Data Science: Inferential Thinking through Simulations

Data Science: Inferential Thinking through Simulations
1 hour coourse
Certification paid
Course by EdX
Data Science: Inferential Thinking through Simulations
What will you learn?
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 course

Using real-world examples from a wide range 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 are 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 learn how to conduct hypothesis testing—comparing theoretical predictions to actual data, and choosing whether to accept those predictions. You will utilize the power of computation to conduct simulations by which you can evaluate theories or hypotheses about how the world works. This course will teach you the power of statistical inference: given a random sample, how do we predict some quantity that we cannot observe directly?

You will also learn how to by quantifying 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 chance. 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.

Foundations of Data Science: Inferential Thinking by Resampling
Learn how to use inferential thinking to make conclusions about unknowns based on data in random samples.
Ani Adhikari
Ani Adhikari
Teaching Professor of Statistics UC Berkeley
John DeNero
John DeNero
Giancarlo Teaching Fellow in the EECS Department UC Berkeley
David Wagner
David Wagner
Professor of Computer Science UC Berkeley
All the courses on this platform are free of charge. The authors are top universities and corporations that seek to maintain high quality standards. If you do not meet a deadline for assignments, you lose points. Like on other platforms, the videos in which the theory is explained are followed by practical assignments. Courses are available in English, Chinese, Spanish, French and Hindi.
Like any other website, konevy uses «cookies». These cookies are used to store information including visitor's preferences, and the pages on the website that the visitor accessed or visited. The information is used to optimize the users' experience by customizing our web page content based on visitors' browser type and/or other information. For more general information on cookies, please read the «What Are Cookies» article on Cookie Consent website.