Hello Learners, Today, we are going to share Free Data Visualization With Python Cognitive Class Course Exam Answer launched by IBM. This certification course is totally free of cost✅✅✅ for you and available on Cognitive Class platform.
Here, you will find Data Visualization With Python Exam Answers in Bold Color which are given below.
These answers are updated recently and are 100% correct✅ answers of all modules and final exam answers of Data Visualization With Python from Cognitive Class Certification Course.
| Course Name | Python For Data Science of Cognitive Class |
| Organization | IBM |
| Skill | Online Education |
| Level | Beginner |
| Language | English |
| Price | Free |
| Certificate | Yes |
Link for Course Enrollment: Enroll Now
Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.
Module 1: Introduction to Visualization Tools
1. What are the layers that make up the Matplotlib architecture?
- FigureCanvas Layer, Renderer Layer, and Artist Layer.
- Backend_Bases Layer, Artist Layer, Scripting Layer.
- Backend Layer, Artist Layer, and Scripting Layer.
- Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer.
- Figure Layer, Artist Layer, and Scripting Layer
2. Using the inline backend, you can modify a figure after it is rendered.
- False
- True
3. Which of the following are examples of Matplotlib magic functions? Choose all that apply.
- %matplotlib inline
- #matplotlib notebook
- $matplotlib outline
- %matplotlib notebook
- #matplotlib inline
Module 2: Basic Visualization Tools
1. Area plots are stacked by default.
- False
- True
2. Given a pandas series, series_data, which of the following will create a histogram of series_data and align the bin edges with the horizontal tick marks?
- count, bin_edges = np.histogram(series_data)
- series_data.plot(kind=’hist’, xticks = count, bin_edges)
- count, bin_edges = np.histogram(series_data)
- series_data.plot(kind=’hist’, xticks = count)
- count, bin_edges = np.histogram(series_data)
- series_data.plot(kind=’hist’, xticks = bin_edges)
- series_data.plot(kind=’hist’)
- count, bin_edges = np.histogram(series_data)
- series_data.plot(type=’hist’, xticks = bin_edges)
3. Given a pandas dataframe, question, which of the following will create a horizontal barchart of the data in question?
- question.plot(type=’bar’, rot=90)
- question.plot(kind=’bar’, orientation=’horizontal’)
- question.plot(kind=’barh’)
- question.plot(kind=’bar’)
- question.plot(kind=’bar’, type=’horizontal’)
Module 3: Specialized Visualization Tools
1. Pie charts are less confusing than bar charts and should be your first attempt when creating a visual.
- False
- True
2. What do the letters in the box plot above represent?
- A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
- A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
- A = Median, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
- A = Median, B = Third Quartile, C = Mean, D = Inter Quartile Range, E = Lower Quartile, and F = Outliers
- A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Maximum
3. What is the correct combination of function and parameter to create a box plot in Matplotlib?
- Function = box, and Parameter = type, with value = “plot”
- Function = boxplot, and Parameter = type, with value = “plot”
- Function = plot, and Parameter = type, with value = “box”
- Function = plot, and Parameter = kind, with value = “boxplot”
- Function = plot, and Parameter = kind, with value = “box”
Module 4: Advanced Visualization Tools
1. Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe?
- import seaborn as sns
- ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”)
- data_dataframe.plot(kind=”regression”, color=”green”, marker=”+”)
- import seaborn as sns
- ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”, marker=”+”)
- data_dataframe.plot(kind=”regplot”, color=”green”, marker=”+”)
- import seaborn as sns
- ax = sns.regplot(x=”total”, y=”year”, data=data_dataframe, color=”green”)
2. In Python, creating a waffle chart is straightforward since we can easily create one using the scripting layer of Matplotlib.
- False
- True
3. A word cloud (choose all that apply)
- is a depiction of the frequency of different words in some textual data.
- is a depiction of the frequency of the stopwords, such as a, the, and, in some textual data.
- is a depiction of the meaningful words in some textual data, where the more a specific word appears in the text, the bigger and bolder it appears in the word cloud.
- can be generated in Python using the word_cloud library that was developed by Andreas Mueller.
- can be easily created using Matplotlib using the scripting layer.
Module 5: Creating Maps and Visualizing Geospatial Data
1. What tile style of Folium maps is usefule for data mashups and exploring river meanders and coastal zones?
- OpenStreetMap incorrect
- Mapbox Bright
- Stamen Toner
- Stamen Terrain
- River and Coastal incorrect
2. You cluster markers superimposed onto a map in Folium using a feature group object.
- False
- True
3. If you are interested in generating a map of Spain to visualize its hill shading and natural vegetation, which of the following lines of code will create the right map for you?
- folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Toner’) incorrect
- folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Terrain’) incorrect
- folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
- folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
- folium.Map(location=[40.4637, 3.7492], zoom_start=6)
Data Visualization with Python Final Exam Answers
1. Data visualizations are used to (check all that apply)
- explore a given dataset.
- perform data analytics and build predictive models.
- train and test a machine learning algorithm.
- share unbiased representation of data.
- support recommendations to different stakeholders.
2. Matplotlib was created by John Hunter, an American neurobiologist, and was originally developed as an EEG/ECoG visualization tool.
- False
- True
3. What are the layers that make up the Matplotlib architecture?
- FigureCanvas Layer, Renderer Layer, and Artist Layer.
- Backend_Bases Layer, Artist Layer, Scripting Layer.
- Backend Layer, Artist Layer, and Scripting Layer. correct
- Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer.
- Figure Layer, Artist Layer, and Scripting Layer.
4. Using the notebook backend, you can modify a figure after it is rendered.
- False
- True
5.The scripting layer is (check all that apply)
- comprised mainly of pyplot.
- an area on which the figure is drawn.
- a handler of user inputs such as keyboard strokes and mouse clicks.
- lighter that the Artist layer, and is intended for scientists whose goal is to perform quick exploratory analysis.
- comprised one one main object – Artist.
6. Which of the following are instances of the Artist object? (check all that apply)
- Titles
- Event
- FigureCanvas
- Tick Labels
- Images
7. There are three types of Artist objects.
- False
- True
8. Each primitive artist may contain other composite artists as well as primitive artists.
- False
- True
9. Given a pandas dataframe, question, which of the following will create a horizontal barchart of the data in question?
- question.plot(type=’bar’, rot=90)
- question.plot(kind=’bar’, orientation=’horizontal’)
- question.plot(kind=’barh’)
- question.plot(kind=’bar’)
- question.plot(kind=’bar’, type=’horizontal’)
10. Pie charts are relevant only in the rarest of circumstances, and bar charts are far superior ways to quickly get a message across.
- False
- True
11. What do the letters in the box plot above represent?
- A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
- A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
- A = Median, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers correct
- A = Median, B = Third Quartile, C = Mean, D = Inter Quartile Range, E = Lower Quartile, and F = Outliers
- A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Maximum
12. What is the correct combination of function and parameter to create a box plot in Matplotlib?
- Function = plot, and Parameter = kind, with value = “boxplot”
- Function = plot, and Parameter = type, with value = “box”
- Function = plot, and Parameter = kind, with value = “box” correct
- Function = box, and Parameter = type, with value = “plot”
- Function = boxplot, and Parameter = type, with value = “plot”
13. Which of the lines of code below will create the following scatter plot, given the pandas dataframe, df_total?
import matplotlib.pyplot as plt
plot(kind=’scatter’, x=’year’, y=’total’, data=df_total)
plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
plt.label (‘Year’)
plt.label(‘Number of Immigrants’)
import matplotlib.pyplot as plt
df_total.plot(type=’scatter’, x=’year’, y=’total’)
plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
plt.label (‘Year’)
plt.label(‘Number of Immigrants’)
import matplotlib.pyplot as plt
df_total.plot(kind=’scatter’, x=’year’, y=’total’)
plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
plt.xlabel (‘Year’)
plt.ylabel(‘Number of Immigrants’)
import matplotlib.scripting.pyplot as plt
df_total.plot(kind=’scatter’, x=’year’, y=’total’)
plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
plt.label (‘Year’)
plt.label(‘Number of Immigrants’)
import matplotlib.scripting.pyplot as plt
df_total.plot(type=’scatter’, y=’year’, x=’total’)
plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
plt.xlabel (‘Year’)
plt.ylabel(‘Number of Immigrants’)
14. A bubble plot is a variation of the scatter plot that displays three dimensions of data.
- False
- True
15. Seaborn is a Python visualization library that is built on top of Matplotlib.
- False
- True
16. Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe?
- import seaborn as sns
- ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”)
- data_dataframe.plot(kind=”regression”, color=”green”, marker=”+”)
- import seaborn as sns
- ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”, marker=”+”)
- data_dataframe.plot(kind=”regplot”, color=”green”, marker=”+”)
- import seaborn as sns
- ax = sns.regplot(x=”total”, y=”year”, data=data_dataframe, color=”green”)
17. A word cloud (choose all that apply):
- is a depiction of the frequency of different words in some textual data.
- is a depiction of the frequency of the stopwords, such as a, the, and, in some textual data.
- is a depiction of the meaningful words in some textual data, where the more a specific word appears in the text, bigger and bolder it appears in the word cloud.
- can be generated in Python using the word_cloud library that was developed by Andreas Mueller.
- can be easily created using Matplotlib using the scripting layer.
18. The following are tile styles of folium maps (choose all that apply).
- Stamen Terrain
- River Coastal
- Stamen Toner
- Mapbox Bright
- Open Stamen
19. You cluster markers superimposed onto a map in Folium using a marker cluster object.
- False
- True
20. If you are interested in generating a map of Spain to explore its river meanders and coastal zones. Which of the following lines of code will create the right map for you?
- folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Terrain’)
- folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Toner’)
- folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles=’Stamen Toner’) correct
- folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
- folium.Map(location=[40.4637, 3.7492], zoom_start=6)
Conclusion
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FAQs
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Yes, you will receive a Data Visualization With Python Certificate of Learning after successful completion of course. You can download a printed certificate or share completion certificates with others and add them to your LinkedIn profile.
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You should go to an online certification course to get credentials that can help you in your work. It also helps you to share your skills with the employer. These certificates are an investment in building your business. And the important thing you can access these courses anytime and multiple times.
Is this course is free?
Yes Data Visualization with Python Course is totally free for you. The only thing is needed i.e. your dedication towards learning this course.
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