Data Visualization with Python Cognitive Class Exam Quiz Answers

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Enroll Here: Data Visualization with Python Cognitive Class Exam Quiz Answers

Data Visualization with Python Cognitive Class Certificate Answers

Module 1 – Introduction to Visualization Tools Quiz Answers – Cognitive Class

Question 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.

Question 2: Using the inline backend, you can modify a figure after it is rendered.

• False
• True

Question 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 Quiz Answers – Cognitive Class

Question 1: Area plots are stacked by default.

• False
• True

Question 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)

Question 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 Quiz Answers – Cognitive Class

Question 1: Pie charts are less confusing than bar charts and should be your first attempt when creating a visual.

• False
• True

Question 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

Question 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 Quiz Answers – Cognitive Class

Question 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”)

Question 2: In Python, creating a waffle chart is straightforward since we can easily create one using the scripting layer of Matplotlib.

• False
• True

Question 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, 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 Quiz Answers – Cognitive Class

Question 1: What tile style of Folium maps is usefule for data mashups and exploring river meanders and coastal zones?

• OpenStreetMap
• Mapbox Bright
• Stamen Toner
• Stamen Terrain
• River and Coastal

Question 2: You cluster markers superimposed onto a map in Folium using a feature group object.

• False
• True

Question 3: If you know that the latitude of Spain is 40.4637° N and its longitude is 3.7492° W, and 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’)
• 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, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6)

Data Visualization with Python Final Exam Answers – Cognitive Class

Question 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.

Question 2: Matplotlib was created by John Hunter, an American neurobiologist, and was originally developed as an EEG/ECoG visualization tool.

• False
• True

Question 3: One type of Artist object is the primitive type. Which of the following are examples of the primitive type? Check all that apply.

• Rectangle
• Figure
• Axes
• Circle
• Text

Question 4: Using the notebook backend, you can modify a figure after it is rendered.

• False
• True

Question 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.

Question 6: Which of the following are instances of the Artist object? Check all that apply.

• Titles
• Event
• FigureCanvas
• Tick Labels
• Images

Question 7: There are three types of Artist objects.

• False
• True

Question 8: Each primitive artist may contain other composite artists as well as primitive artists.

• False
• True

Question 9: Given a pandas dataframe, question, which of the following will create an unstacked area plot of the data in question?

• question.plot(type=’area’, stacked=False)
• question.plot(kind=’area’, unstacked=True)
• question.plot(kind=’area’, stacked=False)
• question.plot(kind=’area’)
• question.plot(type=’area’, unstacked=True)

Question 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

Question 11: What is the correct function, parameter and value input for creating a pie chart in Matplotlib?

• Function = plot, parameter = kind, value = “pie”
• Function = pie, parameter = type, value = “plot”
• Function = plot, parameter = type, value = “pie”
• Function = pie, parameter = kind, value = “plot”

Question 12: What are the five main dimensions of a box plot? Select all that apply.

• Minimum
• Standard Deviation
• Maximum
• First Quartile
• Third Quartile
• Median
• Skewness

Question 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’)

Question 14: A bubble plot is a variation of the scatter plot that displays three dimensions of data.

• False
• True

Question 15: Seaborn is a Python visualization library that is built on top of Matplotlib.

• False
• True

Question 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”)

Question 17: Which of the following can be accomplished with the package word_cloud in Python? Select all that apply.

• Create a word cloud based on the frequency of different words in some textual data.
• Create a bubble plot based on the word cloud.
• Superimpose the words in a word cloud onto the mask of any shape.
• Import default stop words.

Question 18: The following are tile styles of folium maps (choose all that apply).

• Stamen Terrain
• River Coastal
• Stamen Toner
• Mapbox Bright
• Open Stamen

Question 19: You cluster markers superimposed onto a map in Folium using a marker cluster object.

• False
• True

Question 20: If you know that the latitude and the longitude of Spain are 40.4637° N and 3.7492° W, respectively, and 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’)
• folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6)

Introduction to Data Visualization with Python

Data visualization is a powerful tool for understanding and communicating insights from data. Python offers a rich ecosystem of libraries for creating stunning visualizations. Let’s delve into an introduction to data visualization with Python:

Libraries for Data Visualization in Python:

1. Matplotlib: A versatile library for creating static, interactive, and animated visualizations. It provides a MATLAB-like interface and is widely used for basic plotting.
2. Seaborn: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive and informative statistical graphics.
3. Pandas Visualization: This is a convenience wrapper around Matplotlib that simplifies plotting directly from pandas DataFrames and Series.
4. Plotly: Ideal for creating interactive plots and dashboards. It supports a wide range of plot types and offers excellent interactivity.
5. Bokeh: Another library for creating interactive visualizations, particularly suited for web browsers. It’s designed for creating interactive and scalable plots.
6. Altair: A declarative statistical visualization library in Python, based on the Vega and Vega-Lite visualization grammar. It allows for concise, high-level specification of complex visualizations.