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course

Sampling in Python

Intermediate

4.4+

Updated 12/2024

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

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PythonProbability & Statistics4 hours15 videos51 exercises4,000 XP35,710Statement of Accomplishment

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Course Description

Sampling in Python is the cornerstone of inference statistics and hypothesis testing. It's a powerful skill used in survey analysis and experimental design to draw conclusions without surveying an entire population. In this Sampling in Python course, you’ll discover when to use sampling and how to perform common types of sampling—from simple random sampling to more complex methods like stratified and cluster sampling. Using real-world datasets, including coffee ratings, Spotify songs, and employee attrition, you’ll learn to estimate population statistics and quantify uncertainty in your estimates by generating sampling distributions and bootstrap distributions.

Prerequisites

Introduction to Statistics in Python

1

Introduction to Sampling

Sampling and point estimates

Reasons for sampling

Simple sampling with pandas

Simple sampling and calculating with NumPy

Convenience sampling

Are findings from the sample generalizable?

Are these findings generalizable?

Pseudo-random number generation

Generating random numbers

Understanding random seeds

2

Sampling Methods

Simple random and systematic sampling

Simple random sampling

Systematic sampling

Is systematic sampling OK?

Stratified and weighted random sampling

Which sampling method?

Proportional stratified sampling

Equal counts stratified sampling

Weighted sampling

Cluster sampling

Benefits of clustering

Performing cluster sampling

Comparing sampling methods

3 kinds of sampling

Comparing point estimates

3

Sampling Distributions

Relative error of point estimates

Calculating relative errors

Relative error vs. sample size

Creating a sampling distribution

Replicating samples

Replication parameters

Approximate sampling distributions

Exact sampling distribution

Generating an approximate sampling distribution

Exact vs. approximate

Standard errors and the Central Limit Theorem

Population & sampling distribution means

Population & sampling distribution variation

4

Bootstrap Distributions

Introduction to bootstrapping

Principles of bootstrapping

With or without replacement?

Generating a bootstrap distribution

Comparing sampling and bootstrap distributions

Bootstrap statistics and population statistics

Sampling distribution vs. bootstrap distribution

Compare sampling and bootstrap means

Compare sampling and bootstrap standard deviations

Confidence intervals

Confidence interval interpretation

Calculating confidence intervals

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Sampling in Python

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*4.4

from 68 reviews

71%

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Top reviews

PASCAL P.

about 1 month

Great course to learn sampling in Python.

muhammad t.

3 months

Great

Urich K.

4 months

Complex but very interesting

Noel C.

5 months

Excellent course! five stars

Li D.

6 months

Great course - a must. Very useful.

"Great course to learn sampling in Python."

PASCAL P.

"Great"

muhammad t.

"Complex but very interesting"

Urich K.

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