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Assignment Week 3
Business and Data Understanding
Introduction to the Course
Introduction to Data Science
Introduction to Project Methodologies
Business Understanding Phase – Overview
Defining Project Success Criteria
Data Understanding Phase – Overview
Initial Data Analysis and Exploratory Data Analysis
Downloads
Quiz
Data Preparation
Data Preparation Phase – Overview
Predictive Modeling Methodology – Overview
Data Manipulation
Data Encoding
Selecting Data – Variable and Feature Selection
Downloads
Quiz
Modeling (1)
Modeling Phase – Overview
Detecting Anomalies
Association Analysis
Cluster Analysis
Unit 5 A: Classification Analysis with Regression
Unit 5 B: Classification Analysis with Regression
Downloads
Quiz
Modeling (2)
Classification Analysis with Decision Trees
Classification Analysis with KNN, NN, and SVM
Time Series Analysis
Ensemble Methods
Simulation and Optimization
Downloads
Quiz
Evaluating Model Performance
Model Performance Metrics
Model Testing
Improving Model Performance
Evaluation Phase – Overview
Evaluating Model Performance
Downloads
Quiz
Deployment and Maintenance
Deployment Phase – Overview
Deployment Options
Monitoring and Maintenance
Data Science Applications and References
Downloads
Quiz
Business and Data Understanding
Introduction to the Course
Introduction to Data Science
Introduction to Project Methodologies
Business Understanding Phase – Overview
Defining Project Success Criteria
Data Understanding Phase – Overview
Initial Data Analysis and Exploratory Data Analysis
Downloads
Quiz
Data Preparation
Data Preparation Phase – Overview
Predictive Modeling Methodology – Overview
Data Manipulation
Data Encoding
Selecting Data – Variable and Feature Selection
Downloads
Quiz
Modeling (1)
Modeling Phase – Overview
Detecting Anomalies
Association Analysis
Cluster Analysis
Unit 5 A: Classification Analysis with Regression
Unit 5 B: Classification Analysis with Regression
Downloads
Quiz
Modeling (2)
Classification Analysis with Decision Trees
Classification Analysis with KNN, NN, and SVM
Time Series Analysis
Ensemble Methods
Simulation and Optimization
Downloads
Quiz
Evaluating Model Performance
Model Performance Metrics
Model Testing
Improving Model Performance
Evaluation Phase – Overview
Evaluating Model Performance
Downloads
Quiz
Deployment and Maintenance
Deployment Phase – Overview
Deployment Options
Monitoring and Maintenance
Data Science Applications and References
Downloads
Quiz
Knowledge quiz
It's time to put what you've learned to the test, get 8 right to pass this week.
1.
In which task of the Modeling phase of the cross-industry standard process for data mining (CRISP-DM) methodology do you describe the plan for training, testing, and evaluating the models?
Choose the correct answer.
Select modeling technique
Generate test design
Build model
Assess model
2.
What do you try to achieve when building a robust model?
Choose the correct answer.
No training error with high-test error
High training error with low-test error
Low training error with low-test error
Low training error with high-test error
3.
How do you calculate the interquartile range value?
Choose the correct answer.
Q3-Q1
Q4-Q2
Q2-Q1
Q4-Q1
4.
What does a Silhouette value of -1 indicate?
Choose the correct answer.
There is an error, as Silhouette is measured between 0 and +1.
All records are located on the cluster centers of some other cluster.
On average, the records are equidistant between their own cluster center and the next nearest cluster center.
All records are located directly on their cluster centers.
5.
What does a coefficient of determination (R2) value of 0.4 indicate?
Choose the correct answer.
40 percent of the variance in Y (the target) is predictable from X (the explanatory variables).
60 percent of the variance in Y (the target) is predictable from X (the explanatory variables).
60 percent of the variance in X (the explanatory variables) is predictable from Y (the target).
40 percent of the variance in X (the explanatory variables) is predictable from Y (the target).
6.
Which parameter settings can influence a cluster model?
There are three correct answers.
The number of clusters to be created
The choice of distance measure
The support
The lift
The initial choice of cluster centers
7.
Which types of analysis can be heavily biased by outliers?
There are three correct answers.
Linear regression
Simple mean
Correlation
Median
Trimmed mean
8.
Which outputs are generated in the Modeling phase of the cross-industry standard process for data mining (CRISP-DM) methodology?
There are two correct answers.
Modeling assumptions report
Parameter settings report
Data exploration report
Risks and contingencies report
Dataset description
9.
What would be the confidence of the rule {Product A} => {Product B}, if the total number of baskets analyzed is 300, when 100 customers purchased Product A, 60 customers purchases product B, and 20 customers purchased both product A and B?
Choose the correct answer.
20.00%
166.67%
60.00%
6.67%
10.
Which statements regarding association rules are true?
There are two correct answers.
The lift of a rule is symmetric.
The support of a rule is symmetric.
The confidence of a rule is symmetric.
Any rule with a lift greater than 1 does not indicate a real cross-selling opportunity.