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Foundations of Inference in Python

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Updated 12/2024
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
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PythonProbability & Statistics4 hours14 videos48 exercises4,050 XP2,061Statement of Accomplishment

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Course Description

Truly Understand Hypothesis Tests

What happens after you compute your averages and make your graphs? How do you go from descriptive statistics to confident decision-making? How can you apply hypothesis tests to solve real-world problems? In this four-hour course on the foundations of inference in Python, you’ll get hands-on experience in making sound conclusions based on data. You’ll learn all about sampling and discover how improper sampling can throw statistical inference off course.

Analyze a Broad Range of Scenarios

You'll start by working with hypothesis tests for normality and correlation, as well as both parametric and non-parametric tests. You'll run these tests using SciPy, and interpret their output to use for decision making. Next, you'll measure the strength of an outcome using effect size and statistical power, all while avoiding spurious correlations by applying corrections. Finally, you'll use simulation, randomization, and meta-analysis to work with a broad range of data, including re-analyzing results from other researchers.

Draw Solid Conclusions From Big Data

Following the course, you will be able to successfully take big data and use it to make principled decisions that leaders can rely on. You'll go well beyond graphs and summary statistics to produce reliable, repeatable, and explainable results.

Prerequisites

Hypothesis Testing in Python
1

Inferential Statistics and Sampling

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2

Hypothesis Testing Toolkit

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3

Effect Size

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4

Simulation, Randomization, and Meta-Analysis

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Foundations of Inference in Python
Course
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