Complete Data Science Methodology Coursera Quiz Answers [2025 Edition]

Get All Modules Data Science Methodology Coursera Quiz Answers

Module 01: Practice Quiz: From Problem to Approach

Q1. Select the correct statement.

Answer: The first stage of the data science methodology is Business Understanding.

Explanation: The data science process begins with understanding the business problem to frame the question and objectives before moving into data-related tasks.

Q2. Select the answer that describes the primary purpose of the Analytic Approach stage.

Answer: To identify the type of patterns needed to address the posed question most effectively.

Explanation: This stage focuses on determining which analytical techniques (e.g., classification, clustering) are best suited to solve the problem.

Q3. Which machine learning algorithm did data scientists use for the case study, as described in the videos?

Answer: Logistic Regression.

Explanation: Logistic Regression is often used for binary classification problems, as it estimates probabilities and makes categorical predictions.

Module 01: Graded Quiz: From Problem to Approach

Q1. In a data science project, what is the role of the Business Understanding stage in guiding data collection efforts?

Answer: The Business Understanding stage identifies the data sources and variables required to address the business problem.

Explanation: This stage clarifies the business objectives and ensures that data collection focuses on relevant variables and sources for solving the problem.

Q2. What is the primary goal of the Business Understanding stage in this situation?

Answer: Understanding the factors contributing to customer churn.

Explanation: The Business Understanding stage aims to identify the key drivers of churn, which guides subsequent analysis and solution development.

Q3. What is the primary goal of the analytical approach in a data science project?

Answer: To iteratively explore and analyze data to find actionable insights.

Explanation: The analytical approach involves selecting methods to uncover patterns and insights that address the business problem.

Q4. What is the first step data scientists take to attain business understanding?

Answer: Understand the goal.

Explanation: Before diving into details, data scientists must grasp the overarching goal to align their efforts with business priorities.

Q5. What type of analytics would be most suitable for anticipating customer preferences based on historical purchase data?

Answer: Predictive Modeling.

Explanation: Predictive modeling uses historical data to forecast future behaviors, such as predicting customer preferences based on past purchases.

Module 01: Practice Quiz: From Requirements to Collection

Q1. Select the statement that describes what happens during the Data Requirements stage

Answer: Data scientists identify the necessary data content, formats, and sources for initial data collection.

Explanation: The Data Requirements stage involves specifying the type, format, and sources of data needed to address the business problem effectively.

Q2. Who determines how to collect and prepare the data?

Answer: Data scientists.

Explanation: Data scientists are responsible for defining the methods for data collection and preparation to ensure the data meets the needs of the analysis.

Q3. Which of the following statements is correct?

Answer: Data scientists determine how to prepare the data.

Explanation: Data scientists handle data preparation, including cleaning, transforming, and addressing missing or out-of-range values to ensure the data is ready for analysis.

Module 01: Graded quiz: From Requirements to Collection

Q1. In the context of data science methodology, select the answer that describes what happens during the Data Requirements stage.

Answer: Identifying the necessary data content, formats, and sources.

Explanation: The Data Requirements stage focuses on specifying the types of data, its formats, and where it can be sourced to solve the business problem effectively.

Q2. What technique can you apply to the data set during the data collection stage to assess data content, quality, and initial insights?

Answer: Data visualization and descriptive statistics.

Explanation: These techniques help in gaining initial insights into the data and assessing its quality by identifying patterns, outliers, and inconsistencies.

Q3. Imagine you are a data scientist working on a healthcare project. Your task is to define the data requirements for a decision tree classification approach to predict patient readmission rates.

Answer: To identify the necessary data content, formats, and source.

Explanation: Defining data requirements ensures that the data collected is relevant and sufficient for building the decision tree model to predict patient readmission accurately.

Q4. You are a data scientist tasked with collecting and preparing data for a predictive modeling project related to patient outcomes. You have encountered some data sources, mainly drug information, that are missing. How would you handle this situation?

Answer: Attempt to acquire the missing data after obtaining intermediate results.

Explanation: This approach allows progress in the project while trying to resolve missing data issues, ensuring the model can be refined and validated later with complete data.

Q5. Which stage of the methodology involves collaborating with DBAs and programmers to extract and merge data from various sources?

Answer: Data Collection.

Explanation: During the Data Collection stage, collaboration with database administrators and programmers is essential to gather data from diverse sources and ensure it is ready for analysis.

Module 02: Practice Quiz: Lesson 1 From Understanding to Preparation

Q1. In the case study, during the Data Understanding stage, data scientists discovered that not all the expected congestive heart failure admissions were being captured. What action did they take to resolve the issue?

Answer: The data scientists looped back to the Data Collection stage, adding secondary and tertiary diagnoses, and built a more comprehensive definition of congestive heart failure admission.

Explanation: When discrepancies were discovered in the data, the data scientists revisited the Data Collection stage to expand their data criteria, ensuring a more complete capture of relevant cases.

Q2. In the case study, while working through the Data Preparation stage, data scientists learned that the initial definition did not capture all expected congestive heart failure admissions.

Answer: True.

Explanation: During the Data Preparation stage, data scientists often uncover issues such as incomplete data definitions, prompting them to refine their approach to ensure accurate data representation.

Q3. Select the correct statement about the Data Preparation stage of the data science methodology.

Answer: The Data Preparation stage involves handling missing and improperly coded data and can include using text analysis to structure unstructured or semi-structured text data.

Explanation: This stage is critical for transforming raw data into a structured form suitable for analysis. It often includes cleaning, feature engineering, and converting unstructured data into structured formats.

Module 02: Graded Quiz Lesson 1: From Understanding to Preparation

Q1. What is the primary role of the data understanding phase in the data science methodology?

Answer: Assessing data quality and representativeness

Explanation: The Data Understanding phase focuses on evaluating the quality, completeness, and relevance of the data to ensure it aligns with the project’s objectives. This helps identify potential issues before proceeding.

Q2. How does the Data Preparation stage affect the next steps in a data science project?

Answer: The Data Preparation stage provides clean and formatted data for analysis.

Explanation: This stage ensures that data is properly cleaned, transformed, and ready for subsequent modeling and analysis, laying the groundwork for accurate results.

Q3. Why is the Data Preparation stage considered time-consuming for a data science project?

Answer: This stage involves transforming data into a usable format.

Explanation: Data Preparation requires extensive cleaning, handling missing values, and formatting data, which can be labor-intensive and critical for successful analysis.

Q4. What is the purpose of feature engineering during the Data Preparation stage?

Answer: You’ll use feature engineering to create meaningful characteristics for machine learning.

Explanation: Feature engineering transforms raw data into features that better represent the problem to the predictive models, improving their performance.

Q5. How does automating data collection and preparation processes affect the overall project time?

Answer: Automating data collection and preparation processes significantly reduces data collection time.

Explanation: Automation accelerates repetitive tasks, such as data extraction and cleaning, enabling data scientists to focus more on analysis and modeling, which shortens the overall project timeline.

Module 02: Practice Quiz Lesson 2: From Modeling to Evaluation

Q1. Which statement best describes the Modeling Stage of the data science methodology?

Answer: Modeling always requires testing multiple algorithms and parameters

Explanation: During the Modeling stage, different algorithms and parameters are tested to determine the best approach for the given data and business problem. This process helps find the most effective model.

Q2. The Evaluation stage, or Modeling Evaluation, takes place before sharing the model with stakeholders and other users.

Answer: True

Explanation: The Evaluation stage occurs after the model is developed but before sharing it with stakeholders. This stage ensures the model meets the requirements and performs as expected.

Q3. Select the three correct statements about the Evaluation stage of the data science methodology.

Answer:

Evaluating the data model includes ensuring that the model processes the data as intended.
Model Evaluation ensures the data are correctly handled and interpreted.
Model Evaluation includes validating that the model is designed as intended.

Explanation: The Evaluation stage checks the model’s functionality, accuracy, and alignment with the intended purpose. It ensures proper data handling and confirms that the model behaves as expected before it is deployed or shared with stakeholders.

Module 02: Graded Quiz Lesson 2: From Modeling to Evaluation

Q1. What is the main purpose of data modeling in the data science methodology?

Answer: To develop models for descriptive or predictive purposes

Explanation: Data modeling is focused on developing models that either describe data patterns or predict future outcomes based on historical data.

Q2. How does a training set contribute to predictive modeling?

Answer: A training set serves as a calibration gauge for the model

Explanation: A training set is used to teach the model by providing input-output pairs. It helps the model learn patterns and make predictions based on this data.

Q3. What is the primary purpose of model evaluation in the data science methodology?

Answer: To assess the quality of the model and ensure it meets the initial request

Explanation: Model evaluation checks if the model performs correctly and meets the goals outlined in the business problem or project requirements.

Q4. What is the purpose of diagnostic measures during model evaluation?

Answer: To ensure the model is working as intended

Explanation: Diagnostic measures help verify that the model is functioning as expected, identifying any issues that might need adjustment before final deployment.

Q5. What does the ROC curve help determine in model evaluation?

Answer: The true-positive rate and false-positive rate for different criteria

Explanation: The ROC (Receiver Operating Characteristic) curve plots the true-positive rate (sensitivity) against the false-positive rate (1-specificity) to evaluate the performance of a classification model.

Module 03: Practice Quiz: From Deployment to Feedback

Q1. Which of the following statements is correct about the Feedback stage of the data science methodology?

Answer: Feedback is essential to the long-term viability of the model

Explanation: The feedback stage is crucial because it helps identify areas for improvement and refine the model based on real-world performance, ensuring it continues to meet business needs.

Q2. Which of the following statements represents the essential characteristics of the data science methodology?

Answer: Data Science Methodology is a highly iterative process. At any point in the methodology, data scientists can decide to repeat a stage or revisit a prior stage and work forward from that previous stage.

Explanation: The data science process is flexible and iterative, allowing data scientists to revisit previous stages as new insights or issues arise during the project.

Q3. For predictive models, a test data set, which is similar to but independent of the training set, is used to determine how well the model predicts outcomes—using a training or test. A test data set happens during which stage in Foundational Data Science Methodology?

Answer: Model Evaluation

Explanation: The test data set is used in the Model Evaluation stage to assess the model’s accuracy and performance, ensuring it generalizes well to new, unseen data.

Module 03: Graded Quiz: From Deployment to Feedback

Q1. Which of the following options is essential for ensuring the relevance and usefulness of the answer derived from a data science model?

Answer: The availability of data

Explanation: The relevance and usefulness of a model’s answer depend heavily on the quality, quantity, and availability of data, which allows the model to make accurate and meaningful predictions.

Q2. In a business scenario, who are the stakeholders that help make the model’s answer relevant?

Answer: Solution owners, marketing, application developers, and IT administration.

Explanation: Stakeholders involved in the business decision-making process—such as solution owners, marketing, and IT—play a crucial role in ensuring the model addresses real business needs and is applicable to the operational environment.

Q3. Which of the following statements describes how data scientists refine the model after the initial deployment and feedback stages?

Answer: By incorporating information about participation and possibly refining with detailed pharmaceutical data.

Explanation: Data scientists refine the model after deployment by incorporating real-world data, including additional insights and details, to improve its accuracy and effectiveness.

Q4. How does the data science methodology ensure continuous improvement?

Answer: By incorporating feedback and making adjustments

Explanation: Continuous improvement in data science is driven by feedback from stakeholders and real-world application, which allows for adjustments to be made to improve model performance.

Q5. What is the primary purpose of storytelling in data science?

Answer: To communicate complex findings and insights effectively

Explanation: Storytelling in data science is used to convey complex insights in a clear, understandable manner, ensuring that stakeholders can make informed decisions based on the data.

Data Science Methodology Coursera Final Quiz Answers

Q1. The first stage of the data science methodology is ___________________.

Answer: Business Understanding

Explanation: The first stage of the data science methodology is Business Understanding, where the focus is on clearly defining the business problem and understanding the objectives from a business perspective.

Q2. Why is Business Understanding an important stage in the data science methodology?

Answer: This stage clearly defines the problem and the needs from a business perspective.

Explanation: Business Understanding is crucial because it sets the foundation for the entire project by ensuring that the data science work is aligned with solving the right business problem.

Q3. During which stage can data scientists apply descriptive statistics and visualization techniques to the data set to assess the content, quality, and initial insights about the data?

Answer: The Data Collection stage

Explanation: In the Data Collection stage, data scientists explore and assess the data for quality, consistency, and completeness using descriptive statistics and visualization techniques.

Q4. Training sets are used for what purpose?

Answer: Predictive modeling

Explanation: Training sets are used in predictive modeling to “teach” the model to recognize patterns based on historical data, so it can make predictions on new, unseen data.

Q5. A false-positive is what type of error?

Answer: Type I error

Explanation: A Type I error, also known as a false-positive, occurs when the model incorrectly identifies a positive result when there isn’t one.

Q6. During the Data Preparation stage, data scientists take which of the following actions?

Answer: Address missing values

Explanation: In the Data Preparation stage, data scientists clean the data by addressing issues such as missing values, outliers, and inconsistencies to make it suitable for analysis.

Q7. The final stages of the data science methodology are an iterative cycle between which of the different stages?

Answer: Modeling, Evaluation, Deployment, and Feedback

Explanation: After the initial stages, the methodology enters a cycle where the model is continuously refined through iterative stages of Modeling, Evaluation, Deployment, and Feedback to improve the model’s performance.

Q8. Data science methodology provides the data scientist with a framework on how to proceed to do what tasks?

Answer: Obtain answers to business questions.

Explanation: The data science methodology provides a structured approach to solving business problems, focusing on obtaining actionable answers that address business needs.

Q9. For which type of analysis do data scientists use descriptive statistics and data visualization techniques?

Answer: Exploratory

Explanation: Descriptive statistics and data visualization techniques are commonly used in exploratory data analysis (EDA) to uncover patterns, trends, and relationships in the data.

Q10. How does storytelling contribute to the work of data scientists?

Answer: Storytelling helps convey data insights and drive actions.

Explanation: Storytelling is essential in data science as it helps translate complex data findings into clear narratives that motivate stakeholders to take informed actions.