Information Load Can Be Reduced by All of the Following Except

Enroll Here: Data Science Methodology

Module 1 – From Problem to Approach

Question ane: Select the correct statement.

• A methodology is an application for a computer program.
• A methodology is a gear up of instructions.
• A methodology is a system of methods used in a particular surface area of study or activeness.
• All of the in a higher place statements are correct.

Question 2: Select the correct argument.

• The data scientific discipline methodology described in this form is just used past certified data scientists.
• The data science methodology described in this course is outlined past John Rollins from IBM.
• The information science methodology described in this course is limited to IBM.
• None of the above statements are right.

Question 3: Select the correct argument.

• The first stage of the data science methodology is data agreement.
• The first stage of the data science methodology is modeling.
• The first stage of the data science methodology is business agreement.
• The first stage of the data science methodology is data collection.

Module 2 – From Requirements to Collection

Question 1: Select the correct statement.

• If a problem is a dish, then data is an answer.
• If a problem is a dish, then data is an ingredient.
• If a trouble is a dish, so data is a list of information.
• None of the above statements are right.

Question two: Select the right statement.

• A data requirement is never refined.
• A data requirement is set in stone.
• A data requirement is the initial set of ingredients.
• None of the to a higher place statements are correct.

Question 3: Select the right statement.

• Data scientists determine how to prepare the data.
• Data scientists identify the data that is required for data modeling.
• Data scientists determine how to collect the data.
• All of the above.

Module 3 – From Understanding to Preparation

Question 1: Select the correct statement well-nigh information training.

• Data grooming involves properly formatting the data.
• Information preparation involves correcting invalid values and addressing outliers.
• Data preparation involves removing duplicate data.
• Data preparation involves addressing missing values.
• All of the above statements are correct.

Question 2: Select the right statement about data agreement.

• Data agreement encompasses removing redundant data.
• Information agreement encompasses all activities related to constructing the dataset.
• Data understanding encompasses sorting the data.
• All of the in a higher place statements nearly data understanding are correct.

Question 3: Select the correct statement almost what data scientists and database administrators (DBAs) do during information preparation.

• During data grooming, data scientists and DBAs identify missing information.
• During data preparation, data scientists and DBAs determine the timing of events.
• During data training, information scientists and DBAs aggregate the information and merge them from unlike sources.
• During data preparation, data scientists and DBAs define the variables to be used in the model.
• All of the above statements are correct.

Module 4 – From Modeling to Evaluation

Question 1: Select the right argument.

• A training set is used for data visualization.
• A training prepare is used for predictive modeling.
• A training set is used for statistical assay.
• A grooming set is used for descriptive modeling.
• None of the above statements are correct.

Question 2: A statistician calls a false-negative, a blazon I mistake, and a fake-positive, a type II error.

• True
• False

Question iii: Select the right statement about model evaluation.

• Model evaluation can include statistical significance testing.
• Model evaluation includes ensuring that the data are properly handled and interpreted.
• Model evaluation includes ensuring the model is designed as intended.
• Model evaluation includes ensuring that the model is working every bit intended.
• All of the above statements are correct.

Module 5 – From Deployment to Feedback

Question 1: The terminal stages of the data scientific discipline methodology are an iterative bike between modelling, evaluation, deployment, and feedback.

• True
• False

Question 2: What is model evaluation used for?

• Assessing the model after getting deployed.
• Assessing the model before getting deployed.
• Determining if the model is good for other uses.
• All of the above.
• None of the higher up.

Question 3: Select the correct statement about the feedback stage of the data scientific discipline methodology.

• Feedback is essential to the long term viability of the model.
• Feedback is not helpful and gets in the way.
• Feedback is non required one time launched.
• None of the to a higher place statements are correct.

Data Scientific discipline Methodology Final Exam Answers

Question ane: Select the correct judgement nigh the data science methodology explained in the course.

• Data science methodology is non an iterative procedure – i does not go dorsum and along between methodological steps.
• Data scientific discipline methodology is a specific strategy that guides processes and activities relating to data science only for text analytics.
• Data scientific discipline methodology always starts with data collection.
• Information science methodology provides the data scientist with a framework for how to keep to obtain answers.
• Data science methodology depends on a specific set of technologies or tools.

Question two: Business agreement is of import in the data science methodology stage. Why?

• Because it shapes the rest of the methodological steps.
• Because it conspicuously defines the problem and the needs from a business perspective.
• Because it ensures that the work generates the intended solution.
• Because it involves domain expertise.
• All of the higher up.

Question 3: A data scientist determines that building a recommender system is the solution for a particular business problem at manus. What stage of the information science methodology does this represent?

• Modeling
• Deployment
• Model evaluation
• Analytic approach
• Data understanding

Question 4: Which of the post-obit represent the two important characteristics of the information scientific discipline methodology?

• It is a highly iterative process and immediately ends when the model is deployed.
• It is not an iterative process and it never ends.
• It has no endpoint considering data collection occurs before identifying the data requirements.
• It immediately ends when the model is deployed because no feedback is required.
• It is a highly iterative process and it never ends.

Question 5: What do data scientists typically use for exploratory analysis of data and to get acquainted with them?

• They apply support vector machines and neural networks as characteristic extraction techniques.
• They begin with regression, nomenclature, or clustering.
• They employ deep learning.
• They employ descriptive statistics and information visualization techniques.
• All of the above.

Question six: Select the correct argument almost data preparation.

• Information preparation cannot be accelerated through automation.
• Data preparation involves dealing with missing improperly coded data and can include using text assay to structure unstructured or semi-structured text data.
• Data training is typically the least time-consuming methodological step.
• All of the above.
• None of the above.

Question vii: Which argument all-time describes the modeling stage of the data science methodology.

• Modeling is followed past the analytic approach phase.
• Modeling may require testing multiple algorithms and parameters.
• Modeling is always based on predictive models.
• Modeling always uses grooming and test sets.
• All of the above.

Question 8: Which of the following statements best describe the model evaluation phase of the data science methodology?

• Model evaluation may entail statistical significance tests, particularly when boosted proof is necessary to justify some of the emerging recommendations.
• Model evaluation is important because it examines how well the model performs in the context of the business trouble.
• Model evaluation entails computing graphs and/or various diagnostic measures such as a confusion matrix.
• Model evaluation is done using a test ready if the model is a predictive one.
• All of the above.

Question 9: What does deploying a model into product represent?

• It represents the end of the iterative process that includes feedback, model refinement, and redeployment.
• It represents the beginning of an iterative procedure that includes feedback, model refinement and redeployment and requires the input of additional groups, such as marketing personnel and concern owners.
• It represents the terminal data science product.
• None of the above.

Question 10: A data scientist, John, was asked to help reduce readmission rates at a local hospital. After some fourth dimension, 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?

• John only provided one model every bit a solution and he should have provided multiple models.
• The scenario is already optimal.
• Fifty-fifty though John only submitted one solution, it might exist a good 1. However, John needed feedback on his model from the infirmary to confirm that his model was able to accost the problem appropriately and sufficiently.
• John's mistake is that he lied in the analytic approach step of the data scientific discipline methodology.
• John withal needed to collect more data.

Question 11: A automobile company asked a data scientist to determine what blazon of customers are more than likely to purchase their vehicles. Withal, 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 information technology for modeling?

• Characteristic applied science.
• Transforming the information into more useful variables.
• Combining the information from the diverse sources.
• Addressing missing/invalid values.
• All of the above.

Question 12: Loftier-performance, massively parallel systems tin can exist used to facilitate the following methodological steps.

• Data training and Modeling.
• Modeling merely.
• Deployment.
• Business agreement.
• All of the above.

Question xiii: Data scientists may use either a "top-downwardly" approach or a "lesser-upwardly" arroyo to data scientific discipline. These two approaches refer to:

• "Top-downwardly" arroyo – the data, when sorted, is modeled from the "top" of the data towards the "bottom". "Lesser-up" approach – the data is modeled from the "bottom" of the data to the "top".
• "Superlative-down" arroyo – models are fit before the information is explored. "Bottom-upwards" arroyo – data is explored, and so a model is fit.
• "Summit-downwards" approach – first defining a business problem so analyzing the information to find a solution. "Bottom-upwards" approach – starting with the data, and then coming up with a business problem based on the information.
• "Meridian-down" approach – using massively parallel, warehouses with huge information volumes as the data source. "Bottom-upward" approach – using a sample of small data before using big information.
• All of the above.

Question 14: The post-obit are all examples of rapidly evolving technologies that bear upon data science methodology EXCEPT for?

• Data sampling.
• Automation.
• Text analysis.
• Platform growth.
• In-database analytics.

Question 15: Information understanding involves all of the following EXCEPT for?

• Discovering initial insights about the data.
• Visualizing the data.
• Assessing data quality.
• Understanding the content of the data.
• Gathering and analyzing feedback for cess of the model's operation.

Question 16: For predictive models, a test set, which is similar to – but contained of – the grooming set, is used to determine how well the model predicts outcomes. This is an instance of what step in the methodology?

• Data preparation.
• Deployment.
• Analytic approach.
• Model evaluation.
• Data requirements.

Question 17: "When ______ data is available (such as customer call center logs or physicians' notes in unstructured or semi-structured format), _______ analytics tin be useful in deriving new structured variables to enrich the ready predictors and improve model accuracy." Which of the post-obit most appropriately fills in the blanks?

• text; text
• marketplace; statistical
• big; digital
• highly structured; text
• text; predictive

Question 18: Typically in a predictive model, the grooming set and the examination set are very unlike and independent, such as having a different prepare of variables or construction.

• Truthful
• False

Question 19: Data scientists may frequently return to a previous stage to make adjustments, as they learn more than most the data and the modeling.

• True
• Faux

Question 20: Why should data scientists maintain continuous communication with business sponsors throughout a project?

• So that business sponsors can provide domain expertise.
• So that business sponsors tin ensure the work remains on track to generate the intended solution.
• So that business sponsors can review intermediate findings.
• All of the above.
• None of the above.

robbinsshisho89.blogspot.com

Source: https://priyadogra.com/data-science-methodology-cognitive-class-answers/

Share this post