UNIT 1

Question 1

It is an approach that offers new techniques to solve problems

Answer 1

Data Science and Analytics

Question 2

What are the roles in analytics?

Answer 2

Collector/Data Steward, Data Engineer, Business Analyst, Modeler/Data Scientist

Question 3

Also known as data scientist that models algorithm; makes sure data are correct

Answer 3

Modeler/Data Scientists

Question 4

They are the business experts in the field of data science

Answer 4

Business Analyst

Question 5

It oversees all roles in the data field

Answer 5

Project Manager

Question 6

Hacking skills and Substantive expertise

Answer 6

Danger Zone

Question 7

Substantive expertise and Math and Statistics Knowledge

Answer 7

Traditional Research

Question 8

Hacking Skills and Math and Statistics Knowledge

Answer 8

Machine Learning

Question 9

Due to its interdisciplinary nature, it requires an intersection of abilities: hacking skills, math and statistics knowledge, and substantive expertise in a field of science

Answer 9

Data science

Question 10

It is necessary for working with massive amounts of electronic data that must be acquired, cleaned, and manipulated

Answer 10

Hacking skills

Question 11

It allows a data scientist to choose appropriate methods and tools in order to extract insight from data

Answer 11

Math and statistics knowledge

Question 12

In a scientific field, it is crucial for generating motivating questions and hypotheses and interpreting results

Answer 12

Substantive expertise

Question 13

It lies at the intersection of knowledge of math and statistics with substantive expertise in a scientific field

Answer 13

Traditional research

Question 14

Stems from combining hacking skills with math and statistics knowledge, but does not require scientific motivation

Answer 14

Machine Learning

Question 15

Hacking skills combined with substantive scientific expertise without rigorous methods can beget incorrect analyses

Answer 15

Danger zone

Question 16

Scope: Macro

Answer 16

Data Science

Question 17

Goal: To ask the right questions

Answer 17

Data Science

Question 18

Major Fields: Machine learning, AI, Search engine engineering, corporate analytics

Answer 18

Data Science

Question 19

Using Big Data: Yes

Answer 19

Data Science and Analytics

Question 20

Scope: Micro

Answer 20

Data Analytics

Question 21

Goal: To find actionable data

Answer 21

Data Analytics

Question 22

Major FIelds: Healthcare, gaming, travel, industries with immediate data needs

Answer 22

Data Analytics

Question 23

It is the mother of invention

Answer 23

Necessity

Question 24

History: Report Writing; Goal is automation

Answer 24

1970s

Question 25

History: Centralized System; Goal is to have Enterprise Resource Planning or Management Info System

Answer 25

1980s

Question 26

History: Business Intelligence; Goal is apps for everyone, applications for personal use were invented and made to share

Answer 26

1990s

Question 27

History: Internet Data and Mining

Answer 27

2000s

Question 28

History: Big data and data science used for real time analysis

Answer 28

2010s

Question 29

T/F: The needs of the industry, as demanded by the fast moving realities of the present time, also evolve the analytics

Answer 29

TRUE

Question 30

T/F: The value in the data "haystack" is not guided by your knowledge of the domain- but of the tools or techniques

Answer 30

FALSE

Question 31

T/F: Finding that value- the combination of all the skillsets that you need- is data science

Answer 31

FALSE

Question 32

Evolution: Describes historical data; Helps understand how things are going

Answer 32

Descriptive

Question 33

Evolution: Helps understand unique drivers; Segmentation, Statistical & Sensitivity analysis

Answer 33

Diagnostic

Question 34

Evolution: Forecast future performance, events, and results

Answer 34

Predictive

Question 35

Evolution: Analysis that suggest a prescribed action

Answer 35

Prescriptive

Question 36

Evolution: Proactive action; Learn at scale; Reason with purpose interact naturally

Answer 36

Cognitive

Question 37

Medical image analysis, Machine learning in disease diagnosis, Genetics and Genomics, Drug development, Virtual assistance for customer support

Answer 37

Data Science and Analytics in Healthcare

Question 38

Finding useful pattern in a data; It is the process of knowledge discovery, machine learning and predictive analytics

Answer 38

Data Mining

Question 39

Which of the following is NOT about data mining?

Answer 39

Descriptive statistics, Exploratory visualization, Dimensional slicing, Hypothesis testing, Queries

Question 40

It is a type of learning model in data mining which is directed data mining. The model generalizes the relationship between the input and output

Answer 40

Supervised

Question 41

It is a type of learning model in data mining which is an undirected data mining. The objective of this class of data mining techniques is to find patterns in data based on the relationship between data points themselves

Answer 41

Unsupervised

Question 42

What are the groups of learning models in data mining?

Answer 42

Classification, Regression, Clustering, Anomaly Detection, Time Series Forecasting, Association, Text and Sentiment Analysis

Question 43

What are the steps in data mining by CRISP-DM Framework?

Answer 43

Business understanding, Data understanding, Data preparation, Modeling, Testing and Evaluation, Deployment