14 Statistics Course Programs in USA

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.

Elementary treatment of probability densities, means, variances, correlation, independence, the central limit theorem, confidence intervals, and p-values. Students learn to answer questions such as how can a pollster use a sample to predict the uncertainty of an election?

Introduces basic statistical modeling techniques. One- or two- sample inference for unknown means, proportions and variances, categorical data analysis, introduction to design of experiments and analysis of variance, analysis of simple and multiple linear regression models, non-parametric methods.

Introduction to the logic and processes involved in descriptive and inferential statistics for psychology. Topics include statistical inference, significance, t-tests, ANOVAs, correlation, regression, and statistical software analysis. This is a hybrid class: class time is reserved for hands-on activities.

Undergraduate Prerequisites: Academic background that includes the material covered in a standard course on college algebra.

We offer courses that, in the true spirit of the liberal arts, emphasize underlying ideas, their social and ethical context, and their explanatory power. The statistics major emphasizes a strong background in the fundamentals, while also providing pathways to explore how statistics is applied in a variety of fields. Statistics is a core component of many fields from medicine to economics to data science.

The Master of Arts and Master of Science degrees in Applied Statistics provide students with the necessary background to seek employment in statistics, data sciences or a related field. The degree programs provide training in applied statistics and consulting, as well as in the theory of statistics pertinent to applications. In addition, students are required to study an approved area outside of statistics at the graduate level. Possible areas include numerical analysis, biostatistics, education, economics, sociology, psychology, biology, business, bioinformatics, computer science, mathematical finance, and many others.

The Applied Statistics B.A. is designed for students to learn the skills necessary to work in fields such as Economics, Psychology & Biology. Applied Statistics is for students interested in pursuing other disciplines in the field of Statistics such as Economics, Psychology and Biology. The curriculum is focused for students pursuing a B.A. or a Minor in Applied Statistics through practical skills such as regression analysis and ANOVA, or gaining experience in statistical software such as SAS and SPSS.

Kenyon’s mathematics program blends theoretical ideas and methods, the modeling of real-world situations, and the statistical analysis of data. Members of the mathematics faculty are at the forefront of such innovative programs as mathematical biology and work closely with students on research projects.

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 M.S. in Quantitative Methods in the Social Sciences at the Graduate Center prepares students from a variety of backgrounds for a data-driven future. Data analysis is driving research and decision-making in every social science field and sector. The M.S. in Quantitative Methods in the Social Sciences at the Graduate Center prepares students from a variety of backgrounds for a data-driven future.

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.