Using real-world examples from a wide array of domains including law, medicine, and football, you’ll learn how data scientists make conclusions about unknowns based on the data available. Often, the data we have is not complete, yet we’d still like to draw inferences about the world and quantify the uncertainty in our conclusions. This is called statistical inference. In this course, you will learn the framework for statistical inference and apply them to real-world data sets.
Notably, you will develop the practice of hypothesis testing—comparing theoretical predictions to actual data and choosing whether to accept those predictions. This method allows us to evaluate theories or hypotheses about how the world works.
You will also learn how to quantify the uncertainty in the conclusions you draw from hypothesis testing. This helps assess whether patterns that appear to be present in the data actually represent a true relationship in the world, or whether they might merely reflect random fluctuations due to noise. Throughout this course, we will go over multiple methods for estimation and hypothesis testing, based on simulations and the bootstrap method. Finally, you will learn about randomized controlled experiments and how to draw conclusions about causality.
The course emphasizes the conceptual basis of inference, the logic of the decision-making process, and the sound interpretation of results.
Topics of study
The logical and conceptual frameworks of statistical inference
How to conduct hypothesis testing, permutation testing, and A/B testing
The purpose and power of resampling methods
Relations between sample size and accuracy
P-values, quantifying uncertainty, and generating confidence intervals using the bootstrap method
How to interpret the results from hypothesis testing
About the University of California, Berkeley
The University of California, Berkeley was chartered in 1868, and its flagship campus — envisioned as a "City of Learning" — was established at Berkeley, on San Francisco Bay. Berkeley faculty consists of 1,582 full-time and 500 part-time faculty members dispersed among more than 130 academic departments and more than 80 interdisciplinary research units. Berkeley alumni have received 28 Nobel prizes, and there are eight Nobel laureates, 32 MacArthur Fellows, and four Pulitzer Prize winners among the current faculty.
In September 2012, to mark Berkeley's commitment to innovation in teaching and learning, The Berkeley Resource Center for Online Education (BRCOE) was formed. The Center is a resource hub and an operational catalyst for all internal campus-wide and external resources to advise, coordinate, and facilitate the university’s online education initiatives, ranging from credit and non-credit courses, to online degree programs and MOOC projects, including the MOOCLab initiative.
BRCOE's new MOOCLab is a three-year research initiative to fund and develop Massive Open Online Courses (MOOCs) as vehicles for pedagogical research in online education.
Berkeley is also working with edX to develop and foster adoption of Small Private Online Courses (SPOCs) on campuses around the world. SPOCs are designed to supplement and enhance the learning experience of on-campus students, while providing local faculty an opportunity for more interactive activities and more time for “high-touch” pedagogy.