433 Statistics Degree Programs

The curriculum consists of a thesis that incorporates a research proposal, an oral defense (viva voce) and two manuscripts, based on the candidate's research, submitted for publication to a recognized accredited journal. Proof of the submission of the manuscripts is a compulsory condition for the degree to be conferred.

The curriculum consists of a dissertation that incorporates a research proposal to be completed within the first year of study

The program focuses on providing students with a strong foundation in data science and statistical methods. It covers core topics like data analysis, machine learning, and statistical modeling, prepar...

In Computational Mathematics you’ll study the intersection of mathematics, statistics, optimization, and computer science, to solve problems in business, finance, medicine, science, and technology. You'll learn to analyze data sets, formulae, and images in ways that help us understand the world around us and predict and influence the future.

Nowadays, science and technology are rapidly developing with an ever-increasing impetus and a substantial part of knowledge is comprised of quantitative data. The analysis and interpretation of this data require the use of mathematical and statistical methods.

Biostatistics provides an introduction to some important topics in biostatistical concepts, measurements, and reasoning. This course provides students with knowledge of statistics and their applications in biomedical sciences. It represents the need and uses of biostatistics in public health and medicine, data measurement scales, graphical and schematic presentations of various data.

You will be inspired to excel in today’s data-driven environment at Bryant, one of only 7 undergraduate universities worldwide to offer the SAS Certificate in data mining. Develop the skills and knowledge required to solve real-world problems by applying mathematical principles.

It's a great time to be doing graduate studies in statistics! The astonishing amount of data being collected today is literally transforming science and the skills of statisticians are in demand more than ever. The M.S. in Statistical Science degree is intended to prepare a student for a career as a professional statistician working in business, government, or industry.

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.

Our majors and minors develop the ability to understand both applied and abstract quantitative relationships, to interpret and communicate with data, to use modern technology to compute, and to understand and implement algorithms.

Our course is led by academics from the health sector and higher education, including statisticians from the Animal and Plant Health Agency (APHA) and experienced professionals from the National Health Service (NHS) in Scotland. They will provide lectures on real-life health science problems and data.

Since the Fall of 2015, the Graduate School of Arts and Sciences has offered the Master of Science in Analytics with a concentration in Data Science degree. Instruction is provided by the Department of Computer Science and the Department of Mathematics and Statistics and the Analytics Program. The curriculum provides students with a rigorous training in computational, mathematical, and statistical methods to prepare them for careers in data science and analytics.

The Master of Science in Data Science and Business Analytics program is designed to prepare students for roles that require deep understanding of data analysis, statistical methods, and business probl...

In this flexible-credit master of science program, you will learn advanced quantitative research techniques and apply them to critical policy issues across the social, behavioral, and health sciences, preparing for a career as an applied statistician or data scientist or for doctoral study.

The broad objective of the course is to promote an understanding of an objective and disciplined approach in the study and analysis of problems especially those involving numerical data in experimental studies. The course covers: organization and presentation of numerical data, descriptive statistics, concepts of parameter and statistic, distribution of sample means, hypothesis and test of hypothesis, student's t-distribution, confidence interval and coefficient, x2-distribution, F-distribution, analysis of variance, randomized block design, simple regression and correlation.