Understanding and visualizing data with Python

Course syllabus

Week 1 - Introduction to data
Week 2 - Univariate data
Week 3 - Multivariate data
Week 4 - Populations and samples

Week 1

Data can be numbers, images, words, audio
Two key types of data: Organic/process data, "Designed data collection"
i.i.d. means independent and identically distributed
In case data is not i.i.d., dependencies and differences need to be accounted for in analysis.
variable types

continuous vs discrete
ordinal vs nominal
Quantitative discrete variables are numeric, measurable quantities with a set range of countable values
Nominal variables consist of groups or names in which there is no inherent ordering.
Ordinal variables consist of groups or names with an inherent ordering or ranking.
Data types in python
Introduction to libraries and data management

categorical data, tables, bar charts and pie charts
histograms: shape, center, spread, outliers
numerical summaries: Min, 1st quartile(25%), Median(50%), 3rd quartile(75%), Max
standard score (empirical rule) 68-95-99.7 rule
$standard score = \frac{observation-mean}{standard deviation}$
Boxplots
Boxplots can hide gaps and clusters
Seaborn library(sis)

sns.distplot().set()
sns.boxplot()

Gathering multivariate categorical data
Two way or contingency table
Marginal and conditional distribution
Two univariate bar chart, side by side bar chart, stacked bar chart, Mosaic plot
Association type: linear, quadratic, no association
Positive linear association, negative linear association
Association strength(weak, moderate, strong) - measured by Pearson correlation ( $R$ or $\rho$ ), number between -1 and 1
Correlation does not imply causation
Simpson's paradox
Multivariate data selection
Multivariate distributions

Sampling from well-defined populations
Option 1: Conducting a population census
Option 2: Probability sampling
Option 3: Non-probability sampling
Probability sampling
Simple random sampling
Complex samples
Non probability sampling
Sampling distribution
Sampling variance
A sampling distribution is the distribution of all possible estimates that would arise from hypothetical repeated sampling, and larger sample sizes will result in a sampling distribution with less variance, meaning that estimates are more precise.
Making population inference based on only one sample
Inference for non-probability samples
Complex samples (stratification)
The empirical rule of distribution

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