Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
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 inference foundations in Python, you’ll get hands-on experience making sound conclusions based on data. You’ll learn all about sampling and discover how improper sampling can throw statistical inference off course.
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 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.
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 beyond graphs and summary statistics to produce reliable, repeatable, and explainable results.
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 inference foundations in Python, you’ll get hands-on experience making sound conclusions based on data. You’ll learn all about sampling and discover how improper sampling can throw statistical inference off course.
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 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.
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 beyond graphs and summary statistics to produce reliable, repeatable, and explainable results.
