About Data Science Methodology Course
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision-making process is either lost or not maximized at all too often, we don’t have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
Download All Lab Assements
Lab 01 Assessment: Click here to Download
Lab02 Assessment: Click here to Download
Lab 03 Understanding-to-Preparation: Click here to Download
Lab 04 Modeling-to-Evaluation Assessment: Click here to Download
Data Science Methodology Coursera Quiz Answers
From Problem to Approach
Q1. Select the correct statement.
- A methodology is a system of methods used in a particular area of study or activity.
Q2. The first stage of the data science methodology is data understanding.
Q3. Business understanding is important in the data science methodology stage. Why?
- Because it clearly defines the problem and the needs from a business perspective.
Q4. Which of the following statements about the analytic approach are correct?
- If the question defined in the business understanding deals with exploring relationships between different factors, then a descriptive approach, where clusters of similar activities based on events and preferences are examined, would be the right analytic method.
- If the question defined in the business understanding stage can be answered by determining probabilities of an action, then a predictive model would be the right analytic approach.
Q5. Which machine learning algorithm was implemented in the case study discussed in the videos?
- Decision Tree Classification.
From Requirements to Collection
Q1. 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 a cooking task, where the problem at hand is a recipe, and the data to answer the question is the ingredients.
- Data scientists determine how to collect the data.
- Data scientists identify the data that is required for data modeling.
- Data scientists determine how to prepare the data.
Q6. Database Administrators determine how to collect and prepare the data
From Understanding to Preparation
Q1. Select the correct statement about the Data Understanding stage.
- The Data Understanding stage encompasses all activities related to constructing the dataset.
Q2. In the case study, during the Data Understanding stage, data scientists discovered that not all the congestive heart failure admissions that were expected were being captured. What action did they take to resolve the issue?
- The data scientists looped back to the Data Collection stage, adding secondary and tertiary diagnoses, and building a more comprehensive definition of congestive heart failure admission.
Q3. Select the correct statement about the Data Preparation stage
- All of the above statements are correct.
Q4. Select the correct statement about what data scientists do during the data preparation stage.
- All of the above statements are correct.
Q5. The Data Preparation stage is a very iterative and complicated stage that cannot be accelerated through automation.
From Modeling to Evaluation
Q1. A training set is used for descriptive modeling.
Q2. A statistician calls a false-negative, a type I error, and a false-positive, a type II error.
Q3. Which statement best describes the Modeling Stage of the data science methodology?
- Modeling may require testing multiple algorithms and parameters.
Q4. Model Evaluation includes ensuring that the data are properly handled and interpreted.
Q5. The ROC curve is a useful diagnostic tool for determining the optimal classification model.
From Deployment to Feedback
Q1. The final stages of the data science methodology are an iterative cycle between which of the different stages?
- Modeling, Evaluation, Deployment, and Feedback.
Q2. Select the correct statement about the Feedback stage of the data science methodology.
- Feedback is essential to the long-term viability of the model.
Q3. Deploying a model into production represents the end of the iterative process that includes Feedback, Model Refinement, and Redeployment.
Q4. The data science methodology is a specific strategy that guides processes and activities relating to data science only for text analytics.
Q5. A data scientist determines that building a recommender system is the solution for a particular business problem at hand. This is Represent by the modeling stage of the data science methodology.
Q6. A data scientist, John, was asked to help reduce readmission rates at a local hospital. After some time, John provided a model that predicted which patients were more likely to be readmitted to the hospital and declared that his work was done. Which of the following best describes this scenario?
- Even though John only submitted one solution, it might be a good one. However, John needed feedback on his model from the hospital to confirm that his model was able to address the problem appropriately and sufficiently.
Q7. Data scientists typically use descriptive statistics and data visualization techniques for exploratory analysis of data and to get acquainted with it.
Q8. Data scientists may frequently return to a previous stage to make adjustments, as they learn more about the data and the modeling.
Q9. 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?
- Model Evaluation.
Q10. Why should data scientists maintain continuous communication with business sponsors throughout a project?
- So that business sponsors can review intermediate findings.
- So that business sponsors can ensure the work remains on track to generate the intended solution.
- So that business sponsors can provide domain expertise.
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All Quiz Answers of multiple Specializations or Professional Certificates programs:
Course 5: Python Project for Data Science
Course 6: Databases and SQL for Data Science with Python
Course 7: Data Analysis with Python
Course 8: Data Visualization with Python
Course 9: Machine Learning with Python
Course 10: Applied Data Science Capstone