Sampling in Python
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Learn to draw random, stratified and cluster samples in Python, then use sampling and bootstrap distributions to gauge the uncertainty of your estimates.
Sampling lets an analyst draw conclusions about a whole population from a subset of its records, and it underpins statistical inference and hypothesis testing. A sample picked for convenience can mislead, so the work here centers on drawing random, systematic, stratified and cluster samples in Python, checking how far a sample statistic strays from the population value, and using resampling to put a range of uncertainty around an estimate.
What You Will Achieve
- Pick a random sampling approach that suits a dataset and carry it out with pandas and NumPy
- Recognize when a convenience sample makes findings hard to generalize to the wider population
- Judge how close a sample estimate sits to the population value and how sample size changes that gap
- Resample with replacement to estimate variation and report a confidence interval for a population statistic
Program Overview
Sampling Basics and Pseudo-Random Numbers
Why analysts sample at all, how point estimates are calculated, and where convenience samples and computer-generated randomness can weaken an analysis.
- Reasons for sampling and point estimates
- Simple sampling with pandas and NumPy
- Convenience sampling and generalizable findings
- True randomness, pseudo-random numbers and random seeds
Four Random Sampling Methods
Practice with each random design in Python, including when systematic sampling is safe to use and how the designs compare on the same data.
- Simple random and systematic sampling
- Proportional and equal counts stratified sampling
- Weighted random sampling
- Cluster sampling and comparing point estimates
Sampling Distributions, Bootstrapping and Confidence Intervals
Measuring the accuracy and spread of estimates through replicated samples, then resampling a single sample to estimate variation in an unknown population.
- Relative error and sample size
- Exact and approximate sampling distributions
- Standard errors and the Central Limit Theorem
- Bootstrap distributions and confidence intervals
Certificate Awarded (Paid)
Job Outlook
Sampling and resampling are routine in survey analysis, experimental design and A/B testing, tasks that come up often in data analysis and data science work.
Good for: Analysts working with survey or experiment data in Python who want sound ways to estimate population figures and state how uncertain they are.
Keep in mind: Hypothesis testing is named as a use of sampling, but the chapters end with bootstrap confidence intervals and do not teach the tests themselves.
Bottom line: A practical run through four ways to sample data in Python, with sampling and bootstrap distributions to show how far an estimate can be trusted.
Frequently Asked Questions
Which datasets do the exercises use?
The exercises draw on coffee ratings, Spotify songs and employee attrition data, which serve as populations for drawing samples and estimating population statistics.
How does a sampling distribution differ from a bootstrap distribution?
A sampling distribution comes from drawing many samples from the population itself, while a bootstrap distribution resamples one sample with replacement to estimate variation when the population is unknown. The final chapter compares the means and standard deviations the two produce.
How does the course handle randomness in Python?
The first chapter separates true randomness from the pseudo-random numbers a computer generates, then has you generate random numbers and work with random seeds before the chapter on the four sampling methods begins.
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