Chapter 5

Question 1

four v’s of big data

Answer 1

volume, velocity, variety, and veracity

Question 2

data volume

Answer 2

amount of data created and stored by an organization

Question 3

data velocity

Answer 3

pace at which data is created and stored

Question 4

data variety

Answer 4

different forms data can take

Question 5

data veracity

Answer 5

quality or trustworthiness of data

Question 6

analytics mindset is ability to

Answer 6

ask right questions; extract, transform, and load relevant data; apply appropriate data analytic technique; interpret and share results with stakeholders

Question 7

asking right questions is the 1st step of analytics mindset: establishing objectives that are smart

Answer 7

specific, measurable, achievable, relevant, timely

Question 8

etl process

Answer 8

extracting, transforming, and loading data

Question 9

structured data

Answer 9

data that is highly organized and fits into fixed fields

Question 10

unstructured data

Answer 10

data that has no uniform structure

Question 11

semi structured data

Answer 11

organized in some ways but not fully organized to be inserted into a relational database

Question 12

data warehouses store

Answer 12

structured data

Question 13

data lake

Answer 13

collection of structured, semi structured, and unstructured data in a single location

Question 14

dark data

Answer 14

info the organization has collected and stored that would be useful for analysis but is not analyzed and is ignored

Question 15

data swamps

Answer 15

data repositories that arent accurately documented so the stored data cant be properly identified and analyzed

Question 16

data swamps

Answer 16

data repositories that arent accurately documented so the stored data cant be properly identified and analyzed

Question 17

flat file

Answer 17

text file that contains data from multiple tables or sources and merges it into a single row

Question 18

delimiter

Answer 18

character that marks end of 1 field and beginning of tect

Question 19

text qualifier

Answer 19

2 characters that indicate the beginning and end of a field and tell program to ignore any delimiters contained btw the characters

Question 20

4 steps for transforming data

Answer 20

understand the data and desired outcome
standardize, structure, and clean data
validate data uality and verify data meets data requirements
document the transformation process

Question 21

descriptive analytics

Answer 21

info that results from examination of data to understand the pasts
“what happened?”

Question 22

diagnostic analytics

Answer 22

build on descriptive to answer “why did this happen?”
attempt to determine causal relationships

Question 23

predictive analytics

Answer 23

answers “what might happen in the future?”

Question 24

prescriptive analytics

Answer 24

info that provide a recommendation of what should happen
“what should be done?’

Question 25

common way people interpret results incorrectly is

Answer 25

correlation and causation

Question 26

correlation

Answer 26

tells if 2 things happen at the same timie

Question 27

causation

Answer 27

tells if the occurrence of 1 thing causes the occurrence of the 2nd thing

Question 28

components of sharing results

Answer 28

remember the question that initiated the analytics process, consider audience, data visualization

Question 29

good principles of visualization design

Answer 29

choosing right type of visualization, simplifying presentation of data, emphasizing whats important, representing data ethically

Question 30

automation

Answer 30

application of machines to automatically perform a task once performed by humans

Question 31

what is one tool that can be used to automate etl tasks?

Answer 31

robotic process automation