Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.
This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Probability & Expected Values
This week, we’ll focus on the fundamentals including probability, random variables, expectations and more.
Graded: Quiz 1
Variability, Distribution, & Asymptotics
We’re going to tackle variability, distributions, limits, and confidence intervals.
Graded: Quiz 2
Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
Graded: Quiz 3
Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
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