Introduction to Data Analytics

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1st Module Assessment

Data Analysis is a process of?

inspecting data

cleaning data

transforming data

All of the above

Question 2. Which of the following are Benefits of Big Data Processing?

Businesses can utilize outside intelligence while taking decisions

Improved customer service

Better operational efficiency

All of the above

Question 3. Which of the following is correct order of working?

questions->evaluation ->algorithms

evaluation->input data ->algorithms

questions->input data ->algorithms

all of the mentioned

Question 4.Data in ___________ bytes size is called Big Data

Tera

Giga

Peta

Meta

Question 5. Which of the following argument is used to set importance values?

scale

set

value

all of the mentioned

Question 6. Which of the following is not a major data analysis approaches?

Data Mining

Predictive Intelligence

Business Intelligence

Text Analytics

Question 7. Which of the following trade-off occurs during prediction?

Speed vs Accuracy

Simplicity vs Accuracy

Scalability vs Accuracy

None of the mentioned

Question 8. As companies move past the experimental phase with Hadoop, many cite the need for additional capabilities, including _______________

Improved data storage and information retrieval

Improved security, workload management, and SQL support

Improved data warehousing functionality

Improved extract, transform and load features for data integration

Question 9. Which of the following is the valid component of the predictor?

data

question

algorithm

all of the mentioned

Question 10. Transaction data of the bank is?

structured data

unstructured datat

c.Both A and B

None of the above

11. What is a unit of data that flows through a Flume agent?

Record

Event

Row

Log

Question 12. Which of the following is correct skills for a Data Scientist?

Probability & Statistics

Machine Learning / Deep Learning

Data Wrangling

All of the above

Question 13. What are the different features of Big Data Analytics?

Open-Source

Scalability

Data Recovery

All the above

Question 14. The goal of business intelligence is to allow easy interpretation of large volumes of data to identify new opportunities

FALSE

TRUE

Can not say

Can be true or false

Which of the following is not a major data analysis approaches? Select one: a. Business Intelligence b. Data Mining c. Predictive Intelligence d. Text Analytics

Question 15. Which of the following is not a application for data science?

Recommendation Systems

Image & Speech Recognition

Online Price Comparison

Privacy Checker

Question 16. How many main statistical methodologies are used in data analysis?

Question 17. In descriptive statistics, data from the entire population or a sample is summarized with ?

integer descriptors

floating descriptors

numerical descriptors

decimal descriptors

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2nd Module Assessment

Which of the following is a good way of performing experiments in data science?

Measure variability

Generalize to the problem

Have Replication

All of the mentioned

Question 2. Data visualization is also an element of the broader _____________.

deliver presentation architecture

data presentation architecture

dataset presentation architecture

data process architecture

Question 3. The analysis performed to uncover interesting statistical correlations between associated-attribute-value pairs is called?

Mining of Association

Mining of Clusters

Mining of Correlations

None of the above

Question 4. “Efficiency and scalability of data mining algorithms” issues comes under?

Mining Methodology and User Interaction Issues

Performance Issues

Diverse Data Types Issues

None of the above

Question 5. How do you handle missing or corrupted data in a dataset?

Assign a unique category to missing values

Drop missing rows or columns

Replace missing values with mean/median/mode

All of the above

Question 6. Which of the following approach should be used if you can’t fix the variable?

randomize it

non stratify it

generalize it

none of the mentioned

Question 7. Text Analytics, also referred to as Text Mining?

TRUE

FALSE

Can be true or false

Can not say

Question 8. Data Analysis is a process of?

cleaning data

transforming data

inspecting data

All of the above

Question 9. Data Visualization in mining cannot be done using

Photos

Graphs

Charts

Information Graphics

Question 10. Point out the correct statement.

findLinearColumns will also return a vector of column positions can be removed to eliminate the linear dependencies

findLinearCombos will return a list that enumerates dependencies

the function findLinearRows can be used to generate a complete set of row variables from one factor

none of the mentioned

11. Which of the following is a foundational exploratory visualization package for the R language in pandas ecosystem?

yhat

Seaborn

Vincent

None of the mentioned

Question 12. Which of the following analytical capabilities are provided by information management company?

Stream Computing

Content Management

Information Integration

All of the mentioned

Question 13. In normalization of relations ,the property which is critical and must be achieved is classified as:

non additive join property

additive join property

indepedency reservation property

dependency preservation property

Question 14. The process of analysing relation schemas to achieve minimal redudancy and insertion or update anomolies is classified as

normalization of data

denomination of data

isolation of data

de-normalization of data

Which of the following is the top most important thing in data science?

a. answer

b. question

c. data

d. none of the mentioned

Question 15. A goal of data mining includes which of the following?

To explain some observed event or condition

To confirm that data exists

To analyze data for expected relationships

To create a new data warehouse

Question 16. An operational system is which of the following?

A system that is used to run the business in real time and is based on historical data.

A system that is used to run the business in real time and is based on current data.

A system that is used to support decision making and is based on current data.

A system that is used to support decision making and is based on historical data.

Question 17. A multifield transformation does which of the following?

Converts data from one field into multiple fields

Converts data from multiple fields into one field

Converts data from multiple fields into multiple fields

All of the above

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3rd Module Assessment

1. In descriptive statistics, data from the entire population or a sample is summarized with ?

numerical descriptors

integer descriptors

decimal descriptors

floating descriptors

Question 2. A__________ begins by hypothesizing a sentence (the symbol S) and successively predicting lower level constituents until individual preterminal symbols are written.

bottow-up parser

top parser

top-down parser

bottom parser

Question 3. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case?

Feature F1 is an example of nominal variable

Feature F1 is an example of ordinal variable

It doesn’t belong to any of the above category

Both of these

Question 4. In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ?

hyper parameters

optimized parameters

mini-batches

super parameters

Question 5. Different learning methods does not include?

Analogy

Memorization

Deduction

Introduction

Question 6. varImp is a wrapper around the evimp function in the _______ package

plot

numpy

earth

none of the mentioned

Question 7. Which of the following hyper parameter(s), when increased may cause random forest to over fit the data?

1. Number of Trees

2. Depth of Tree

3. Learning Rate

1,2 and 3

2 and 3

1 and 2

Only 2

Question 8. Which of the following is true about regression analysis?

describing associations within the data

answering yes/no questions about the data

estimating numerical characteristics of the data

modeling relationships within the data

Question 9. Fit a straight line y=a+bx into the given data: (x,y):(5,12)(10,13)(15,14)(20,15)(25,16).

y=1.1+0.2x

y=11

y=11+0.2x

y=0.2x

Question 10. What would be the probability of an event ‘G’ if H denotes its complement, according to the axioms of probability?

P (G) = 1 / P (H)

P (G) = 1 – P (H)

P (G) = 1 + P (H)

P (G) = P (H)

11. The equation of the regression line is y = 5x + 3. Predict y when x = 8.

None

Question 12. Mutually Exclusive events ___________

Contain all common sample points

Contain all sample points

Does not contain any common sample point

Does not contain any sample point

Question 13. Can we perform linear regression with a neural network?

Yes, we can

Partially we can

No, we can not

None

Now situation is same as written in previous question(under fitting).Which of following regularization algorithm would you prefer? Select one:

a. L2

b. L1

c. All

d. None of these

Question 14. The expected value of a discrete random variable ‘x’ is given by ___________

P(x)

∑ x P(x)

∑ P(x)

Question 15. Which of the following function tracks the changes in model statistics?

findTrack

varImpTrack

varImp

none of the mentioned

Question 16. Randomly assigning treatment to experimental units allows:

Population inference

Causal inference

Both types of inference

Neither type of inference

Question 17. A national random sample of 20 ACT scores from 2010 is listed below. Calculate the sample mean and standard deviation. 29, 26, 13, 23, 23, 25, 17, 22, 17, 19, 12, 26, 30, 30, 18, 14, 12, 26, 17, 18

20.85, 5.94

20.50, 5.79

20.50, 5.94

20.85, 5.79

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4th Module Assessment

Which of the following is a disadvantage of decision trees?

Factor analysis

Decision trees are prone to be overfit

Decision trees are robust to outliers

None of the above

Question 2. That strategies can help reduce overfitting in decision trees?

(i) Enforce a maximum depth for the tree

(ii) Enforce a minimum number of samples in leaf nodes

(iii) Make sure each leaf node is one pure class

(IV) Pruning

All

(i), (iii), (iv)

(i), (ii) and (iii)

None

Question 3. Factors which affect the performance of learner system does not include

Type of feedback

Representation scheme used

Training scenario

Good data structures

Question 4. Which of the following is a reasonable way to select the number of principal components “k”?

Use the elbow method

Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer)

Choose k to be the smallest value so that at least 99% of the varinace is retained

Choose k to be the largest value so that 99% of the variance is retained

Question 5. Which of the following options is/are true for K-fold cross-validation?

Increase in K will result in higher time required to cross validate the result.

Higher values of K will result in higher confidence on the cross-validation result as compared to lower value of K.

If K=N, then it is called Leave one out cross validation, where N is the number of observations.

1 and 2

1 and 3

1,2 and 3

2 and 3

Question 6. What kind of learning algorithm for “Facial identities or facial expressions”?

Prediction

Generating Patterns

Recognizing Anomalies Answe

Recognition Patterns

Question 7. When performing regression or classification, which of the following is the correct way to preprocess the data?

PCA -> normalize PCA output -> training

Normalize the data -> PCA -> normalize PCA output -> training

Normalize the data -> PCA -> training

None of the above

Question 8. Which of the following is/are one of the important step(s) to pre-process the text in NLP based projects?

1. Stemming

2. Stop word removal

3. Object Standardization

1 and 2

1 and 3

1,2 and 3

All of them

Question 9. Which of the following clustering algorithms suffers from the problem of convergence at local optima?

K- Means clustering

Hierarchical clustering

Diverse clustering

All of the above

Question 10. What are the advantages of neural networks over conventional computers?

(i) They have the ability to learn by

(ii) They are more fault

(iiI) They are more suited for real time operation due to their high „computational‟

Only (i)

(i) and (iii)

All

(i) and (ii)

For clustering, we do not require-

a. Unlabeled data

b. Numerical data

c. Labeled data

d. Categorical data

11. What is a support vector?

The average distance between all the data points

The distance between two boundary data points

The distance between any two data points

The minimum distance between any two data points

Question 12. On which data type, we can not perform cluster analysis?

Multimedia data

Text data

Time series data

None

Question 13.Which of the following is a lazy learning algorithm?

KNN

Decision tree

SVM

All of the above

Question 14. Which version of the clustering algorithm is most sensitive to outliers?

K-means clustering algorithm

K-medians clustering algorithm

K-modes clustering algorithm

None

Question 15. Which of the following is the best algorithm for text classification?

Naive Bayes

Random forest

KNN

Decision tree

Question 16. Support Vector Machine is-

a lazy learning classifier

a discriminative classifier

a probabilistic classifier

None

Question 17. What is the most widely used distance metric in KNN?

Perpendicular distance

Manhattan distance

Euclidean distance

All of the above

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5th Module Assessment

Let the following state-reward transactions are observed if a fixed policy is applied on some MDP with two state A,B. What will the value function for this when TD(O) is applied on the data,

A,2,B,3,A,4,B,3,A,3

B,4,B,3,A,9

Select one:

V(A) = 55/7, V(B) = 64/7

V(A) = 60/7, V(B) = 66/7

V(A) = 37/7, V(B) = 26/7

V(A) = 57/7, V(B) = 55/7

Question 2. The multi-armed bandit problem is a generalized use case for-

Unsupervised learning

Supervised learning

Reinforcement learning

All of the above

Question 3. A definition of a concept is if it recognizes all the instances of that concept

Consistent

Complete

Constant

None of these

Question 4. Iteration is also called as ________

Self-correcting process

Accurate process

Approximate process

Rounding off process

Question 5. Which of the following is true about reinforcement learning?

The target of an agent is to maximize the rewards

The agent gets rewards or penalty according to the action

It’s an online learning

All of the above

Question 6. Case-based learning is

A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.

An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation

Any mechanism employed by a learning system to constrain the search space of a hypothesis

None of these

Question 7. What is the parameter of analysis in reinforcement learning?

Number of requests during wake cycle

Number of processes to achieve final outcome

Degree of failure

Degree of success

Question 8. Reinforcement learning is

Unsupervised learning

Award based learning

Supervised learning

None

Question 9. What is the difference between TD(O) and Monte-Carlo value function update equations?

MC uses unbiased sample of the return while TD(O) uses biased sample

MC uses biased sample of the return while TD(O) uses sample

For a single update step MC uses reward while TD(()) a sample of the reward

or a single update step MC uses sample of the reward while T D(O) uses reward

Question 10. Which of the factors affect the performance of learner system does not include?

Training scenario

Type of feedback

Good data structures

Representation scheme used

11. A model of language consists of the categories which does not include ________.

structural units

System Unit

data units

empirical units

Question 12. Real-Time decisions, Game AI, Learning Tasks, Skill Aquisition, and Robot Navigation are applications of which of the folowing

Unsupervised Learning: Clustering

Unsupervised Learning: Regression

Supervised Learning: Classification

Reinforcement Learning

Question 13. Concept learning inferred a ———– valued function from training examples of its input and output.

Hexadecimal

Decimal

Boolean

All of the above

Question 14. ________ produces the relation that has attributes of Ri and R2

Difference

Cartesian product

Intersection

Product

SET concept is used in:

a. Relational Model

b. Hierarchical Model

c. Network Model

d. None of these

Question 15. Which one of the following statements is

(a.) Planning can used when MDP parameters are unknown but sample can taken

(b.) Learning can be used when MDP parameters are unknown but samples can taken

(c.) Planning can used when a model is present even if sampling is not allowed

(d.) Learning can when a model is present even if sampling is not allowed

Select one:

(a.)

(a.) and (b.)

(b.) and (c.)

(d.) and (c.)

Question 16. Assertion: Having a simulator/model is an advantage when using rollouts based methcds

Reason: Multiple trajectories can sampled from the model from any given state

(a.) Assertion and Reason are both true and Reason is a explanation of Assertion

(b.) Assertion and Reason are true and Reason is not a correct explanation of Assertion

(c.) Assertion is true but Reason is false

(d.) Assertion and Reason are falæ

Select one:

(a.)

(a.) and (b.)

(b.) and (c.)

(d.) and (c.)

Question 17. Assertion: a-learning can use asynchronous samplæ from different policies to update Q values.

Reason: a-leaming is an online learning algorithm explanation of Assertion

(a.) Assertion and Reawn are true and Reason is not a correct explanation of Assertion

(b.) Assertion and Reason are both true and Reason is a correct “

(c.) Assertion is false but Reason is true” (d.) Assertion is true but Reason is false

(a.)

(a.) and (b.)

(b.) and (c.)

(d.) and (c.)

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Assignment 2

Case Study:

The oil and gas industry uses predictive analytics in many different ways to ensure efficient, safe, and clean extraction, processing, and delivery of their product. While shale oil and gas are abundant in the US, they are difficult to find and extract safely. Horizontaldrilling and fracking are expensive and possibly cause environmental damage. They are also relatively inefficient. As a result, some of the biggest oil and gas corporations are using prescriptive analytics to help deal with and minimize these problems

Question: How does Bigdata helps to oil and gas industry?

Analytics of gas and oil field data

Simulation of weather data

Drilling

Extration of oil

Question 2. The mapping or classification of a class with some predefined group or class is known as

Data Characterization

Data Discrimination

Data Set

Data Sub Structure

Question 3. In Model based learning methods including oil and gas, an iterative process takes place on the ML models that are built based on various model parameters, called ?

mini-batches

super parameters

optimized parameters

hyper parameters

Question 4. Data Analysis of oil and gas are a process of?

inspecting data

cleaning data

transforming data

All of the above

Question 5. Which of the following is not a major data analysis approaches of oil and gas ?

Business Intelligence

Data Mining

Predictive Intelligence

Text Analytics

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Introduction to Data Analytics

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