intro to data science and analytics

Question 1

new techniques to solve problems

Answer 1

data science and analytics

Question 2

t/f: data scientis is the sexiest job of the 21st century

Answer 2

t

Question 3

According to Glassdoor, data scientists earn a base pay of __ a year, on the average

Answer 3

$116,840

Question 4

roles in analytics

Answer 4

collector / data steward
data engineer
business analyst
modeler / data scientist

Question 5

data science, due to its __ nature, requires an intersection of abilities

Answer 5

interdisciplinary

Question 6

data science skillset: necessary for working with massive amounts of electronic data that must be acquired, cleaned, and manipulated

Answer 6

hacking skills

Question 7

data science skillset: allows a data scientist to choose appropriate methods and tools in order to extract insight from data

Answer 7

math and statistics knowledge

Question 8

data science skillset: crucial for generating motivating questions and hypotheses and interpreting results

Answer 8

substantive expertise

Question 9

data science skillset: lies at the intersection of knowledge of math and statistics with substantive expertise in a scientific field

Answer 9

traditional research

Question 10

data science skillset: stems from combining hacking skills with math and statistics knowledge, but does not require scientific motivation

Answer 10

machine learning

Question 11

data science skillset: hacking skills combined with substantive scientific expertise without rigorous methods can beget incorrect analyses

Answer 11

danger zone

Question 12

data science vs. data analytics:
scope - macro

Answer 12

data science

Question 13

data science vs. data analytics:
scope - micro

Answer 13

data analytics

Question 14

data science vs. data analytics:
goal - to ask the right questions

Answer 14

data science

Question 15

data science vs. data analytics:
goal - to find actionable data

Answer 15

data analytics

Question 16

data science vs. data analytics:
major fields - Machine learning, AI, search engine engineering, corporate analytics

Answer 16

data science

Question 17

data science vs. data analytics:
major fields - Healthcare, gaming, travel, industries with immediate data needs

Answer 17

data analytics

Question 18

t/f: bot data science and data analytic uses big data

Answer 18

t

Question 19

mother of invention

Answer 19

necessity

Question 20

goal of report writing

Answer 20

automation

Question 21

year of report writing

Answer 21

1970s

Question 22

year of centralized system

Answer 22

1980s

Question 23

goal of centralized system

Answer 23

ERP (Enterprise Resource Planning) / MIS (Management Info System)

Question 24

year of business intelligence

Answer 24

1990s

Question 25

goal of business intelligence

Answer 25

apps fo everyone

Question 26

t/f: in the 1990s, applications were invented to share and to analyze

Answer 26

f (made to share but not yet to analyze)

Question 27

when was microsoft office for windows released

Answer 27

november 19, 1990

Question 28

year of internet & data mining

Answer 28

2000

Question 29

year of big data & data science

Answer 29

2010

Question 30

t/f: The needs of the industry, as demanded by the fast moving realities of the
present time, also evolve the analytics.

Answer 30

t

Question 31

the value in the data “__” is guided by your knowledge of the domain– not the tools or techniques

Answer 31

haystack

Question 32

combination of all the skillsets needed in data science

Answer 32

analytics

Question 33

evolution of analytics: describes historical data, helps understand how things are going

Answer 33

descriptive

Question 34

evolution of analytics: helps understand unique driver, segmentation, statistical, sensitivity analysis

Answer 34

diagnostic

Question 35

evolution of analytics: forecast future performance events and results

Answer 35

predictive

Question 36

evolution of analytics: analysis that suggest a prescribed action

Answer 36

prescriptive

Question 37

evolution of analytics: proactive action, learn off scale, reason with purpose interact naturally

Answer 37

cognitive

Question 38

finding useful pattern in a data

Answer 38

data mining

Question 39

process of knowledge discovery, machine learning, and predictive analytics

Answer 39

data mining

Question 40

extracting meaningful patterns

Answer 40

data mining

Question 41

building representative models

Answer 41

data mining

Question 42

combination of statistics, machine learning, and computing algorithms

Answer 42

data mining

Question 43

t/f: data mining is about descriptive statistics

Answer 43

f

Question 44

t/f: data mining is not about dimensional slicing

Answer 44

t

Question 45

types of learning models in data mining

Answer 45

supervised
unsupervised

Question 46

types of learning models: directed data mining

Answer 46

supervised

Question 47

types of learning models: undirected data mining

Answer 47

unsupervised

Question 48

types of learning models: the model generalizes the relationship between the input and output variables

Answer 48

supervised

Question 49

types of learning models: the objective is to find patterns in data based on the relationship between data points themselves

Answer 49

unsupervised

Question 50

supervised goups of learning models

Answer 50

classification models
regression models
clustering models

Question 51

unsupervised groups of learning models

Answer 51

clustering models
anomaly detection
time series forecasting
association
text and sentiment analysis

Question 52

t/f: clustering model can be both supervised and unsupervised

Answer 52

t

Question 53

data mining steps

Answer 53

business understanding
data understanding
data preparation
modeling
testing and evaluation
deployment