/Python & Data Analytics/Phase 9

Phase 9Days 97-108

Descriptive Statistics, Probability & Sampling

Quantify variation before making population claims

Phase Goal

Describe distributions, reason with probability, design samples, and communicate representativeness limits.

Progress

Notes

Open the written lectures for this course before checking off the phase topics.

Projects

Rural Broadband Experience Survey

Day 97: Centre, Spread & Shape

Build the first-principles mental model for Mean, median, and mode; name the question it answers and the unit of analysisPredict and trace a tiny example of Mean, median, and mode before inspecting the resultApply Mean, median, and mode to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Mean, median, and mode; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Range, IQR, variance, and standard deviation; name the question it answers and the unit of analysisPredict and trace a tiny example of Range, IQR, variance, and standard deviation before inspecting the resultApply Range, IQR, variance, and standard deviation to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Range, IQR, variance, and standard deviation; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Skew, tails, and modality; name the question it answers and the unit of analysisPredict and trace a tiny example of Skew, tails, and modality before inspecting the resultApply Skew, tails, and modality to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Skew, tails, and modality; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Misleading typical values; name the question it answers and the unit of analysisPredict and trace a tiny example of Misleading typical values before inspecting the resultApply Misleading typical values to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Misleading typical values; diagnose the visible failure and perform the corrective check

Retrieve one recent mechanism and one older prerequisite before consulting notesComplete a guided example, then reconstruct its critical path from a blank artifactSolve a changed task that requires choosing among already-taught toolsRecord the decision changed, failure tested, help used, and adaptive 1/3/7/14/30-day review cue

Day 98: Percentiles & Robust Comparison

Build the first-principles mental model for Quantile definitions; name the question it answers and the unit of analysisPredict and trace a tiny example of Quantile definitions before inspecting the resultApply Quantile definitions to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Quantile definitions; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Z-scores; name the question it answers and the unit of analysisPredict and trace a tiny example of Z-scores before inspecting the resultApply Z-scores to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Z-scores; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Median absolute deviation; name the question it answers and the unit of analysisPredict and trace a tiny example of Median absolute deviation before inspecting the resultApply Median absolute deviation to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Median absolute deviation; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Unlike distributions and percentile confusion; name the question it answers and the unit of analysisPredict and trace a tiny example of Unlike distributions and percentile confusion before inspecting the resultApply Unlike distributions and percentile confusion to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Unlike distributions and percentile confusion; diagnose the visible failure and perform the corrective check

Day 99: Covariance, Correlation & Confounding

Build the first-principles mental model for Covariance direction and scale; name the question it answers and the unit of analysisPredict and trace a tiny example of Covariance direction and scale before inspecting the resultApply Covariance direction and scale to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Covariance direction and scale; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Pearson and rank correlation; name the question it answers and the unit of analysisPredict and trace a tiny example of Pearson and rank correlation before inspecting the resultApply Pearson and rank correlation to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Pearson and rank correlation; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Confounding and restricted range; name the question it answers and the unit of analysisPredict and trace a tiny example of Confounding and restricted range before inspecting the resultApply Confounding and restricted range to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Confounding and restricted range; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Causal claims and influential points; name the question it answers and the unit of analysisPredict and trace a tiny example of Causal claims and influential points before inspecting the resultApply Causal claims and influential points to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Causal claims and influential points; diagnose the visible failure and perform the corrective check

Day 100: Probability Foundations

Build the first-principles mental model for Outcomes, events, spaces, and complements; name the question it answers and the unit of analysisPredict and trace a tiny example of Outcomes, events, spaces, and complements before inspecting the resultApply Outcomes, events, spaces, and complements to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Outcomes, events, spaces, and complements; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Addition and multiplication rules; name the question it answers and the unit of analysisPredict and trace a tiny example of Addition and multiplication rules before inspecting the resultApply Addition and multiplication rules to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Addition and multiplication rules; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Conditional probability and independence; name the question it answers and the unit of analysisPredict and trace a tiny example of Conditional probability and independence before inspecting the resultApply Conditional probability and independence to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Conditional probability and independence; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Base rates and mutually-exclusive confusion; name the question it answers and the unit of analysisPredict and trace a tiny example of Base rates and mutually-exclusive confusion before inspecting the resultApply Base rates and mutually-exclusive confusion to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Base rates and mutually-exclusive confusion; diagnose the visible failure and perform the corrective check

Day 101: Bayes with Natural Frequencies

Build the first-principles mental model for Prior, likelihood, evidence, and posterior; name the question it answers and the unit of analysisPredict and trace a tiny example of Prior, likelihood, evidence, and posterior before inspecting the resultApply Prior, likelihood, evidence, and posterior to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Prior, likelihood, evidence, and posterior; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Frequency tables before formulas; name the question it answers and the unit of analysisPredict and trace a tiny example of Frequency tables before formulas before inspecting the resultApply Frequency tables before formulas to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Frequency tables before formulas; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Diagnostic and quality examples; name the question it answers and the unit of analysisPredict and trace a tiny example of Diagnostic and quality examples before inspecting the resultApply Diagnostic and quality examples to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Diagnostic and quality examples; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Inverse probability errors; name the question it answers and the unit of analysisPredict and trace a tiny example of Inverse probability errors before inspecting the resultApply Inverse probability errors to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Inverse probability errors; diagnose the visible failure and perform the corrective check

Day 102: Random Variables & Expected Value

Build the first-principles mental model for Discrete and continuous variables; name the question it answers and the unit of analysisPredict and trace a tiny example of Discrete and continuous variables before inspecting the resultApply Discrete and continuous variables to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Discrete and continuous variables; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Mass, density, and cumulative views; name the question it answers and the unit of analysisPredict and trace a tiny example of Mass, density, and cumulative views before inspecting the resultApply Mass, density, and cumulative views to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Mass, density, and cumulative views; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Expectation and variance; name the question it answers and the unit of analysisPredict and trace a tiny example of Expectation and variance before inspecting the resultApply Expectation and variance to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Expectation and variance; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Expectation as guarantee; name the question it answers and the unit of analysisPredict and trace a tiny example of Expectation as guarantee before inspecting the resultApply Expectation as guarantee to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Expectation as guarantee; diagnose the visible failure and perform the corrective check

Day 103: Common Distributions

Build the first-principles mental model for Bernoulli and binomial assumptions; name the question it answers and the unit of analysisPredict and trace a tiny example of Bernoulli and binomial assumptions before inspecting the resultApply Bernoulli and binomial assumptions to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Bernoulli and binomial assumptions; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Normal distribution; name the question it answers and the unit of analysisPredict and trace a tiny example of Normal distribution before inspecting the resultApply Normal distribution to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Normal distribution; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Poisson counts and exposure; name the question it answers and the unit of analysisPredict and trace a tiny example of Poisson counts and exposure before inspecting the resultApply Poisson counts and exposure to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Poisson counts and exposure; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Shape-only distribution choice; name the question it answers and the unit of analysisPredict and trace a tiny example of Shape-only distribution choice before inspecting the resultApply Shape-only distribution choice to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Shape-only distribution choice; diagnose the visible failure and perform the corrective check

Day 104: Population, Sample & Selection

Build the first-principles mental model for Target population, frame, sample, and unit; name the question it answers and the unit of analysisPredict and trace a tiny example of Target population, frame, sample, and unit before inspecting the resultApply Target population, frame, sample, and unit to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Target population, frame, sample, and unit; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Coverage, nonresponse, and survivorship; name the question it answers and the unit of analysisPredict and trace a tiny example of Coverage, nonresponse, and survivorship before inspecting the resultApply Coverage, nonresponse, and survivorship to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Coverage, nonresponse, and survivorship; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Random selection; name the question it answers and the unit of analysisPredict and trace a tiny example of Random selection before inspecting the resultApply Random selection to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Random selection; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Large biased samples; name the question it answers and the unit of analysisPredict and trace a tiny example of Large biased samples before inspecting the resultApply Large biased samples to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Large biased samples; diagnose the visible failure and perform the corrective check

Day 105: Sampling Designs

Build the first-principles mental model for Simple random and systematic; name the question it answers and the unit of analysisPredict and trace a tiny example of Simple random and systematic before inspecting the resultApply Simple random and systematic to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Simple random and systematic; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Stratified sampling; name the question it answers and the unit of analysisPredict and trace a tiny example of Stratified sampling before inspecting the resultApply Stratified sampling to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Stratified sampling; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Cluster sampling; name the question it answers and the unit of analysisPredict and trace a tiny example of Cluster sampling before inspecting the resultApply Cluster sampling to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Cluster sampling; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Design effects; name the question it answers and the unit of analysisPredict and trace a tiny example of Design effects before inspecting the resultApply Design effects to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Design effects; diagnose the visible failure and perform the corrective check

Day 106: Sampling Distributions & Standard Error

Build the first-principles mental model for Repeated-sample variation; name the question it answers and the unit of analysisPredict and trace a tiny example of Repeated-sample variation before inspecting the resultApply Repeated-sample variation to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Repeated-sample variation; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Standard error versus standard deviation; name the question it answers and the unit of analysisPredict and trace a tiny example of Standard error versus standard deviation before inspecting the resultApply Standard error versus standard deviation to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Standard error versus standard deviation; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Central limit conditions; name the question it answers and the unit of analysisPredict and trace a tiny example of Central limit conditions before inspecting the resultApply Central limit conditions to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Central limit conditions; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Dependence, skew, and false normality; name the question it answers and the unit of analysisPredict and trace a tiny example of Dependence, skew, and false normality before inspecting the resultApply Dependence, skew, and false normality to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Dependence, skew, and false normality; diagnose the visible failure and perform the corrective check

Day 107: Survey Measurement & Weighting

Build the first-principles mental model for Question wording and recall; name the question it answers and the unit of analysisPredict and trace a tiny example of Question wording and recall before inspecting the resultApply Question wording and recall to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Question wording and recall; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Response scales and missingness; name the question it answers and the unit of analysisPredict and trace a tiny example of Response scales and missingness before inspecting the resultApply Response scales and missingness to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Response scales and missingness; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Weights for representation; name the question it answers and the unit of analysisPredict and trace a tiny example of Weights for representation before inspecting the resultApply Weights for representation to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Weights for representation; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Extreme weights and false precision; name the question it answers and the unit of analysisPredict and trace a tiny example of Extreme weights and false precision before inspecting the resultApply Extreme weights and false precision to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Extreme weights and false precision; diagnose the visible failure and perform the corrective check

Day 108: Phase Project — Broadband Survey

Build the first-principles mental model for Population, frame, and response design; name the question it answers and the unit of analysisPredict and trace a tiny example of Population, frame, and response design before inspecting the resultApply Population, frame, and response design to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Population, frame, and response design; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Weighted and unweighted summaries; name the question it answers and the unit of analysisPredict and trace a tiny example of Weighted and unweighted summaries before inspecting the resultApply Weighted and unweighted summaries to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Weighted and unweighted summaries; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Coverage gaps and sensitivity; name the question it answers and the unit of analysisPredict and trace a tiny example of Coverage gaps and sensitivity before inspecting the resultApply Coverage gaps and sensitivity to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Coverage gaps and sensitivity; diagnose the visible failure and perform the corrective check

Build the first-principles mental model for Advocacy brief with bounded claims; name the question it answers and the unit of analysisPredict and trace a tiny example of Advocacy brief with bounded claims before inspecting the resultApply Advocacy brief with bounded claims to a realistic analyst decision while stating assumptions, units, and boundariesBreak one assumption behind Advocacy brief with bounded claims; diagnose the visible failure and perform the corrective check

Deliver Rural Broadband Experience Survey for a regional digital-inclusion council; support the decision: which communities need measurement follow-up and infrastructure advocacy?Reconstruct the critical workflow without the example open and disclose help usedFind and repair a planted data, logic, or communication failure with evidenceRespond to a changed requirement without being told which technique to useState limitations, non-goals, uncertainty, and what evidence would change the recommendation

Capstone

Rural Broadband Experience Survey

Build for a regional digital-inclusion council. The result must support this decision: which communities need measurement follow-up and infrastructure advocacy? Include reproducible inputs, validation evidence, one tested failure, a changed requirement, and an explicit non-goal.

Reproducible source-to-output workflow
Documented quality checks and assumptions
Decision-ready output for a named user
Independent reconstruction and transfer evidence

Phase Complete!

After this phase, you'll be able to:

Centre, Spread & Shape
Percentiles & Robust Comparison
Covariance, Correlation & Confounding
Probability Foundations
Bayes with Natural Frequencies
Random Variables & Expected Value
Common Distributions
Population, Sample & Selection

Independently deliver Rural Broadband Experience Survey, diagnose a planted failure, explain the decision logic, and transfer the workflow to a changed requirement.

Back to all phases

Phase Goal

Day 97: Centre, Spread & Shape

Mean, median, and mode

Range, IQR, variance, and standard deviation

Skew, tails, and modality

Misleading typical values

Practice, retrieval, and analyst judgment

Day 98: Percentiles & Robust Comparison

Quantile definitions

Z-scores

Median absolute deviation

Unlike distributions and percentile confusion

Practice, retrieval, and analyst judgment

Day 99: Covariance, Correlation & Confounding

Covariance direction and scale

Pearson and rank correlation

Confounding and restricted range

Causal claims and influential points

Practice, retrieval, and analyst judgment

Day 100: Probability Foundations

Outcomes, events, spaces, and complements

Addition and multiplication rules

Conditional probability and independence

Base rates and mutually-exclusive confusion

Practice, retrieval, and analyst judgment

Day 101: Bayes with Natural Frequencies

Prior, likelihood, evidence, and posterior

Frequency tables before formulas

Diagnostic and quality examples

Inverse probability errors

Practice, retrieval, and analyst judgment

Day 102: Random Variables & Expected Value

Discrete and continuous variables

Mass, density, and cumulative views

Expectation and variance

Expectation as guarantee

Practice, retrieval, and analyst judgment

Day 103: Common Distributions

Bernoulli and binomial assumptions

Normal distribution

Poisson counts and exposure

Shape-only distribution choice

Practice, retrieval, and analyst judgment

Day 104: Population, Sample & Selection

Target population, frame, sample, and unit

Coverage, nonresponse, and survivorship

Random selection

Large biased samples

Practice, retrieval, and analyst judgment

Day 105: Sampling Designs

Simple random and systematic

Stratified sampling

Cluster sampling

Design effects

Practice, retrieval, and analyst judgment

Day 106: Sampling Distributions & Standard Error

Repeated-sample variation

Standard error versus standard deviation

Central limit conditions

Dependence, skew, and false normality

Practice, retrieval, and analyst judgment

Day 107: Survey Measurement & Weighting

Question wording and recall

Response scales and missingness

Weights for representation

Extreme weights and false precision

Practice, retrieval, and analyst judgment

Day 108: Phase Project — Broadband Survey

Population, frame, and response design

Weighted and unweighted summaries

Coverage gaps and sensitivity

Advocacy brief with bounded claims

Phase gate: independent delivery and transfer

Rural Broadband Experience Survey

Phase Complete!