina city, Raleigh. What type of chart is it?

• A scatter chart
• A pie chart
• A line chart
• A column, or bar, chart 

Question 8

A data analyst wants to demonstrate how the population in Charlotte has increased over time. They create the chart below. What is this type of chart called?

• Column chart
• Area chart
• Line chart
• Bar chart

Coursera Google Data Analytics Week 5 Quiz Answers!

> Endless career possibilities.

Question 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). 

Considering the size of your dataset, what’s the best way to proceed with the process and analyze steps?

• Download the data, then use a spreadsheet to process and analyze it.
• Continue using the company database to process and analyze the data.
• Upload the data, then process and analyze it using Tableau.
• Use SQL to process and analyze the data.

Spreadsheets work well for processing and analyzing a small dataset, such as the one you’re using.

Question 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.” 

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.

Course Challenge Dataset - Scenario 1 - Scenario 1_ Pharmacy Data - Part 1.csv

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. The best course of action is to delete row 16 from your dataset so the missing data doesn’t get in the way of your results.

• True
• False

Question 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: Average Percentage of Total Sales - Splashtastic. In data analytics, this column label is called an attribute.

• True
• False

This column label is an attribute, which is a characteristic or quality of data used to label a column.

Question 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. Fill in the blank to complete the function correctly: =_____ (E2:E39). 

• AVERAGE
• SUM
• SALES
• TOTAL

The function begins with an equal sign (=), then the word AVERAGE. The range is all of column E, represented by E:E.

Question 5

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.
• Prepare a slideshow about Splashtastic’s sales and practice your presentation.
• Stop selling Splashtastic because it doesn't represent a large percentage of total sales.
• Present your findings about Splashtastic to stakeholders.

The share phase involves creating data visualizations, preparing your presentation, and communicating your findings to stakeholders.

Question 6

Scenario 2, Question 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. How do you complete the following query? 

• WHERE_zip_code = 81137
• WHERE = 81137
• WHERE zip_code = 81137
• zip_code = 81137

The correct syntax is WHERE zip_code = 81137. WHERE indicates where to look for information. The column name is zip_code. And the database is being asked to return only records matching zip code 81137.

Question 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.”

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.

Course Challenge Dataset - Scenario 2.csv

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

It’s your responsibility as a data analyst to make sure your analysis is fair. Although many zip codes do reflect diverse populations, a better choice would be to include data about people who live in multiple zip codes.

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

A business task is the question or problem data analysis answers for a business.

Question 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. 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 include customers and competitors.

• True
• False

Question 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. 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 trend of people being less likely to attend follow-up appointments as they get older. Which type of chart will be most effective?

• A pie chart
• A table
• A doughnut chart
• A line chart

A line chart is effective for tracking trends over time, such as people attending fewer follow-up appointments as they get older.