Data Science

Front 1

Observational study

Back 1

I.e. Correlations

Front 2

Sample study

Back 2

Randomly sample source. More sample the better. To estimate. To get a sense of something.

Front 3

Experimental study

Back 3

Experimental group, control group. Randomly put in either group. Find causality.

Front 4

Define correlations and causality

Back 4

Todo

Front 5

What is the mean sign

what is the standard deviation sign

Back 5

µ (mu mean sign)

σ (sigma standard deviation)

Front 6

Set: X \ Y means?

Back 6

X not in Y.

X = {1,2}

Y = {1,2,3}

X \ Y = {}

Front 7

P(A or B) = P(A) + P(B) - ?

Back 7

P(A or B) = P(A) + P(B) - P(A and B)

Front 8

Empirical Rule?

Back 8

68% - 95% - 99.7% for normal distribution problems

Front 9

how to calculate z-score? define what is a z-score.

Back 9

z = (X - population mean) / standard deviation

z-score or standard score indicates how many standard deviation an element is from the mean

Front 10

When two events, A and B, are dependent, the probability of both occurring is:

P(A and B) = ?

Back 10

P(A and B) = P(A) * P(B|A)

P(A and B) = P(B) * P(A|B)

P(B|A) = P(A and B) / P(A)

Front 11

Data Types

Back 11

Discrete Continuous

Ordered Ordinal (S,M,L) Fields (temp)

(values are Quantitative

comparable) (1,2,3)

Unordered Nominal (shape) Cyclic values

Categories (directions, hues)

(nationality)

Front 12

Give some examples of construct and operational definition

Back 12

construct: intelligence, hunger, maturity

operational definition: grams of food consume, number of questions answered correctly

Front 13

Define the core activities for each of the following analysis:

Descriptive Analysis, Inference, Prediction, Design

Back 13

Descriptive analysis: EDA, quantification, summarization, clustering

Inference: estimation, sampling, variability, defining populations

Prediction: machine learning, supervised learning

Design: design experiments

Front 14

What is A/B (split) testing?

Back 14

Compare two variants A and B in a controlled experiment. For example, showing two variant of a web page to similar visitors at the same time to see which gives a better conversion rate.

Front 15

In data analysis, a descriptive question is...

Back 15

one that seeks to summarize a characteristic of a set of data. e.g. proportion of males, mean number of servings of fresh fruits and vegetables per day. There is no interpretation of the result itself as the result is a fact.

Front 16

In data analysis, an exploratory question is...

Back 16

one in which you analyze the data to see if there are patterns, trends or relationships between variables. Also called "hypothesis-generating" analyses because rather than testing a hypothesis, you are looking for patterns that would support proposing a hypothesis. e.g. propose the hypothesis that "among adults, eating at least 5 servings a day of fresh fruit and vegetables is associated with fewer viral illnesses per year"

Front 17

In data analysis, an inferential question is...

Back 17

a restatement of a proposed hypothesis as a question and would be answered by analysing a different set of data. By analysing this different set of data you are both determining if the association you observed in your exploratory analysis holds in a different sample.

Front 18

In data analysis, a predictive question is...

Back 18

one you are less interested in a cause but just to predict a result. For example higher income may be one of the final set of predictors, and you may not know (or even care) why people with higher incomes are more likely to eat diet high in fresh fruits and vegetables, but what is most important is that income is a factor that predicts this behavior.

Front 19

In data analysis, a mechanistic question is...

Back 19

a question that asks how a diet high in fresh fruits and vegetables leads to a reduction in the number of viral illnesses would be a mechanistic question