Web analytics tools can help an analyst learn: (Choose all that apply)
- The name and address of visitors to a site
- How they arrived at a site
- Information about visitors to a site
- How many visitors went to a site
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Meta Marketing Analytics Professional Certificate Answers (Coursera)
Developed by marketing analytics experts at Aptly together with Meta marketers, the industry-relevant curriculum is designed to prepare you for jobs that include Marketing Analyst, Marketing Researcher, and more.
You’ll learn basic marketing principles, how data informs marketing decisions, and how you can apply the OSEMN data analysis framework to approach common analytics questions. You’ll learn how to use essential tools like Python and SQL to gather, connect, and analyze relevant data. Plus, common statistical methods used to segment audiences, evaluate campaign results, optimize the marketing mix, and evaluate sales funnels.
Along the way, you'll learn to visualize data using Tableau and how to use Meta Ads Manager to create campaigns, evaluate results, and run experiments to optimize your campaigns. You'll also get to practice your new skills through hands-on, industry-relevant projects.
This certification is part of Meta Career Programmes
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Courses:
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Learners who pass all 5 courses will receive a one-time voucher to take the 200-101: Meta Certified Marketing Science Professional exam at no cost. You are required to take this exam in order to complete the Meta Marketing Analyst Certificate Program and earn your Coursera certificate.
Questions:
PRACTICE QUIZ: BASIC PRINCIPLES OF MARKETING
PRACTICE QUIZ: USES OF ANALYTICS IN MARKETING
META MARKETING ANLYTICS COURSERA ANSWERS AND STUDY GUIDE
GRADED QUIZ: USE CASES FOR DATA IN MARKETING
1.When segmenting data collected about people, it’s best…
5.When optimizing a marketing campaign, it’s a good idea to…
PRACTICE QUIZ: UNDERSTANDING DATA MARKETERS USE
PRACTICE QUIZ: DATA SOURCES
GRADED QUIZ: DATA SOURCES
True or false: In a spreadsheet cell, you can only include numbers and letters.
A Global Site Tag is a piece of code provided by what analytics tools?
True or false: There is only a single web analytics tool available- Google Analytics.
PRACTICE ACTIVITY: GOOGLE SHEETS SIMULATION
PRACTICE QUIZ: ANALYZING AND CATEGORIZING DATA
If the above is written in a calculated cell, the cell will display:
PRACTICE ACTIVITY: GOOGLE ANALYTICS SIMULATION 1
Which of the following product groupings list the four top sellers?
PRACTICE ACTIVITY: GOOGLE ANALYTICS SIMULATION 2
Once again you can use Google Analytics to understand your customer’s behavior.
Click here to go to the Google Analytics simulation
PRACTICE QUIZ: GOOGLE ANALYTICS AND WEB MEASUREMENT
PRACTICE ACTIVITY: FACEBOOK ADS MANAGER SIMULATION
Click here to go to the Facebook Ads Manager simulation
How many people subscribed as a result of the entire campaign?
How many impressions did the campaign deliver in the last 30 days?
What was the CPM over the past 30 days for the campaign?
PRACTICE ACTIVITY: GOOGLE ADS REPORT
Which keyword led to the most conversions?
For which keyword was the interaction rate highest?
What was the lowest cost per conversion?
PRACTICE QUIZ: FACEBOOK ADS MANAGER AND GOOGLE ADS
GRADED QUIZ: MARKETING MEASUREMENT AND ANALYTICS TOOLS
In addition to the settings on a device, some platform specific privacy settings can include:
True or false: The most important person in the advertising ecosystem is the advertiser
Second party data is:
PRACTICE QUIZ: CONSUMERS AND DATA
PRACTICE QUIZ: ADVERTISER ECOSYSTEM
GRADED QUIZ: DATA AND PRIVACY
The stage of the OSEMN framework which transforms dirty data to clean data is ________.
What might need to be done to the data below before conducting further analysis?
User_ID | Purchase_ID | Purchase_Date | Purchase_Amount |
28947587 | 294438967 | 05/24/20 | $84.95 |
94387957 | 598034750 | December 9th 2020 | $77.87 |
28947587 | 294438967 | 24th May 2020 | $84.95 |
38297405 | 293875999 | 1-2-21 | $115.00 |
During the Obtain step of the OSEMN framework, you would collect data for the next steps. What do we call data that has been collected by a company directly from its audience?
PRACTICE QUIZ: GOALS AND KEY PERFORMANCE INDICATORS
PRACTICE QUIZ: THE OSEMN PROCESS
GRADED QUIZ: AN ANALYTICS CASE STUDY
After modeling, the next stage of the OSEMN process is ________ .
GRADED QUIZ: LOADING AND RUNNING A PYTHON PROGRAM
PRACTICE QUIZ: PYTHON
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”)
for count in range(1,5):
print(count)
if count > 1:
print("Hello")
elif count <= 3:
print("Hi")
else:
print("*")
print("All Done")
GRADED QUIZ: PYTHON FOR DATA ANALYSIS
total = 0
for n in range(5):
total = total + n
print(total)
If we run the code, what will the output of the above code be?
Which built-in function would you use to look up the definition of the other function?
Given the following variable, what will my_list[2] return?
total = 0
for v in range(4):
total = total + v
print(total)
If we run the program, what will the output of the above code be?
def doubler(n):
result = n * 2
return result
In the code above, the “result” variable is a global variable.
PRACTICE QUIZ: FILTERING DATA WITH GOOGLE SHEETS
PRACTICE QUIZ: SQL SELECT STATEMENTS
Company Quarter Internet TV Print
FocusVu 1 3,000 5000 3000 Paola Snaps 1 1500 5000 500 Calla & Ivy 1 2500 9000 400 Inu + Neko 1 2000 5000 700 FocusVu 2 3000 7000 1000 Paola Snaps 2 1500 11000 600 Calla & Ivy 2 2300 7000 400 Inu + Neko 2 1800 5000 200 FocusVu 3 3000 5500 550 Paola Snaps 3 5000 15000 400 Calla & Ivy 3 2200 9000 500 Inu + Neko 3 3000 6000 700 FocusVu 4 3500 10000 900 Paola Snaps 4 6300 5000 1100 Calla & Ivy 4 2000 8500 250 Inu + Neko 4 3400 8000 300
AdvertExpend Table
Which query will return the company, internet expenditure, and quarter in descending order of internet expenditure?
SELECT Company, Internet
FROM AdvertExpend
ORDER BY Internet DESC
SELECT Company, Internet, Quarter
FROM AdvertExpend
ORDER BY Quarter DESC
SELECT Company, Internet, Quarter
FROM AdvertExpend
SELECT Company, Internet, Quarter
FROM AdvertExpend
ORDER BY Internet DESC
SELECT Company
FROM AdvertExpend
ORDER BY Company
SELECT DISTINCT(Company)
FROM AdvertExpend
ORDER BY Company DESC
SELECT DISTINCT(Company)
FROM AdvertExpend
SELECT DISTINCT(Company)
FROM AdvertExpend
ORDER BY Company
SELECT Company, Quarter, TV
FROM AdvertExpend
ORDER BY TV
LIMIT 4
The return-set will contain only the four companies that have spent the least amount of TV advertising.
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 the company, quarter, and TV values for the four rows that have the highest values in the TV column.
PRACTICE QUIZ: SQL FILTERING + GROUPING
Company Quarter Internet TV Print
FocusVu 1 3,000 5000 3000 Paola Snaps 1 1500 5000 500 Calla & Ivy 1 2500 9000 400 Inu + Neko 1 2000 5000 700 FocusVu 2 3000 7000 1000 Paola Snaps 2 1500 11000 600 Calla & Ivy 2 2300 7000 400 Inu + Neko 2 1800 5000 200 FocusVu 3 3000 5500 550 Paola Snaps 3 5000 15000 400 Calla & Ivy 3 2200 9000 500 Inu + Neko 3 3000 6000 700 FocusVu 4 3500 10000 900 Paola Snaps 4 6300 5000 1100 Calla & Ivy 4 2000 8500 250 Inu + Neko 4 3400 8000 300
AdvertExpend Table
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 AND Company = ‘Calla & Ivy’
SELECT Quarter
FROM AdvertExpend
WHERE Print < 400
SELECT Print
FROM AdvertExpend
WHERE Quarter < 400 AND Company = ‘Calla & Ivy’
SELECT Quarter
FROM AdvertExpend
WHERE Print < 400 AND Company = ‘Calla & Ivy’
SELECT Quarter, MAX(TV)
FROM AdvertExpend
GROUP BY Quarter
SELECT Quarter, TV
FROM AdvertExpend
GROUP BY Quarter
SELECT Quarter, SUM(TV)
FROM AdvertExpend
GROUP BY TV
SELECT Quarter, SUM(TV)
FROM AdvertExpend
GROUP BY Quarter
SELECT AVG(Internet)
FROM AdvertExpend
WHERE Company = ‘Calla & Ivy’
GROUP BY Quarter
The query will return Calla & Ivy’s average internet expenditure across all quarters.
The query will return the internet expenditure for an average quarter at Calla & Ivy.
The query will return Calla & Ivy’s total internet expenditure for each quarter.
The query will return Calla & Ivy’s average internet expenditure for each quarter.
PRACTICE QUIZ: FINDING AND REMOVING DUPLICATE RECORDS
Column 1 Column 2 Column 3 Column 4 0 5.92 heating 1418 twins 1 2.48 santa cruz 1171 enrollment 2 5.23 ought 1616 pork 3 3.13 emily 9442 corey 4 2.20 rising 4620 berry
What code would get you just the data for `Column 2`?
GRADED QUIZ: PANDAS AND SQL ASSESSMENT
Meta Marketing Analytics – Course 2 Week 3 (Image 1)
What code would you write to get all the rows where `subscriptionTier` has the word “Free”?
Meta Marketing Analytics – Course 2 Week 3 (Image 2)
What code would I write to find out how many customers there are in each industry?
Meta Marketing Analytics – Course 2 Week 3 (Image 3)
What code would you write to get all the rows where `industry` starts with the letters “Re”?
Meta Marketing Analytics – Course 2 Week 3 (Image 4)
What code would I write to find out how many customers there are that have the security add on?
Meta Marketing Analytics – Course 2 Week 3 (Image 5)
How would you interpret this output?
df[ 'totalCosts' ].quantile(0.6)
4.99
60% of the data has a totalCost of 4.99 or more
60% of the data has a value of 4.99 or less for the totalCost column
60% of the data has a totalCost of exactly 4.99
40% of the data has a totalCost of 4.99 or less
Meta Marketing Analytics – Course 2 Week 3 (Image 6)
What code would you write to get just the single column `totalCost`?
Meta Marketing Analytics – Course 2 Week 3 (Image 7)
What code would I write to find out how many customers there are in each subscription tier?
Meta Marketing Analytics – Course 2 Week 3 (Image 8)
df[ 'totalCosts' ].quantile(0.8)
8.99
80% of the data has a value of 8.99 or less for the totalCost column
20% of the data has a totalCost of exactly 8.99
80% of the data has a totalCost of 8.99 or more
20% of the data has a totalCost of 8.99 or less
Meta Marketing Analytics – Course 2 Week 3 (Image 9)
What code would you write to get all the rows where `industry` starts with the letter “R”?
Meta Marketing Analytics – Course 2 Week 3 (Image 10)
What code would I write to find out how many customers there are in each subscription tier?
Meta Marketing Analytics – Course 2 Week 3 (Image 11)
What code would you write to get all the rows where `totalCost` is less than $6?
SELECT DISTINCT company
FROM advert
ORDER BY company DESC
LIMIT 5
Meta Marketing Analytics – Course 2 Week 3 (Image 12)
What are the resulting data for the SQL query:
SELECT company, quarter, internet
FROM adverts
WHERE internet >= 500
1 df['totalCosts'].quantile(.75)
2 7.99
25% of the data has a totalCosts of 7.99 or less.
75% of the data has a totalCost of 7.99 or more.
25% of the data has a totalCosts of 7.99 or more.
75% of the data has a value of 7.99 or less for the totalCosts column.
PRACTICE QUIZ: CHART TYPES AND BASIC TABLEAU GRAPHS
Meta Marketing Analytics – Course 2 Week 4 (Image 1)
.
PRACTICE QUIZ: BUILDING CHARTS IN TABLEAU II PRACTICE
2.
Meta Marketing Analytics – Course 2 Week 4 (Image 2)
According to the chart above, which U.S. State hired workers for 17,342,587 jobs in 2013?
Meta Marketing Analytics – Course 2 Week 4 (Image 2)
In the map from the chart above, California is the state with the darkest green color and, therefore, the highest number of jobs.
EXEMPLAR: TABLEAU INTERACTIVITY PRACTICE
Here’s a quick test to see if your interactive elements are working properly. Using your Date filter and the various highlighters, find out what the “Avg. Percent of Usage” was for the “SymbianOS” operating system on 8/1/2013. What answer do you get?
GRADED QUIZ: INTRODUCTION TO DATA VISUALIZATION
In a scatter plot each measurement is represented as an individual point in a Cartesian coordinate grid. This type of chart can be used to graph any pair of numerical data, one variable on each axis, to look for a relationship.
Which of the following statements is true about spreadsheets?
In which of the following scenarios would you use spreadsheets to analyze data?
What is one of the limitations of spreadsheets in data analysis? Select all that apply.
In which scenario would a data analyst choose to use spreadsheets during the Obtain stage of data analysis?
A data analyst obtains a dataset with customer information. They notice there are multiple entries with the same email addresses. What functionality do spreadsheets offer that will help them resolve this issue?
A data analyst receives a dataset containing customer purchase history. How do spreadsheets help them explore this data and gain insights into customer behavior?
Which of the following tasks require specialized tools like Python for modeling in data analysis?
In which of the following scenarios would it be beneficial to use a powerful programming language like Python instead of spreadsheets?
When working with data in Google Sheets, you begin by uploading the data. How do you upload the data?
What conditional filters can you apply when working with data in Google Sheets? Select all that apply.
What is the purpose of formatting data in spreadsheets?
A data analyst uses a spreadsheet to track monthly sales data and aims to determine the average monthly sales to assess the team's performance. Why would the analyst use functions to determine the average monthly values?
What are the formulas for calculating the highest and lowest values in a range in Google Sheets?
A data analyst aims to determine the relationship between a company's promotions and sales. What formula should they use to determine the correlation between the two data points?
A data analyst aims to determine dietary habits by analyzing daily caloric intake across five food groups. What chart should the analyst use to show how much of a whole each food group represents?
Which of the following relationships describes a negative correlation?
Fill in the blank: _____ is a method by which a set of inputs and related outputs are provided to a computer system, and this process is referred to as training.
What would you use a linear regression model for?
What term describes the process of extracting and shaping data to answer specific questions?
What is one of the benefits of learning a new programming language like SQL?
True or False: You can use SQL to select specific data and perform calculations, like finding a dataset's maximum or minimum values.
Which of the following parts are required when using the QUERY function? Select all that apply.
A data analyst working in Google Sheets writes the statement "SELECT A, C." What does this query intend to do?
What are non-numeric values called?
True or False: The ORDER BY clause enables you to include in your query how many resulting rows you want or the number of resulting rows up to a limit.
When working in Google Sheets, how would you modify your query to calculate the sum of values from columns D and E?
You are working with a data set containing a company's sales figures, including columns for item costs (D) and quantity sold (E). Which of the following queries will calculate the average item cost?
Which aggregation functions are commonly used with the GROUP BY clause?
Which of the following data analysis tasks can you complete in Google Sheets or Excel? Select all that apply.
Which of the following statements accurately describes the roles of visualization tools in data analysis?
What types of charts are typically used to show percentages in different groups?
In which of the following scenarios would you use a pie chart?
What are trend charts commonly used for in data analysis?
Fill in the blank: In data analysis, _____ are used to visually represent data values as dots, and each dot represents two variables.
What happens when you connect your data to your source file in Tableau?
After linking your Excel data in Tableau Public, how do you connect the specific data you want to work with?
When would you use Tableau for data visualization instead of spreadsheets?
You’re creating a scatter plot in Google Sheets. After setting the horizontal and vertical titles for the chart, you add a trendline. What steps did you follow to add this trendline?
Which of the following statements is true about data visualization?
What type of chart best represents the change of data trends over time?
A data analyst compares a company's recruitment efforts across four channels: job boards, referrals, career fairs, and online applications. What type of chart will help them complete this task?
In which of the following scenarios would you use a scatter plot to visualize your data?
Which of the following statements is true about Tableau Public?
When connecting to a CSV file in Tableau, which file option should you select?
True or False: Tableau Public and spreadsheets offer identical capabilities, and the choice between them depends on your preference.
Which of the following statements accurately describes dashboards in data analysis?
What should you do before designing a dashboard?
A data analyst is designing a dashboard to show how production costs have affected profitability over the last five years. They aim to keep the dashboard simple and easy to understand. Which of the following best practices should they consider to simplify the dashboard?
A data analyst connects an Excel workbook to a new Tableau project to display their findings on sustainability standards. What is the next step in creating their dashboard?
In Tableau, what is the purpose of the Filtering Action?
Which of the following actions helps make dashboards in Tableau interactive?
You are creating a presentation to communicate the findings of your analysis. You gather your data and the charts and graphs showing the main patterns and trends. What else do you need to create a compelling data story?
Which of the following is a common color-related issue which you should avoid when working data visualizations?
You’ve gathered the findings of your analysis and are putting together a presentation. How should you start your story?
Which of the following best practices should you consider when designing a dashboard?
Which of the following is something you would likely produce in the Explore step of the OSEMN process?
A data analyst is labeling containers of stored information. What is the name for these labels?
What type of variable is the following?
inches_of_rain = 5.02
Given the following code snippet, how can we “cast” or change the data type of the string value to a float?
math_grade = “100”
A data analyst is working with a list of store locations in Python. Examine this code and select the expected output:
store_locations = ['ATL', 'AUS', 'CHI', 'NYC', 'SFO']
store_locations[2]
A data analyst is working with a list of ice cream flavors in Python. Examine this code and select the expected output:
ic_flavors = ['ChocChip', 'Van', 'Choc', 'MintChip', 'Straw']
ic_flavors[0:3]
A data analyst is working with a dictionary of dog breed sizes in Python. Examine this code and select the expected output:
dog_size = { 'lab': 'medium', 'terrier': 'small', 'great dane': 'large', 'chihuahua': 'xsmall' }
dog_size['great dane']
A data analyst is working with a list of the most popular items at a restaurant in Python. Examine this code and select the expected output:
pop_items = ['hot dog', 'hamburger', 'pizza', 'calzone', 'salad', 'water']
'soda' in pop_items
You are working on a data analysis project for an airline. You’ve decided to use if, elif, and else clauses to sort passengers by how often they fly. You have written the following code:
annual_flights = 8
if annual_flights >= 10:
print('Frequent Flier')
elif annual_flights >= 5:
print('Occasional Flier')
print('Standard Flier')
What would you expect the output of this code to be?
When working with functions in Python, what term describes the actual data given to a function via its parameters?
A data analyst is going to use the library addup and wants to give the library the alias au. Which of the following pieces of code should the analyst use?
A data analyst is using Pandas to analyze recent trends in their social media campaigns. They have recent data saved in a CSV named “smcdata”. Which of the following pieces of code would allow them to import this data?
If you want to preview the first five rows of a DataFrame assigned to the variable df, which of the following methods should you use?
When using Pandas, what is a series object?
You want to create a mask that identifies a subset of customers who visit your store at least twice a week. The DataFrame is assigned the variable df and the applicable information is stored in the weeklyvisits column. Which of the following masks should you use?
When scrubbing data in Python, what is one example of an issue that will likely require additional context before fixing?
You are scrubbing a DataFrame (df) and need to remove an unnecessary column called date_of_sale_copy. Which of the following methods can you use?
You are scrubbing a DataFrame and want to find exact copies of rows in your dataset. Which of the following methods can you use?
You are scrubbing a DataFrame and want to find missing values in your dataset. Which of the following methods can you use?
You are scrubbing a DataFrame (df) and notice that a number of age values for subscribers are negative. You know from context that people’s ages should be all positive integers and these instances are typos. You want to replace negative values in df.subscribers with positive values in your dataset. Which of the following pieces of code will change those negatives to positives?
df.ages[negative_ages] = -1 * df.ages[negative_ages]
df.ages[negative_ages] = invert[df.ages[negative_ages]
df.subscribers[negative_ages] = -1 * df.ages[negative_ages]
df.ages[negative_ages] = pos df.ages[negative_ages]
Which of the following tools cannot be used to create visualizations like graphs and charts?
Which method is used to view a specified number of rows from the end of a Pandas dataFrame?
You are given a DataFrame stored in a variable, df, and it includes a column of sales_total. How can you filter the DataFrame to only show the rows that have a sales total of $1000 or more?
You are given a DataFrame stored in a variable, df. You want to create a boxplot of only the column with the name ‘petal_width’. Which of the following code snippets in Pandas would NOT be a viable option to achieve this?
Which type of exploratory visualization can be used to compare how one variable goes up or down compared to another?
Which type of chart should you use to compare the relationship between two variables, but you don't want the data points to seem connected?
Which of the following is an example of a Python aggregation function?
Which method in Python allows us to “split” the data by values in a column then aggregate the information based on those values?
Which method is used to set the style in the Seaborn visualization library?
Which code snippet correctly calls for a violin plot by using the Seaborn visualization library as ‘sns’ and Pandas dataframe as ‘df’?
Matplotlib and Seaborn build on your data to create which kind of visualizations?
Which method is used to view a general breakdown of data including mean, standard deviation, min, max, and quartiles?
You are given a DataFrame stored in a variable, df, and it includes a column of sales_total. How can you filter the DataFrame to only show the rows that have a sales total of $1000 or more?
You are given a DataFrame stored in a variable, df. How can you use Pandas to create a box plot of the numerical columns?
Which type of exploratory chart can be used to illustrate the percentage of voters by age in a local election?
When reviewing both the distribution and magnitude of a particular variable, which of the following chart types would work best?
Which of the following code snippets correctly uses the groupby() method to compare the average heights of male and female giraffes with columns ‘sex’ and ‘height_cm’?
The Seaborn method set() without any parameters is used to do which of the following?
What is the required first parameter when building a Seaborn visualization using a method like boxplot()?
Question 1
Remove all rows that have are missing either the Product_Name or the Product_Category. Assign the cleaned DataFrame to the variable df (overwriting the original DataFrame.).
df = df.dropna(subset=['Product_Name', 'Product_Category'])
Question 2
Find any clearly "incorrect" values in the Price column and "clean" the DataFrame to address those values.
Ensure you make the changes to the DataFrame assigned to the variable df.
# Convert all non-numeric values to NaN
df['Price'] = pd.to_numeric(df['Price'], errors='coerce')
# Remove rows with negative or zero values
df = df[df['Price'] > 0]
# You might also want to remove extremely high values, for example, anything above $10,000
df = df[df['Price'] <= 10000]
Question 3
After you've done the cleaning above, remove any column that has more than 500 missing values.
Ensure you make the changes to the DataFrame assigned to the variable df.
df = df.dropna(thresh=len(df) - 500, axis=1)
Question 4
Address the other missing values. You can replace the values or remvove them, but whatever method you decide to clean the DataFrame, you should no longer have any missing values.
Ensure you make the changes to the DataFrame assigned to the variable df.
# For numerical columns
for col in df.select_dtypes(include=['float64', 'int64']).columns:
df[col] = df[col].fillna(df[col].mean())
# For categorical columns
for col in df.select_dtypes(include=['object']).columns:
df[col] = df[col].fillna(df[col].mode()[0])
Question 5
Create a Subtotal column by multiplying the Price and Quantity values. This represents how much was spent for a given transaction (row).
df['Subtotal'] = df['Price'] * df['Quantity']
Question 6
Determine most common category (Product_Category) purchases (number of total items) for both Product_Line categories. Assign the (string) name of these categories to their respective variables common_category_cat & common_category_dog.
# Assuming 'df' is your DataFrame and it has columns 'Product_Line' and 'Product_Category'
grouped = df.groupby('Product_Line')['Product_Category'].value_counts()
common_category_cat = grouped['cat'].idxmax()
common_category_dog = grouped['dog'].idxmax()
Question 7
Determine which categories (Product_Category), by Product_Line have the median highest Price. Assign the (string) name of these categories to their respective variables priciest_category_cat & priciest_category_dog.
# Assuming 'df' is your DataFrame and it has columns 'Product_Line', 'Product_Category' and 'Price'
grouped = df.groupby(['Product_Line', 'Product_Category'])['Price'].median()
priciest_category_cat = grouped['cat'].idxmax()
priciest_category_dog = grouped['dog'].idxmax()
Question 8
You want to emphasize to your stakeholders that the total number of product categories sold differ between the two Product_Line categories ('cat' & 'dog').
Create a horizontal bar plot that has Product_Category on the y-axis and the total number of that category sold (using the Quantity) by each Product_Line category. Also change the axis labels to something meaningful and add a title.
You will likely want to use Seaborn. Make sure you set the result to the variable ax like the following:
ax = # code to create a bar plot
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming 'df' is your DataFrame and it has columns 'Product_Line', 'Product_Category' and 'Quantity'
grouped = df.groupby(['Product_Line', 'Product_Category'])['Quantity'].sum().reset_index()
plt.figure(figsize=(10, 8))
ax = sns.barplot(x='Quantity', y='Product_Category', hue='Product_Line', data=grouped)
ax.set_xlabel('Total Quantity Sold')
ax.set_ylabel('Product Category')
plt.title('Total Quantity of Product Categories Sold by Product Line')
plt.show()
Question 9
Based on the plot from Question 8, what would you conclude for your stakeholders about what products they should sell? What would be the considerations and/or caveats you'd communicate to your stakeholders?
Write at least a couple sentences of your thoughts in a string assigned to the variable answer_to_9.
The cell below should look something like this:
answer_to_9 = '''
I think that based on the visualization that ****.
Therefore I would communicate with the stakeholders that ****
'''
answer_to_9 = '''
Based on the visualization, it appears that certain product categories are more popular in each product line. For the 'cat' product line, the most popular categories are X, Y, and Z, while for the 'dog' product line, the most popular categories are A, B, and C.
Therefore, I would communicate to the stakeholders that focusing on these popular categories could potentially increase sales. However, it's important to consider that this data only shows the quantity of products sold, not the profit generated from each sale. It's possible that less popular categories could be more profitable due to higher prices or lower costs.
Additionally, this data is historical and may not accurately predict future trends. Market conditions, customer preferences, and other factors can change over time. Regularly reviewing sales data and adjusting the product strategy accordingly would be a prudent approach.
'''
Question 10
The plot you created for Question 8 is good but could be modified to emphasize which products are important for the business.
Create an explanatory visualization that emphasizes the insight you about the product category. This would be a visualization you'd share with the business stakeholders.
Make sure you set the result to the variable ax like the following:
ax = # code to create explanatory visualization
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# Assuming df is your DataFrame and it has columns 'Product' and 'Sales'
df = pd.DataFrame({
'Product': ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'],
'Sales': [1000, 2000, 3000, 4000, 5000]
})
# Sort the products by sales
df_sorted = df.sort_values(by='Sales', ascending=False)
# Create a bar plot
plt.figure(figsize=(10, 6))
ax = sns.barplot(x='Sales', y='Product', data=df_sorted, palette='viridis')
# Set labels
ax.set_xlabel('Sales')
ax.set_ylabel('Product')
ax.set_title('Sales by Product')
plt.show()
Which phase of the OSEMN framework uses the data we have collected to try to consider what might happen next?
What type of models help identify groups of data that are similar but don't rely on pre-defined groups?
Which of these Python libraries uses machine learning to create models?
Which of the following best describes the prediction in a regression analysis?
Which of the following is most likely to cause your model to fail?
After sharing your findings with stakeholders what should you do?
Your goal is to determine whether your new customer falls into the “casual shopper”, “experienced browser”, or “high roller” category. Which type of model should you use?
Which one of the following describes a difference between exploratory and explanatory visualizations?
You want to better understand your data for a classification model with a 0.60 mean accuracy score. What should you do?
Which scenario best represents when you should use an explanatory visualization?
The formula for finding the z-score for a data value is z = (value – mean)/std.
What is the formula for finding the mean of a dataset in a spreadsheet? (Assume that the data is in the cells A1 to A100.)
What is the formula for finding the range of a dataset?
What is the mode for the following set of numbers? 5, -1, 3, 8, 2, 1, 0
What is the median for the given dataset? 1, 3, 7, 5, 3
What is the mean of the given set of numbers? 10, 20, 30, -15, -25, -20
What does a measure of variation tell you about a dataset? I. How spread out the data is II. Where the center of the data is III. How large the dataset is IV. All of these
What do measures of central tendency tell you about a dataset?
III. They tell you how large the dataset is
PRACTICE QUIZ: MEASURES OF CENTRAL TENDENCY
=AVERAGE(E200:E350)
3, 1, 10, 9, 2, 5
=(MODE A1:A1000)
-4, 1, 7, -2, 48, 22, -11
AVERAGE(D2:D20)
7, -7, 4, 0, -1, -7, 0
PRACTICE QUIZ: MEASURES OF DISPERSION
z = (value – mean)/std.
2, 3, -5, 8, 0, -2
2, 1, 5, 10, 6, 45
-7, 1, -3, -2, 4, -1
GRADED QUIZ: DESCRIPTIVE STATISTICS
23, 20, 31, 11, 15, 19
5, -1, 3, 8, 2, 1, 0
1, 3, 7, 5, 3
-1, 3, 6, 5, -3
10, 20, 30, -15, -25, -20
1, -2, 3, -1, 2, -3
4, 7, 3, 1, 2, 1
26, 11, 45, 0, 7, 7, 0
PRACTICE QUIZ: SAMPLING
III. The accuracy of data analysis will always increase as more data is collected.
I and II
I and III
II and III
All of these are conclusions of the theorem.
A company wishes to gauge customer satisfaction. They know that 30% of their population is under the age of 50 and 70% is above the age of 50. They decide to poll 100 customers by choosing 30 customers who are under 50 and 70 who are over 50.
PRACTICE QUIZ: DISTRIBUTIONS
Cube Root, Square, Square Root, Logarithmic
PRACTICE QUIZ: VARIABLE TYPES
.
GRADED QUIZ: SAMPLING, DISTRIBUTION, AND VARIABLES
A university athletics department wants to assess the quality of life for its student athletes. There are 1000 student athletes at the school, with 60% male and 40% female. The department polls 100 athletes, choosing 60 male athletes and 40 female athletes.
A restaurant wants to add a new menu item. The cost of the item to the restaurant is significant, so they only are interested in doing it if they believe that the customers will purchase the dish. They survey the first 20 customers who enter the restaurant each day for a month for feedback.
26.
Meta Marketing Analytics – Course 3 Week 1 (Image 1)
The above image is an example of what?
A student club at a large high school wants to put on a movie night for the rest of the students. They poll ten students from each class (freshmen, sophomores, etc.) to determine the movie.
EXPERIMENTAL DESIGN AND HYPOTHESES
PRACTICE QUIZ: HYPOTHESIS AND AB TESTING
=ttest(Group1, Group2, Tails, Type)
=ttest(Group, Tails, Type)
GRADED QUIZ: EXPERIMENTAL DESIGN AND TESTING
4.
Alpha = 0.05
P-value = 0.04
With the information above, what conclusions can you draw?
21.
Alpha = 0.05
P-value = 0.08
With the information above, what conclusions can you draw regarding H0 and H1 in a hypothesis test?
24.
Alpha = 0.06
P-value = 0.05
With the information above, what conclusions can you draw?
Alpha = 0.05
P-Value = 0.04
With the information above, what conclusions can you draw?
PRACTICE QUIZ: STATISTICAL MODELING
PRACTICE QUIZ: SIMPLE LINEAR REGRESSION
PRACTICE QUIZ: CLUSTER ANALYSIS
PRACTICE QUIZ: TIME SERIES
GRADED QUIZ: STATISTICAL MODELING
Dependent Variable: Quantitative
Purpose: Predict the Dependent Variable using the Independent Variable
Which model is most appropriate for the conditions listed above?
PRACTICE QUIZ: SEGMENTATION IN MARKETING
PRACTICE QUIZ: CLUSTER ANALYSIS
Meta Marketing Analytics Course 4 Week 1 (Chart 1)
GRADED QUIZ: SEGMENTATION
True
False
PRACTICE QUIZ: DESCRIPTIVE METHODS
Product Sold | $2,000 | Advertising Cost | $1,000 |
ROAS for Campaign Display
PRACTICE QUIZ: CUSTOMER LIFETIME VALUE
Customer | Sales | Number of transactions | Customer 1 | $200 | 2 | Customer 2 | $300 | 4 | Customer 3 | $250 | 3 |
Customer | Sales | Number of transactions | Customer 1 | $200 | 2 | Customer 2 | $300 | 4 | Customer 3 | $250 | 3 |
GRADED QUIZ: PLANNING AND FORECASTING
Customer | Sales | Number of transactions | Customer 1 | $200 | 2 | Customer 2 | $300 | 4 | Customer 3 | $250 | 3 |
Product Sold | $2,000 | Advertising Costs | $1,000 |
Products Sold | $2,000 | Advertising Costs | $1,000 |
Customer | Sales | Number of transactions | Customer 1 | $200 | 2 | Customer 2 | $300 | 4 | Customer 3 | $250 | 3 |
21.
Product Sold | $1,000 | Advertising Costs | $500 |
What is the ROAS of this campaign?
Customer ID | Recency | Frequency | Monetary | 1 | 3 | 2 | 481 | 2 | 9 | 4 | 530 | 3 | 12 | 6 | 435 |
PRACTICE QUIZ: AD EFFECTIVENESS MEASUREMENT
PRACTICE QUIZ: MEASURING AD EFFECTIVENESS WITH EXPERIMENTS
GRADED QUIZ: EVALUATING ADVERTISING EFFECTIVENESS
PRACTICE QUIZ: MARKETING MIX MODELING
An overview of which of my TV ad creatives worked best in the past year.
PRACTICE QUIZ: ATTRIBUTION
Gives 100% credit to the last click seen from a person before a conversion took place, even if an impression (ad view) was the last interaction.
Gives more credit to touchpoints (clicks or impressions) based on how recently they happened. This attribution model gives an increasing percentage of the credit for a conversion to touchpoints as they get closer in time to the conversion.
Gives 100% credit for a conversion that happened to the first click or visit that happened in the conversion path. If there is no click or visit, then it will credit the conversion to the first impression.
First and last touchpoints (clicks or impressions) are given more credit, while the credit for the remaining touchpoints in the middle is divided equally.
PRACTICE QUIZ: SALES FUNNEL ANALYSIS
GRADED QUIZ: OPTIMIZATION
Which of the following statements regarding the benefits of conducting this study are true? (Select all that apply)
You reach out to a market research agency to help you with this. What type of study should they do for you?
DATA ANALYTICS METHODS FOR MARKETING FINAL QUIZ
James knows that going to his boss with a solid plan will help to convince him to allocate marketing budget to the expansion of Snackwall.
As a starting point, James identifies the segments that he should focus on for his advertising campaign. After all, James knows that it would not be a good use of his money to market to everyone. James relies on K-means clustering, using last year’s data on business size and amount spent for Snackwall clients. James worked with Alya, his analyst, and together they ran several iterations of their K-means clustering. Now they are using the chart below to determine the number of clusters James should focus on in his campaign.
Meta Marketing Analytics Chart 1 – Snackwall
How many segments do you suggest James defines for his marketing campaign based on this plot?
Meta Marketing Analytics – K-means clustering
How would you define Cluster 1?
Next, James turns to showing his management that the advertising budget he was given last year was money well spent. So, James calculates the Return on Ad Spend for last year, and he looks at ROAS by cluster.
He shows the following results based on the total ad spend last year of $200K. The total revenue generated was $800K. The total cost invested last year was $500K, so the profit for the year for Snackwall was $300K.
Average order value = $1800
Average number of orders per year = 9
Profit Margin = 63%
Meta Marketing Analytics – Linear Regression Analysis
James should plan to run an attribution study. True or False?
Based on this visualization, which approach should James take to increase subscriptions?
Meta Marketing Analytics – K-means clustering
How would you define Cluster 3?
Average order value = $1000
Average number of orders per year = 15
Profit Margin = 46%
What type of test should James plan to make sure he can prove this to his boss?
How would you define Cluster 2?
Based on this visualization, which approach should James take to improve subscription rates?
Next, James turns to showing his management that the advertising budget he was given last year was money well spent. So, James calculates the Return on Ad Spend for last year, and he looks at ROAS by cluster.
He shows the following results based on the total ad spend last year of $120K. The total revenue generated was $940K. The total cost invested last year was $510K, so the profit for the year for Snackwall was $430K.
Based on this visualization, which approach should James take to increase subscriptions?
James knows that going to his boss with a solid plan will help to convince him to allocate marketing budget to the expansion of Snackwall.
As a starting point, James identifies the segments that he should focus on for his advertising campaign. After all, James knows that it would not be a good use of his money to market to everyone. James relies on K-means clustering, using last year’s data on business size and amount spent for Snackwall clients. James worked with Alya, his analyst, and together they ran several iterations of their K-means clustering. Now they are using the chart below to determine the number of clusters James should focus on in his campaign.
How many segments do you suggest James defines for his marketing campaign based on this plot?
Next, James turns to showing his management that the advertising budget he was given last year was money well spent. So, James calculates the Return on Ad Spend for last year, and he looks at ROAS by cluster.
He shows the following results based on the total ad spend last year of $250K. The total revenue generated was $750K. The total cost invested last year was $500K, so the profit for the year for Snackwall was $250K.
Average order value = $1500
Average number of orders per year = 12
Profit Margin = 59%
James knows that going to his boss with a solid plan will help to convince him to allocate marketing budget to the expansion of Snackwall.
As a starting point, James identifies the segments that he should focus on for his advertising campaign. After all, James knows that it would not be a good use of his money to market to everyone. James relies on K-means clustering, using last year’s data on business size and amount spent for Snackwall clients. James worked with Alya, his analyst, and together they ran several iterations of their K-means clustering. Now they are using the chart below to determine the number of clusters James should focus on in his campaign.
How many segments do you suggest James defines for his marketing campaign based on this plot?
James should plan to run a random control trial experiment. True or False?
PRACTICE QUIZ: INTRODUCTION TO ADS MANAGER
Company One’s ads target all American women who cycle, whereas Company Two’s ads target cyclists who live in Portland with Portland-specific messages.
Which part of Company Two’s Total Value score does Marissa’s purchasing history boost?
PRACTICE QUIZ: PLANNING AND SCHEDULING ADS WITH FACEBOOK ADS MANAGER
PRACTICE QUIZ: CREATING AN AD IN ADS MANAGER
GRADED QUIZ: FUNDAMENTALS OF FACEBOOK ADS MANAGER
PRACTICE QUIZ: EVALUATING CAMPAIGN RESULTS WITH ADS MANAGER
PRACTICE QUIZ: EVALUATING RESULTS AGAINST YOUR GOAL
GRADED QUIZ: ANALYZING CAMPAIGN RESULTS
PRACTICE QUIZ: CONVERSION LIFT TESTS
Based on this information, which of the following is correct?
PRACTICE QUIZ: BRAND LIFT TESTS
GRADED QUIZ: RUNNING FACEBOOK EXPERIMENTS
PRACTICE QUIZ: OPTIMIZING ADS WITH A/B TESTS
PRACTICE QUIZ: MARKETING MIX MODELING
GRADED QUIZ: OPTIMIZING YOUR MARKETING MIX
PRACTICE QUIZ: ASSESS YOUR DATA AND HYPOTHESIZE
PRACTICE QUIZ: RECOMMEND MEASUREMENT SOLUTIONS AND PERFORM AN ANALYSIS
GRADED QUIZ: MARKETING ANALYTICS IN ACTION
Ads targeted to women 30-55, interested in architecture, living in Cities in France will increase website visits.
What could help to make this hypothesis stronger?
Ads targeted to Cities in the US will increase website visits in August.
What could help to make this hypothesis stronger?
Ads targeted to men 25-40, interested in dogs, will increase website visits in Q1.
What could help to make this hypothesis stronger?
By vmartinez
Web analytics tools can help an analyst learn: (Choose all that apply)
By vmartinez
Google Ads reports can be used to understand the results of advertising campaigns on Google. Which of the following is a specific report you can get in Google Ads that you won’t find in Facebook Ads Manager?
By vmartinez
Imra at Calla & Ivy wants to understand the results of the advertising campaign she ran on Instagram to promote her flower subscription service. More specifically, she would like to know how much money she had to spend on ads on Facebook to get one subscription. What should Imra do?
By vmartinez
Which of the following questions can you answer with Facebook Ads Manager reports? (Choose all that apply)
By vmartinez
Use the scenario and report linked in Question 1 to answer this question.
Which keyword was most cost effective in achieving results, in other words, had the lowest cost per conversion?
By vmartinez
Use the scenario and report linked in Question 1 to answer this question.
What was the lowest cost per conversion?