8 Online Statistics Course Programs

“If you can’t measure it, you can’t change it.” Masterclass in Labour Statistics: From Standards to Data course enable us to monitor one of the most important aspects of our well-being: work. As we spend a large part of our lives at work and live on the earnings from it, governments must have access to timely and relevant data to inform accurate and sound policies.

Due to COVID-19, classroom courses during Fall 2021 will be offered in a live online format. We continue to offer self-paced online courses.

From probability and statistics to data analysis and machine learning, master the skills needed to solve complex challenges with data.

Causal Inference with R is the first course in a series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this introduction focuses on how to interpret treatment effects, and how to explore and derive key summary statistics from dataframes.

The main goal of this learning module is to discuss phase transitions and critical phenomena in statistical physics. We introduce the ideas of mean-field theory and renormalization group and exploit them to calculate the critical exponents for the Ising model. More advanced topics concern the models with continuous symmetry, topological phase transitions, as well as Monte Carlo simulations.

Searching for an Excel course for beginners to experts? This online course has something for everybody, covering beginner, intermediate and advanced aspects in Excel.

Causal Inference with R – Experiments is the second course on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you about concepts, methods, and how to code in R with realistic data, this course focuses on experiment design, working with data from controlled and natural experiments, and dealing with noncompliance.

Causal Inference with R – Experiments is the fifth course on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this course focuses on how to use the advanced methods of instrumental variables and regression discontinuity to find causal effects. We will give you the reasoning be behind the methods, how you need to argue their validity, and what they look like in practice.