Data Analysis and Visualization Study Guide Questions
Course Introduction
This course provides an introduction to the fundamental concepts and techniques of data analysis and visualization. Students will learn to process, analyze, and interpret data using statistical methods and computational tools. Emphasis will be placed on exploring datasets, identifying patterns and trends, and effectively communicating results through visual representations such as charts, graphs, and dashboards. Through practical exercises and projects, students will develop the skills needed to transform raw data into actionable insights, supporting data-driven decision-making in various fields.
Recommended Textbook
Data Mining A Tutorial Based Primer 1st Edition by Richard Roiger
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14 Chapters
167 Verified Questions
167 Flashcards
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Chapter 1: Data Mining: a First View
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22 Verified Questions
22 Flashcards
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Sample Questions
Q1) Data mining is best described as the process of
A) identifying patterns in data.
B) deducing relationships in data.
C) representing data.
D) simulating trends in data.
Answer: A
Q2) Supervised learning differs from unsupervised clustering in that supervised learning requires
A) at least one input attribute.
B) input attriutes to be categorical.
C) at least one output attribute.
D) ouput attriubutes to be categorical.
Answer: C
Q3) Data used to build a data mining model.
A) validation data
B) training data
C) test data
D) hidden data
Answer: B
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Page 3
Chapter 2: Data Mining: a Closer Look
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16 Verified Questions
16 Flashcards
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Sample Questions
Q1) What percent of the instances were correctly classified?
Answer: 60%
Q2) Which statement is true about prediction problems?
A) The output attribute must be categorical.
B) The output attribute must be numeric.
C) The resultant model is designed to determine future outcomes.
D) The resultant model is designed to classify current behavior.
Answer: C
Q3) Which of the following is a common use of unsupervised clustering?
A) detect outliers
B) determine a best set of input attributes for supervised learning
C) evaluate the likely performance of a supervised learner model
D) determine if meaningful relationships can be found in a dataset
E) All of a,b,c, and d are common uses of unsupervised clustering.
Answer: E
Q4) How many instances were incorrectly classified with class 3?
Answer: 6
Q5) How many class 2 instances are in the dataset?
Answer: 23
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Chapter 3: Basic Data Mining Techniques
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13 Flashcards
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Sample Questions
Q1) Based on the two-item set table, which of the following is not a possible two-item set rule?
A) IF Life Ins Promo = Yes THEN Magazine Promo = Yes
B) IF Watch Promo = No THEN Magazine Promo = Yes
C) IF Card Insurance = No THEN Magazine Promo = Yes
D) IF Life Ins Promo = No THEN Card Insurance = No
Answer: D
Q2) Association rule support is defined as
A) the percentage of instances that contain the antecendent conditional items listed in the association rule.
B) the percentage of instances that contain the consequent conditions listed in the association rule.
C) the percentage of instances that contain all items listed in the association rule.
D) the percentage of instances in the database that contain at least one of the antecendent conditional items listed in the association rule.
Answer: C
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Chapter 4: An Excel-Based Data Mining Tool
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12 Verified Questions
12 Flashcards
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Sample Questions
Q1) What is the predictability score for the attribute value medium risk?
A) 0.10
B) 0.20
C) 0.25
D) 0.50
Q2) The first row of an iDAV formatted file contains attribute names. The second row reflects attribute types. What is specified in the third row of an iDAV formatted file?
A) attribute predictability
B) attribute tolerance
C) attribute similarity
D) attribute usage
Q3) ESX represents the overall similarity of the exemplars contained in an individual class by computing a ____ score.
A) class resemblance
B) class predictability
C) class predictiveness
D) typicality
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6
Chapter 5: Knowledge Discovery in Databases
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) The price of a 12 ounce box of cereal decreases from $3.50 to $3.00. What fraction is used to compute the percent decrease in the price of the cereal?
A) 1/3
B) 1/5
C) 1/6
D) 1/7
Q2) The choice of a data mining tool is made at this step of the KDD process.
A) goal identification
B) creating a target dataset
C) data preprocessing
D) data mining
Q3) A common method used by some data mining techniques to deal with missing data items during the learning process.
A) replace missing real-valued data items with class means
B) discard records with missing data
C) replace missing attribute values with the values found within other similar instances
D) ignore missing attribute values
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Chapter 6: The Data Warehouse
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13 Verified Questions
13 Flashcards
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Sample Questions
Q1) This process removes redundancies that may be present in a data model.
A) abstraction
B) granularization
C) standardization
D) normalization
Q2) A one-to-many relationship.
A) brother-sister
B) father-son
C) uncle-nephew
D) teacher-student
Q3) Which of the following is not an example of a slice operation?
A) Select all cells where purchase category = retail.
B) Select all cells where purchase category = retail or vehicle.
C) Provide a spreadsheet of quarter and region information for all cells pertaining to restaurant.
D) Identify the region of peak travel expenditure for each quarter.
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8
Chapter 7: Formal Evaluation Techniques
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Sample Questions
Q1) Unsupervised evaluation can be internal or external. Which of the following is an internal method for evaluating alternative clusterings produced by the K-Means algorithm?
A) Use a production rule generator to compare the rule sets generated for each clustering.
B) Compute and compare class resemblance scores for the clusters formed by each clustering.
C) Compare the sum of squared error differences between instances and their corresponding cluster centers for each alternative clustering.
D) Create and compare the decision trees determined by each alternative clustering.
Q2) If a real-valued attribute is normally distributed, we know that approximately 95% of all attribute values lie within
A) one standard deviation of the mean.
B) two standard deviations of the mean.
C) three standard deviations of the mean.
D) four standard deviations of the mean.
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9
Chapter 8: Neural Networks
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10 Flashcards
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Sample Questions
Q1) Epochs represent the total number of
A) input layer nodes.
B) passes of the training data through the network.
C) network nodes.
D) passes of the test data through the network.
Q2) Neural network training is accomplished by repeatedly passing the training data through the network while
A) individual network weights are modified.
B) training instance attribute values are modified.
C) the ordering of the training instances is modified.
D) individual network nodes have the coefficients on their corresponding functional parameters modified.
Q3) Genetic learning can be used to train a feed-forward network. This is accomplished by having each population element represent one possible
A) network configuration of nodes and links.
B) set of training data to be fed through the network.
C) set of network output values.
D) set of network connection weights.
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10
Chapter 9: Building Neural Networks With Ida
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4 Verified Questions
4 Flashcards
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Sample Questions
Q1) Two classes each of which is represented by the same pair of numeric attributes are linearly separable if
A) at least one of the pairs of attributes shows a curvilinear relationship between the classes.
B) at least one of the pairs of attributes shows a high positive correlation between the classes.
C) at least one of the pairs of attributes shows a high positive correlation between the classes
D) a straight line partitions the instances of the two classes
Q2) The test set accuracy of a backpropagation neural network can often be improved by
A) increasing the number of epochs used to train the network.
B) decreasing the number of hidden layer nodes.
C) increasing the learning rate.
D) decreasing the number of hidden layers.
Q3) This type of supervised network architecture does not contain a hidden layer.
A) backpropagation
B) perceptron
C) self-organizing map
D) genetic
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Chapter 10: Statistical Techniques
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Sample Questions
Q1) Machine learning techniques differ from statistical techniques in that machine learning methods
A) typically assume an underlying distribution for the data.
B) are better able to deal with missing and noisy data.
C) are not able to explain their behavior.
D) have trouble with large-sized datasets.
Q2) Regression trees are often used to model _______ data.
A) linear
B) nonlinear
C) categorical
D) symmetrical
Q3) This clustering algorithm initially assumes that each data instance represents a single cluster.
A) agglomerative clustering
B) conceptual clustering
C) K-Means clustering
D) expectation maximization
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Chapter 11: Specialized Techniques
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) At least eighty percent of the time spent on a Web-based data mining project is devoted to this.
A) goal idenficiation
B) data preparation
C) data mining
D) interpretation of results
Q2) Which of the following problems is best solved using time-series analysis?
A) Predict whether someone is a likely candidate for having a stroke.
B) Determine if an individual should be given an unsecured loan.
C) Develop a profile of a star athlete.
D) Determine the likelihood that someone will terminate their cell phone contract.
Q3) The automation of Web site adaptation involves creating and deleting
A) index pages
B) cookies
C) pageviews
D) clickstreams
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Chapter 12: Rule-Based Systems
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15 Verified Questions
15 Flashcards
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Sample Questions
Q1) Knowledge about knowledge is known as
A) metaknowledge
B) class knowledge
C) structured knowledge
D) classified knowledge
Q2) The knowledge contained in a knowledge-based system
A) contains the knowledge of one or more human experts.
B) contains the knowledge of one or more intelligent agents.
C) contains the knowledge of exactly one human expert.
D) contains knowledge extracted from one or several sources.
Q3) This reasoning strategy works best for problems where the goal can be stated as a question.
A) forward chaining
B) depth-first search
C) backward chaining
D) breadth-first search
Q4) Construct a goal tree using the following production rules. Assume the goal is g.
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Chapter 13: Managing Uncertainty in Rule-Based Systems
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) A fuzzy set is associated with a A) linguistic variable.
B) certainty factor.
C) hypothesis to be tested.
D) linguistic value.
Q2) For Bayes theorem to be applied, the following relationship between hypothesis H and evidence E must hold.
A) PH|E) + PH| ~E) = 1
B) PH|E) + P~H| E) = 1
C) PH|E) + PH| ~E) = 0
D) PH|E) + P~H| E) = 0
Q3) The probability that a person owns a sports car given that they subscribe to at least one automotive magazine is 40%. We also know that 3% of the adult population subscribes to at least one automotive magazine. Finally, the probability of a person owning a sports car given that they don't subscribe to at least one automotive magazine is 30%. Use this information together with Bayes theorem to compute the probability that a person subscribes to at least one automotive magazine given that they own a sports car.
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Chapter 14: Intelligent Agents
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6 Verified Questions
6 Flashcards
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Sample Questions
Q1) Autonomy is an agent's ability to
A) react to a changing environment.
B) act without direct intervention from others.
C) confer with other agents.
D) react to sensory information received from the environment.
Q2) An agent's ability to choose its actions in the context of other agents.
A) autonomy
B) cooperation
C) adaptivity
D) coordination
Q3) A fundamental difference between a data mining approach to problem solving and an expert systems approach is
A) the output of an expert system is a set of rules and the output of a data mining technique is a decision tree.
B) a data mining technique builds a model without the aid of a human expert.
C) a model built using a data mining technique can explain how decisions are made but an expert system cannot.
D) an expert system is built using inductive learning whereas a data mining model is built using one or several deductive techniques
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