Week 1 Continued

Question 1

Cleaning data means removing __________, dealing with _______ data, and resolving _________ data

Answer 1

duplicates, dealing with missing data, and resolving incomplete

Question 2

Name the 5 ways to handle missing data

Answer 2

1. Delete the row
2. Replace it with the Mean/Median/Mode
3. Create a new Category for missing values (I.E. ‘Unknown’)
4. Predict the missing values
5. Use an algorithm to produce a estimated result (kNN or Random forest work)

Question 3

Name the 3 categories of data

Answer 3

Structured, Unstructured, Semi-structured

Question 4

Explain structured data

Answer 4

Every element shares the same field. Ex: DBs, objects

Question 5

Explain unstructured data

Answer 5

No common structure. News articles, websites, videos, audio, photographs.

They’re all the same thing but there’s no agreed upon format or rules

Question 6

Explain semi-structured

Answer 6

Some structure, but it’s not common.

Ex: XML, JSON

Question 7

Categorical and numerical are ____ ____s

Answer 7

data types

Question 8

Nominal and ordinal are ___________ data types

Answer 8

Categorical

Question 9

discrete and continuous are _________ data types

Answer 9

numerical

Question 10

Example of nominal.

Answer 10

Male or female

Question 11

Example of ordinal.

Answer 11

Strongly agree, agree, neutral, disagree, strongly disagree

Question 12

T/F - Ordinal answers must have a gradual order

Answer 12

True

Question 13

Explain discrete

Answer 13

Values must be distinct and separated, cannot be measured

Ex. # of students in class, # of tickets sold

Question 14

Explain continuous

Answer 14

Measured, cannot be counted

Ex. Height, salary

Question 15

Explain Sample vs Population

Answer 15

Sample contains a subset of the population

Population always contains all members of a given group

Question 16

How do you get the variance?

Answer 16

Standard deviation^2

Question 17

Stopped at distributions

Question 18

Name this: When the probability of an event is equal (Heads or tails)

Answer 18

Uniform

Question 19

Name this: Values are centered on a mid-point but but decrease as you get farther from the mean (I.E. grades)

Answer 19

Gaussian

Question 20

What is it called if μ = 0 and 𝜎^2 = 1

Answer 20

Standard Normal Distribution

Question 21

Example of rank statistics

Answer 21

95th percentile. It’s where a number compares to the rest of the data.

Question 22

Covariance computes the strength and direction of ___ ____ of _______. Do they get larger together? Do they have an inverse relationship? No relationship?

Answer 22

two sets of values.

Question 23

Name this: A number between 1 and -1, this tells you the strength of a relationship

Answer 23

Correlation