Data Science Capstone Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

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Here, you will find Data Science Capstone Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Data Science Capstone from Coursera Free Certification Course.

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About Data Science Capstone Course

This course is included in numerous curricula. This course is applicable to a number of Specialization and Professional Certificate programs.

Course Apply Link – Data Science Capstone

Data Science Capstone Quiz Answers

Applied Data Science Capstone Week 01 Quiz Answers

Check Points: Data Collection API Answers

Q1. Did you Request and parse the SpaceX launch data using the GET request?

• Yes
• No

Q2. Did you filter the dataframe to only include Falcon 9 launches?

• Yes
• No

Q3. Did you relace None values in the PayloadMass with the mean?

• Yes
• No

Graded Quiz: Data Collection API with Webscraping Answers

Q1. After you performed a GET request on the Space X API and convert the response to a dataframe using pd.json_normalize. What year is located in the first row in the column static_fire_date_utc?

• 2006

Q2. Using the API, how many Falcon 9 launches are thereafter we remove Falcon 1 launches?

• 90

Q3. At the end of the API data collection process, how many missing values are there for the column landing pad?

• 26

Q4. After making a request to the Falcon9 Launch Wiki page and creating a BeautifulSoup object what is the output of:

soup. title

• < td colspan=”9″ > First flight of Falcon 9 v1.0. < sup class=”reference” id=”cite_ref-sfn20100604_17-0″
• <table class=”wikitable plainrowheaders collapsible” style=”width: 100%;”>
• <title> List of Falcon 9 and Falcon Heavy launches – Wikipedia </title>

Check Points: Data Wrangling Answers

Q1. Did you calculate the number of launches on each site?

• Yes
• No

Q2. Did you calculate the number and occurrence of each orbit?

• Yes
• No

Q3. Did you calculate the number and occurrence of mission outcome per orbit type?

• Yes
• No

Q4. Did you create a landing outcome label from the outcome column?

• Yes
• No

Graded Quiz: Data Wrangling Quiz Answers

Q1. How many launches came from CCAFS SLC 40?

• 55

Q2. What was the success rate?

• 80%
• 67%
• 40%

Q3. In the lab you used the method .value_counts() to determine the number and occurrence of each orbit in the column Orbit. What was the value for Orbit with the column name GTO.

• 27

Q4. How many landing outcomes in the column landing_outcomes had a value of none.

• 19

Applied Data Science Capstone Week 02 Quiz Answers

Check Points: Exploratory Analysis Using SQL Answers

Q1. Have you created a Db2 database asset in your IBM Watson Studio project?

• Yes
• No

Q2. Have you loaded SpaceX dataset into Db2 Table?

• Yes
• No

Q3. Have you used SQL queries with the SQL magic commands in Python to perform EDA?

• Yes
• No

Exploratory Data Analysis using SQL Answers

Q1. Which of the following will retrieve the most recent date from the spacex table?

• SELECT MAXIMUM(Date) from SPACEXTBL
• SELECT HIGHEST(Date) from SPACEXTBL
• SELECT DATE FROM SPACEXTBL WHERE DATE=MAX(DATE)
• SELECT max(Date) from SPACEXTBL

Q2. Which of the following queries display the minimum payload mass?

• select payload_mass__kg_ from SPACEXTBL order by payload_mass__kg_ group by booster_version LIMIT 1
• select payload_mass__kg_ from SPACEXTBL order by payload_mass__kg_ desc LIMIT 1
• select min(payload_mass__kg_) from SPACEXTBL
• select payload_mass__kg_ from SPACEXTBL where payload_mass__kg_=(select max(payload_mass__kg_) from SPACEXTBL) LIMIT 1

Q3. You are writing a query that will give you the total payload_mass_kg carried by the booster versions. The mass should be stored in the mass column. You want the result column to be called “Total_Payload_Mass”. Which of the following SQL queries is correct?

• SELECT sum(PAYLOAD_MASS__KG_) as Total_Payload_Mass from SPACEXTBL
• SELECT sum(PAYLOAD_MASS__KG_) from SPACEXTBL
• SELECT count(PAYLOAD_MASS__KG_) as Total_Payload_Mass from SPACEXTBL

Q4. Which of the following query to display 5 records launched on Friday?

• SELECT * FROM SPACEXTBL where DAYNAME(DATE)=’Friday’ LIMIT 5
• SELECT * FROM SPACEXTBL where DAY(DATE)=’Friday’ LIMIT 5

Q5. What are the unique launch sites mentioned in the Spacex table?

• CCAS LC-40,KSC LC-39A
• CCAFS LC-40,KSC LC-39B
• CCAFS LC-40,KSC LC-39A
• None of the Above

Exploratory Data Analysis for Data Visualization Answers

Q1. What type of data does a Bar Chart best represent?

• Location Data
• Numerical
• Categorical
• None of the above

Q2. What are the total number of columns in the features dataframe after applying one hot encoding to columns Orbits, LaunchSite, LandingPad and Serial .

Here the features  dataframe consists of the following columns FlightNumber’, ‘PayloadMass’, ‘Orbit’, ‘LaunchSite’, ‘Flights’, ‘GridFins’, ‘Reused’, ‘Legs’, ‘LandingPad’, ‘Block’, ‘ReusedCount’, ‘Serial’

• 120
• 80
• 83
• 96

Q3. The catplot code to show the scatterplot of  FlightNumber vs LaunchSite with x as FlightNumber, and y to Launch Site and hue to ‘Class’ is

 sns.catplot(y=”LaunchSite”,x=”FlightNumber”,hue=”Class”, data=df, aspect = 1,kind=’cat’)

plt.ylabel(“Launch Site”,fontsize=15)

plt.xlabel(“Flight Number”,fontsize=15)

plt.show()

 sns.catplot(y=”LaunchSite”,x=”FlightNumber”,hue=”Class”, data=df, aspect = 1)

plt.ylabel(“Launch Site”,fontsize=15)

plt.xlabel(“Flight Number”,fontsize=15)

plt.show()

sns.catplot(y=”LaunchSite”,x=”FlightNumber”,hue=”Class”, data=df, aspect = 1,kind=’scatter’)

plt.ylabel(“Launch Site”,fontsize=15)

plt.xlabel(“Flight Number”,fontsize=15)

plt.show()

sns.catplot(y=”LaunchSite”,x=”FlightNumber”,hue=”Class”, col=”Class”, data=df, aspect = 1)

plt.ylabel(“Launch Site”,fontsize=15)

plt.xlabel(“Flight Number”,fontsize=15)

plt.show()

Applied Data Science Capstone Week 03 Quiz Answers

Q1. How can you add marking objects such as circles, markers, or lines on a Folium map? (Click all choices that apply)

• add_node(map, object)
• map.add_child(object)
• map.add_to(object)
• object.add_to(map)

Q2. If you want to add multiple markers with similar coordinates on the Folium map, which Folium plugin you should use?

• MarkerCluster
• MarkerGroup
• MarkerContainer
• Markers should be add to map directly without any extra layer

Q3. Which attribute is used to provide available selections (such as a list of launch sites) for a Plotly DropDown input?

• input
• values
• placeholder
• options

Q4. How can we associate the result of a callback function (like a Ploty figure) to an element defined in the application layout

• Using a unique component id
• Dash automatically render the result of a callback function
• Using component name

Q5. Can we add multiple input components to a dash callback function

• Yes
• No

Applied Data Science Capstone Week 04 Quiz Answers

Graded Quiz: Predictive Analysisis Answers

Q1. How many records were there in the test sample?

• 18

Q2. For Support Vector Machines, what kernel has the best result on the validation dataset

• linear
• rbf
• sigmoid

Q3. After selecting the best hyperparameters for the decision tree classifier using the validation data, what was the accuracy achieved on the test data

• 83.33%
• 73.33%
• 93.33%

More About This Course

Students will be able to develop a usable/public data product that can be utilized to demonstrate their talents to prospective employers through the capstone project course. The projects will be based on real-world issues and will be done in partnership with industry, government, and academic institutions.

This course is included in numerous curricula.
This course is applicable to a number of Specialization and Professional Certificate programs. This course will contribute to your education in any of the following programs:

• Data Science Specialization
• Statistics and Machine Learning Specialization in Data Science

WHAT YOU WILL Discover

• Create a public data product that is useful
• Employ your abilities in exploratory data analysis
• Create an effective and precise prediction model
• Create a PowerPoint deck highlighting your findings.

SKILLS YOU WILL GAIN

• Data Science
• Machine Learning
• R Programming
• Natural Language Processing

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Data Science Capstone Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.