Visualization Review Questions

Table of Contents

Preface

This page contains review questions for teaching ITSE-1302 Computer Programming: Scientific Python 1 at Austin Community College in Austin, TX.

The questions and the answers on this page are connected by hyperlinks to make it easy for you to navigate from the question to the answer and back again.

The questions on this page are similar to the questions that you will find on the test for this competency. Therefore, it is strongly recommended that you study the material until you thoroughly understand the material covered by these questions.

Questions

Question 1.

True or False? This course uses a conventional paper textbook.

Answer 1

Question 2

True or False? This course is structured into four competency units.

Answer 2

Question 3

True or False? Data visualization is a key competency for data science and analytics.

Answer 3

Question 4

True or False? Your grades on assessments in certain free online resources will be integrated into your grade for this course.

Answer 4

Question 5

True or False? The visualization competency for this course is based primarily on a Python library named Matplotlib with support from other libraries in the the Python scientific computing ecosystem.

Answer 5

Question 6

True or False? The document titled "Visualization Exercises Part 1" and other similar documents used for this competency were prepared using a tool named Jupyter Notebook.

Answer 6

Question 7

True or False? Students in this course are expected to know how to create Jupyter Notebook documents.

Answer 7

Question 8

True or False? Being able to present the results of a Data Science project in text, tables, graphs, and plots in such a way as to clearly communicate those results to a reader is a very important capability. This will often entail the need to combine multiple plots in a single figure.

Answer 8

Question 9

True or False? When all you need is a single plot, the matplotlib syntax can be very simple and straightforward. However, when you need to combine two or more plots in the same figure, that can become one of the most complex aspects of using matplotlib.

Answer 9

Question 10

True or False? A matplotlib Figure object can be thought of as the container for one or more plots or subplots.

Answer 10

Question 11

True or False?  There is only one way to arrange subplots in a matplotlib Figure object.

Answer 11

Question 12

True or False? In order to use the matplotlib library effectively, you also need to have some knowledge of various features of the numpy library.

Answer 12

Question 13

True or False? Python does not support keyword arguments.

Answer 13

Question 14

True or False? Sometimes when you arrange multiple subplots in a figure, it is cosmetically pleasing to let them share the tick labeling on the x-axis, the y-axis or both.

Answer 14

Question 15

True or False?  The standard math library and the numpy library each provide a function that returns the cosine of an angle expressed in radians. However, the input requirements of the two functions are different. The numpy.cos function expects to receive a single angle in radians and returns the consine of the angle. The math.cos function expects to receive a list or an array of angles expressed in radians and returns an array containing the cosine values for the corresponding angles.

Answer 15

Question 16

True or False? The numpy.exp function receives an array of input values and returns a single output value.

Answer 16

Question 17

True or False? Matplotlib supports the creation of major and minor tick marks as well as major and minor grid lines.

Answer 17

Question 18

True or False? Matplotlib supports the creation of both log and linear plots.

Answer 18

Question 19

True or False? Scatter plots show how much one variable is affected by another. The relationship between two variables is called their combined relationship.

Answer 19

Question 20

True or False? The simplest possible box plot displays the full range of variation (from min to max), the likely range of variation (the IQR), and a typical value (the median).

Answer 20

Question 21

True or False? The notch in a notched box plot displays the confidence interval around the median which is normally based on the median +/- 1.57 x IQR/sqrt of n.

Answer 21

Question 22

True or False? A violin plot is a hybrid of a box plot and a kernel density plot, which shows peaks in the data.

Answer 22

Question 23

True or False? Bar charts are used to display values associated with continuous data.

Answer 23

Question 24

True or False? Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.

Answer 24

Question 25

True or False? A pie chart (or a circle chart) is a circular statistical graphic which is divided into slices to illustrate numerical proportion.

Answer 25


Answers

Answer 25

True

Explanation

See "Visualization Exercises Part 7" Also see https://en.wikipedia.org/wiki/Pie_chart

Back to Question 25

Answer 24

True.

Explanation

See "Visualization Exercises Part 7" Also see http://www.stat.yale.edu/Courses/1997-98/101/catdat.htm

Back to Question 24

Answer 23

False

Explanation

Bar charts are used to display values associated with categorical data. See "Visualization Exercises Part 7" Also see http://benalexkeen.com/bar-charts-in-matplotlib/

Back to Question 23

Answer 22

True.

Explanation

See "Visualization Exercises Part 6" Also see https://blog.modeanalytics.com/violin-plot-examples/

Back to Question 22

Answer 21

True

Explanation

See "Visualization Exercises Part 5" Also see https://sites.google.com/site/davidsstatistics/home/notched-box-plots

Back to Question 21

Answer 20

True.

Explanation

See "Visualization Exercises Part 5" Also see http://www.physics.csbsju.edu/stats/box2.html

Back to Question 20

Answer 19

False.

Explanation

Scatter plots show how much one variable is affected by another. The relationship between two variables is called their correlation.

See "Visualization Exercises Part 4"

Back to Question 19

Answer 18

True.

Explanation

See "Visualization Exercises Part 3"

Back to Question 18

Answer 17

True.

Explanation

See "Visualization Exercises Part 3"

Back to Question 17

Answer 16

False.

Explanation

See "Visualization Exercises Part 2".

The numpy.exp function receives an array of input values and returns an array of corresponding output values.

Back to Question 16

Answer 15

False.

Explanation

See "Visualization Exercises Part 2".

The libraries are reversed in the above statement.

The standard math library and the numpy library each provide a function that returns the cosine of an angle expressed in radians. However, the input requirements of the two functions are different. The math.cos function expects to receive a single angle in radians and returns the consine of the angle. The numpy.cos function expects to receive a list or an array of angles expressed in radians and returns an array containing the cosine values for the corresponding angles.

This is typical of many features of the numpy library.

Back to Question 15

Answer 14

True.

Explanation

See "Visualization Exercises Part 1"

Back to Question 14

Answer 13

False.

Explanation

See "Visualization Exercises Part 1".

Also see https://docs.python.org/3.5/tutorial/controlflow.html#keyword-arguments. Many of the functions in the Python scientific computing ecosystem make heavy use of keyword arguments.

Back to Question 13

Answer 12

True.

Explanation

See "Visualization Exercises Part 1" where various sample programs import and use features of the numpy library, such as numpy.ndarray.

Back to Question 12

Answer 11

False.

Explanation

One of the reasons that arranging subplots in a figure can be complex is because there are different ways to create an arrangement and you need to keep the different ways separated in your mind.

See "Visualization Exercises Part 1"

Back to Question 11

Answer 10

True.

Explanation

See "Visualization Exercises Part 1"

Back to Question 10

Answer 9

True.

Explanation

See "Visualization Exercises Part 1"

Back to Question 9

Answer 8

True.

Explanation

See "Visualization Exercises Part 1"

Back to Question 8

Answer 7

False.

Explanation

Students in this course are not expected to know how to create Jupyter Notebook documents.

See "Visualization Exercises Part 1"

Back to Question 7

Answer 6

True.

Explanation

See "Visualization Exercises Part 1"

Back to Question 6

Answer 5

True.

Explanation

See "Visualization Exercises Part 1"

Back to Question 5

Answer 4

False.

Explanation

... grades and credits associated with those resources will not be integrated into your grade for this course. Your grade for this course will be based solely on your grades on assignments, quizzes, and tests administered by your ACC instructor through Blackboard.

See "Data Visualization using Matplotlib"

Back to Question 4

Answer 3

True.

Explanation

See "Data Visualization using Matplotlib"

Back to Question 3

Answer 2

 False.

Explanation

The course is structured into four major units: one review unit and three competency units.

See "Data Visualization using Matplotlib".

Back to Question 2

Answer 1

 False

Explanation

This course does not use a conventional paper or electronic textbook. Instead, this online study guide and the Blackboard learning management system will guide you through a variety of free online resources on topics that you will need to learn in order to succeed in the course.

See "Data Visualization using Matplotlib".

Back to Question 1

Houskeeping material

File name: VisualizationReview.htm

Revised: 04/24/18

Copyright 2018, Richard Baldwin

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