Midterm 1

Question 1

Data Science Lifecycle Step 1

Answer 1

Frame the problem

Question 2

Data Science Lifecycle Step 2

Answer 2

Collect raw data

Question 3

Data Science Lifecycle Step 3

Answer 3

Process the data

Question 4

Data Science Lifecycle Step 4

Answer 4

Explore the data

Question 5

Data Science Lifecycle Step 5

Answer 5

Perform in depth analysis

Question 6

Data Science Lifecycle Step 6

Answer 6

Communicate results

Question 7

Association

Answer 7

any relation or link

Question 8

Causality

Answer 8

One thing causes the other

Question 9

Categorical

Answer 9

Each value is from a fixed inventory

Question 10

Numerical

Answer 10

Each value is a number (not a code)

Question 11

Values

Answer 11

can be numerical or categorical, and of many subtypes within these

Question 12

Distribution

Answer 12

For each different value of the variable, the frequency of individuals that have that value

Question 13

Randomize

Answer 13

If you assign individuals to treatment and control at random, then the two groups are likely to be similar apart from the treatment
Random =/ = Haphazard … regardless of what the dictionary says (in probability theory)

Question 14

Assignment statements

Answer 14

statements don’t have a value ; they perform an action

Question 15

f(27)

Answer 15

(f- what function to call) (27-argument to the function) “Call f on 27”

Question 16

t.select(label)

Answer 16

constructs a new table with just the specified columns

Question 17

t.drop(label)

Answer 17

constructs a new table in which the specified columns are omitted

Question 18

t.sort(label)

Answer 18

constructs a new table with rows sorted by the specified column

Question 19

t.where(label, condition)

Answer 19

constructs a new table with just the rows that match the condition

Question 20

Integers

Answer 20

an integer of any size,
an int never has a decimal point

Question 21

Floats

Answer 21

has an optional fractional part
always has a decimal point
may use scientific notation
they have limited size (but the limit is huge)
they have limited precision of 15-16 decimal places
after arithmetic the final few decimal places can be wrong

Question 22

string

Answer 22

a set of characters of any length - ‘A’

Question 23

Arrays

Answer 23

A collection of things
sequence of values of the same type (Arrays -> Columns)

Question 24

Ranges

Answer 24

A range is an array of consecutive numbers

Question 25

np.arange(end)

Answer 25

An array of increasing integers from 0 up to end

Question 26

np.arange(start,end)

Answer 26

An array of increasing

Question 27

np.arange(start,end,step)

Answer 27

A range with step between consecutive values

Question 28

NOTE: The range always includes start but excludes end

Question 29

Table.read_table(filename)

Answer 29

reads a table from a spreadsheet

Question 30

Numerical Attribute types

Answer 30

Each value is from a numerical scale
Numerical measurements are ordered
Differences are meaningful

Question 31

Categorical Attribute types

Answer 31

Each value is from a fixed inventory
May or may not have an ordering
Categories are the same or different

Question 32

Use line polts for sequential data if

Answer 32

Your x axis has an order
Sequential differences in y values are meaningful
Theres only one y-value for each x-value
Usually x-axis is time or distance

Question 33

Use scatter plots for non-sequential data

Answer 33

When you’re looking for associations

Question 34

Binning

Answer 34

counting the number of numerical values that lie within rages, called bins

Question 35

Bins

Answer 35

defined by their lower bounds (inclusive)
The upper bound is the lower bound of the next bin

Question 36

Histogram

Answer 36

Chart that displays the distribution of a numerical value / attribute
Uses bins; there is one bar corresponding to each bin
Uses the area principle
The area of each bar is the percent of individuals in the corresponding bin

Question 37

Height formula

Answer 37

(% in bin/width of bin)

Question 38

Area of bar formula

Answer 38

% in bin = Height x width of bin

Question 39

Scatter plot

Answer 39

relation between numerical variables

Question 40

Line graph

Answer 40

sequential data (over time)

Question 41

Bar chart

Answer 41

distribution of categorical data

Question 42

Histogram

Answer 42

distribution of numerical data

Question 43

Grouped Table

Answer 43

One combo of grouping variables per row
Any number of grouping variables
Aggregate values of all other columns in table
Missing combos absent

Question 44

Pivot table

Answer 44

One combo of grouping variables per entry
Two grouping variables: columns and rows
Aggregate values of values column
Missing combos = 0 (or empty string)

Question 45

Probability

Answer 45

Lowest value: 0
Chance of even that is impossible
Highest value: 1 (or 100%)
Chance of event that is certain
Complement: if an event has chance 70%, then the chance that it doesn’t happen is
100% - 70% = 30%
1 - 0.7 = 0.3

Question 46

Equally likely outcomes

Answer 46

P(A) = (number of outcomes that make A happen) / (total number of outcomes)

Question 47

Multiplication Rule

Answer 47

Chance that two events A and B both happen
P(A) = P(A happens) x P(B happens given that A has happened)

Question 48

Addition Rule

Answer 48

If event A can happen in exactly one of two ways, then
P(A) = P(first way) + P(second way)

Question 49

Pivot

Answer 49

Cross-classified according to two categorical variables
Produces a grid of counts or aggregated values
Two required arguments
First (A): variable that forms column labels of grid
Second (B): variable that forms row labels of grid
Two optional arguments (include both or neither)
values = column_label_to_aggregate
collect = function_to_aggregate_with

Question 50

Lists

Answer 50

sequence of values of different types (Lists -> Rows)

Question 51

Groups

Answer 51

collect rows by some column