Intro. to Data Science

In a Data Science project lifecycle, "Build a Model" stage helps us to QL decide if something belongs to one category or another comment on whether the quality of data is good or not group similar items based on certain parameters evaluate the model to see if its accuracy is enough to meet our requirement

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In a Data Science project lifecycle, the "Build a Model" stage helps us to:

• decide if something belongs to one category or another

• group similar items based on certain parameters

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When it comes to methodical alignment of data in a given business scenario, choose the odd one out. Finding objective observations from the data Extracting evidence from the data Cleaning data by maintaining a consistent font size, color and arrangement of data Finding patterns in the data Reset Save

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The odd one out is:

Cleaning data by maintaining a consistent font size, color and arrangement of data

This is more about data formatting and presentation rather than methodical alignment of data for analysis purposes.

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What is machine learning? Question 3 1. Field of study that gives computers the ability to learn without being explicitly programmed. 2. It is the field of scientific study that concentrates on induction algorithms and on other algorithms that can be said to "learn". Only 1 Only 2 Neither 1 nor 2 Both 1 and 2 Reset Save

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Both 1 and 2

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Which of the following is NOT an example of the step, methodical alignment of data? Predicting user preferences from search history. Predicting trend in sales of different items based upon the distribution of visitors age. Suggesting when to buy a product on the basis of past trends in price. None of the choices 12 15 Reset Save

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None of the choices

All the given options involve using data to predict or suggest future outcomes, which are examples of the methodical alignment of data.

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In the context of structured, unstructured and semi-structured data, choose the odd one from the list. 10 12 Email messages OXML documents Excel documents HTML documents C 13 15 Reset Save

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The odd one out is:

Excel documents

Excel documents are typically considered structured data, while email messages, OXML documents, and HTML documents are examples of semi-structured or unstructured data.

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Which would be a suitable model when an application has to classify people as native english language speakers or not based on their signatures Note: All signatures are in english language. Regression model Clustering model Classification model Reset Save

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Classification model

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What is the possible range of values for a variable (P) representing probability? Question 7 0<P<1 -Infinity <= P <= +Infinity -1 <= P <= 1 -1<P<1 Reset Save

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0 < P < 1

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We need to make machines learn because 00 10 11 12 OBJECTIVE QUESTIONS Question 8 ✔machines can analyze huge data quickly in a cost effective manner as compared to humans. machines can be made to learn quickly such that it can perform task with minimal human intervention like exploring surface of other planets machines can work 24x7x365 with same consistency wherein humans cannot maintain such a consistency over a long period of time. machines can solve almost all types of problems by themselves. Reset Save

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✔ machines can analyze huge data quickly in a cost-effective manner as compared to humans.

machines can be made to learn quickly such that it can perform tasks with minimal human intervention like exploring the surface of other planets.

machines can work 24x7x365 with the same consistency wherein humans cannot maintain such consistency over a long period of time.

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Which of the following is NOT a use case of reinforcement learning? 00 Question 9 10 11 12 Machine being trained to play game of chess. Mobile robot searching for an object in a newly exposed room. 15 Reset Machine identifying a defective bulb in production line. Save

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Machine identifying a defective bulb in the production line.

This is typically a classification problem, not a reinforcement learning use case.

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Which of the following is NOT an application of Data Science? 0 11 12 Question 10 Verifying a portal login using user name and password Verifying voice for authentication Verifying the topic of a text document Verifying the patterns in the weather

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Verifying a portal login using username and password

This task is typically handled by standard authentication methods rather than data science techniques.

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We need to predict the mileage of a car in kilometers per litre, on the basis of its weight and horsepower. What kind of model should be used if we a relationship between these three parameters? Regression model Classification model Clustering model Save 11 12 15 Reset

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Regression model

Since you are predicting a continuous variable (mileage) based on other continuous variables (weight and horsepower), a regression model is appropriate.

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Which of the following is/are true about Data Science? Select all that apply. 11 12 Question 12 It is a cross-functional discipline. It is the empirical synthesis of actionable knowledge from raw data through the complete data lifecycle process. It is about scientific exploration of data to extract meaning or insight. It is about performing science experiment on a given data under controlled environment. Reset

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The following statements are true about Data Science:

• It is a cross-functional discipline.

• It is the empirical synthesis of actionable knowledge from raw data through the complete data lifecycle process.

• It is about scientific exploration of data to extract meaning or insight.

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Consider data related to the census of a country having population around a billion. Which of the following methods would be efficient in computing the weighted average of the population's income for this data? Option 1: Vectorization Methods Option 2: Looping Methods Option 1 only Option 2 only Both option 1 and option 1 Nether option 1 nor option 2

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Option 1 only

Vectorization methods are generally more efficient than looping methods for computing operations on large datasets, such as calculating a weighted average.

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Choose the incorrect option in the context of semi-supervised learning. 11 12 Question 14 All the data would not be labeled. All the data would be labeled.

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All the data would be labeled.

In semi-supervised learning, only some of the data is labeled, while the rest is unlabeled.

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To execute a successful Data Science project, a project need(s) to have: (Select all that apply.) Question 15 a clear, verifiable and quantifiable goal realistic expectations for all stakeholders an unbiased data set covering all examples the right model based on the business scenario

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To execute a successful Data Science project, a project needs to have:

• a clear, verifiable, and quantifiable goal

• realistic expectations for all stakeholders

• the right model based on the business scenario

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