data science methodology coursera week 3 quiz answers
From Deployment to Feedback
1. Feedback is not required once the model is deployed because the Model Evaluation stage would have assessed the model and made sure that it performed well.
- True
- False
2. A data scientist determines that building a recommender system is the solution for a particular business problem at hand. This is represented by the Modeling stage of the data science methodology?
- True
- False
3. A car company asked a data scientist to determine what type of customers are more likely to purchase their vehicles. However, the data comes from several sources and is in a relatively “raw format”. What kind of processing can the data scientist perform on the data to prepare it for the Modeling stage?
A. Feature Engineering.
B. Transforming the data into more useful variables.
C. Combining the data from the various sources.
D. Addressing missing invalid values.
- Only options A and D are correct.
- Only option C is correct.
- None of the options are correct.
- All of the options are correct.
4. Which of the following represent the two important characteristics of the data science methodology?
- It has no endpoint because data collection occurs before identifying the data requirements.
- It is a highly iterative process and immediately ends when the model is deployed.
- It is a highly iterative process and it never ends.
- It immediately ends when the model is deployed because no feedback is required.
5. For predictive models, a test set, which is similar to – but independent of – the training set, is used to determine how well the model predicts outcomes. This is an example of what step in the methodology?
- Data Requirements.
- Analytic Approach.
- Deployment.
- Model Evaluation.
6. What are three important reasons that data scientists should maintain continuous communication with business sponsors throughout a project?
- So that business sponsors can review intermediate findings.
- Actually, data scientists do not need to maintain a continuous communication with business sponsors and stakeholders.
- So that business sponsors can provide domain expertise.
- So that business sponsors can ensure the work remains on track to generate the intended solution.
Final Exam
7. The first state of the ________________ is Business Understanding.
- Computer modeling methodology
- Data science methodology
- Data analysis methodology
- Data collection methodology
8. Business Understanding is an important stage in the data science methodology because;
- It clearly defines the problem and the needs from a business perspective.
- It generates the data that will be used in the study.
- It ensures that the work generates all possible solutions.
- It is determined by the analytical approach you want to use.
9. Which of the following analogies is used in the videos to explain the Data Requirements and Data Collection stages of the data science methodology?
- You can think of the Data Requirements and Data Collection stages as building an outpatient clinic for patients with congestive heart failure, where the medical condition is the data and the patients are the ingredients.
- You can think of the Data Requirements and Data Collection stages as a cooking task, where the problem at hand is a recipe, and the data to answer the question is the ingredients.
10. In what stage can techniques such as descriptive statistics and visualization applied to the data set, to assess the content, quality, and initial insights about the data?
- The Business Analysis stage
- The Data Requirements stage
- The Data Analysis stage
- The Data Collection stage
11. A training set is used for what?
- Data Visualization
- Predictive modeling
- Statistical analysis
- Descriptive modeling
12. A statistician calls a false-negative, a type I error, and a false-positive, a type II error.
- True
- False
13. The Data Understanding stage encompasses what?
- All activities related to constructing the dataset.
- Sorting the data.
- Transforming data
- Removing redundant data.
14. The Data Preparation stage involves what?
- Addressing missing values.
- Correcting invalid values and addressing outliers.
- Removing duplicate data.
- Formatting the data.
- All of the above
15. The final stages of the data science methodology are an iterative cycle between which of the different stages?
- Data Understanding, Data Preparation, Evaluation, and Modelling.
- Modelling, Evaluation, Deployment, and Feedback.
- Modelling, Evaluation, Data Understanding, Data Preparation, and Deployment.
- Modelling, Data Preparation, Deployment, and Feedback.
16. Deploying a model into production represents the beginning of an iterative process between;
- Feedback
- Model Refinement
- Redeployment
- All of the above
17. Select the correct sentence about the data science methodology as explained in the course.
- The data science methodology does not depend on a specific set of technologies or tools.
- The data science methodology always starts with Business Understanding.
- The data science methodology is an iterative process.
- All of the above
18. What do data scientists typically use for exploratory analysis of data and to get acquainted with it?
- They begin with regression, classification, or clustering.
- They use descriptive statistics and data visualization techniques.
- They use deep learning.
- They use support vector machines and neural networks as feature extraction techniques.