Data Analytics

Question 1

iloc example

Answer 1

df.iloc[1,2] - single cell (200)
df.iloc[2] - Entire row (1000, 2000, 3000, 4000)

Question 2

loc example

Answer 2

Same as iloc but with string headings
e.g.
df.loc[2,’a’]

Question 3

describe

Answer 3

Summary of a single column

df.[‘a’].describe()

Question 4

Mean

Answer 4

The total of the figures, divided by the number of individual figures

1,2,2,3,2,4
Mean: 13/6 = 2.16666

Question 5

Median

Answer 5

The middle point

1,2,2,3,2,4 -> 1,2,2,2,3,4
Median: 2

Question 6

Mode

Answer 6

The most common Figure

1,2,2,3,2,4
Mode : 2

Question 7

Inter Qaurtile range

Answer 7

The Difference between the First and Third Qaurtile Values
Q1: 10
Q3: 50

IQR: 40

Question 8

Nominal

Answer 8

Categorisation without order e.g. the books are in: English, French, German etc.

Distinctiveness ( = and != )

Question 9

Ordinal

Answer 9

Categorisation with order e.g. the coffee was: Good, Medium, Bad

Distinctiveness ( = and != )
Order ( <,<=,>,>= )

Question 10

Interval

Answer 10

Scale with an arbitrary zero value e.g. temperature, shoe size, dates

Distinctiveness ( = and != )
Order ( <,<=,>,>= )
Addition ( + and - )

Question 11

Ratio

Answer 11

Scale with a non-arbitrary zero value e.g. distance, age, speed etc.

Distinctiveness ( = and != )
Order ( <,<=,>,>= )
Addition ( + and - )
Multiplication ( * and / )

Question 12

NOIR

Answer 12

Qualitative:
Nominal
Ordinal

Quantatitive:
Interval
Ratio

Question 13

DOAM

Answer 13

Distinctiveness (=, !=)
Ordering (<, <=, >, >=)
Addition (+, -)
Multiplication (*, /)

Question 14

Nominal : Binary

Answer 14

1/0, On/Off, Yes/No, True/False

Question 15

Normal Distribution

Answer 15

Standard Bell Curve

Mode, mean and Median are in the centre

Question 16

Left skewed

Answer 16

Tail is on the left, Hump on the right

Left: Mean
Middle: Median
Right: Mode

“You’re mean when you walk away”

Question 17

Right skewed

Answer 17

Tail on the right, hump on the left

Left: Mode
Middle: Median
Right: Mean

“You’re mean when you walk away”

Question 18

Tuple

Answer 18

stores data but cant be changed

myTuple = (1,2,3)

Question 19

List in relation to tuple

Answer 19

Like a tuple but can be changed

myList = [1,2,3]

Question 20

List

Answer 20

ordered collection of elements supporting mixed data types

Question 21

Array

Answer 21

similar to a list but all must be of the same type

Question 22

2D array or matrix

Answer 22

a grid of elements with uniform data types

Question 23

DataFrame

Answer 23

two dimensional, potentially tabular data structure with labelled axes, allowing different data types for each column

e.g. SQL, or CSV

Question 24

Measures of Dispersion

Answer 24

Standard Deviation, and Variance

Question 25

Variance

Answer 25

The averages of the squared differences form the mean

Question 26

Standard Deviation in relation to variance

Answer 26

The square root of the variance

Question 27

Standard Deviation (small and large)

Answer 27

Smaller: data points tend closer to the mean Larger: data points have greater variability

Question 28

Correlation

Answer 28

Measures the strength and direction of the linear relationship between two variables

Question 29

Correlation: -1, 1

Answer 29

1 = Perfect positive correlation -1 = Perfect negative correlation

Question 30

Covariance

Answer 30

The degree to which two variables change together in a dataset

Question 31

Strong and Weak Correlation

Answer 31

Strong Correlation: High degree of association between the two variables. Weak Correlation: Low degree of association between the two variables.