433 Statistics Degree Programs

The Master of Statistics and Data Science is also fully offered as a distance learning (DL) programme. Enrolling for the DL programme means that as a student, you will take the programme from a distance, i.e. from home, workplace or virtually any place with access to the internet. The DL programme can be followed full-time or part-time, allowing for a flexible and feasible combination of work and study.

This is one of several subject combinations available in our Certificate of Higher Education in Computing & IT and Statistics and a second subject (T13) – you’ll divide your time equally between the two subjects. You will be introduced to probability and the most common methods of statistical data analysis, and you will explore the profound technological, economic, political and ethical change driven by digital technologies.

Statistical data science is at the core of modern data analytics that turn data into intelligence to inform decision-making and solve challenging problems. Applications range from economics and medicine to social and environmental sciences. This degree covers theoretical and applied elements of modern statistics, and provides training and practical experience in modelling, analyzing and interpreting real data required in the economy, industry and research. The early years of the degree cover basic mathematics, computing, probability and statistics. The final years focus on advanced specialist topics in statistical modelling, data science, machine learning, probability and stochastic processes.

This program will suit anyone who is interested in mathematics and wants to apply his or her skills to problem-solving and for creating new mathematical models. It also gives you the possibility to start an academic career. In Mathematical Statistics and Financial Mathematics, you study courses in mathematics but you also train stochastic thinking and statistic skills. It contains courses in e.g. probability theory, stochastic analysis, insurance mathematics, and financial modeling.

The program is based on a mathematical background, which involves probability theory, stochastic processes, mathematical and computational statistics, optimization, econometrics, operational research, financial and insurance mathematics. The connected creative work is represented especially by the basic mathematical research, and partly by applications, e.g. in finance and insurance. The doctoral students enhance their knowledge in advanced courses and seminars, according to their individual study plans. Under the supervision of their advisor, they provide research work on a given topic and publish their results and present them at scientific conferences. The study branch will prepare experts who can become successors of faculty professors, others will profit from their mathematical thought in practice.

The new 2-year international Master in Business Analytics aims to prepare students from a variety of backgrounds for a career as business analysts in modern private and public organizations. The Programme is designed for students with a strong propensity for computational and statistical sciences as well as a strong interest in working with data.

Propel your career forward by tackling real-world challenges through statistical analysis. Enhance your statistical problem solving by developing and applying sophisticated statistical models, and by using computational techniques and advanced statistical software.

Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences, as well as in finance, commerce and medicine. The quantitative skills training provided by this MSc can lead to new and exciting opportunities in industry, healthcare, government, commerce or research.

CERGE-EI (Center for Economic Research and Graduate Education - Economics Institute) is a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences.

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.

Basic concepts of estimation and tests of hypotheses, ideas from probability; one-, two-, and multiple-sample problems. Applications are in the social sciences, and students will be able to understand the basics of using a sample to predict uncertainty.

Numerical and graphical summaries of univariate and bivariate data. Basic probability, random variables, binomial distribution, normal distribution. One-sample statistical inference for normal means and binomial probabilities. Primarily for students in the social sciences with limited mathematics preparation.

The inter-university master's degree provides advanced training in Statistics and Operations Research and in the application of statistical methods to biomedicine, finance, engineering or social research, facilitating internships in companies and access to the doctoral program correspondent.

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.