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    Moodle is an open-source Learning Management System (LMS) that provides educators with the tools and features to create and manage online courses. It allows educators to organize course materials, create quizzes and assignments, host discussion forums, and track student progress. Moodle is highly flexible and can be customized to meet the specific needs of different institutions and learning environments.

    Moodle supports both synchronous and asynchronous learning environments, enabling educators to host live webinars, video conferences, and chat sessions, as well as providing a variety of tools that support self-paced learning, including videos, interactive quizzes, and discussion forums. The platform also integrates with other tools and systems, such as Google Apps and plagiarism detection software, to provide a seamless learning experience.

    Moodle is widely used in educational institutions, including universities, K-12 schools, and corporate training programs. It is well-suited to online and blended learning environments and distance education programs. Additionally, Moodle's accessibility features make it a popular choice for learners with disabilities, ensuring that courses are inclusive and accessible to all learners.

    The Moodle community is an active group of users, developers, and educators who contribute to the platform's development and improvement. The community provides support, resources, and documentation for users, as well as a forum for sharing ideas and best practices. Moodle releases regular updates and improvements, ensuring that the platform remains up-to-date with the latest technologies and best practices.

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Available courses

Mathematical statistics

This course provides a comprehensive introduction to the principles and methods of statistical inference and analysis. Students will explore foundational concepts in probability theory, random variables, and distribution theory, as well as advanced statistical techniques.

Key Topics Include:

  • Foundations of Statistics: Understanding types of data, measurement scales, and the role of statistics in various fields.
  • Probability Theory: Basic concepts, conditional probability, independence, and Bayes' theorem.
  • Random Variables: Discrete and continuous random variables, probability distributions, and their applications.
  • Estimation and Inference: Point estimation, interval estimation, hypothesis testing, and the evaluation of Type I and Type II errors.
  • Regression Analysis: Simple and multiple regression techniques, model fitting, and diagnostics.
  • Analysis of Variance (ANOVA): Techniques for comparing means across multiple groups.
  • Non-parametric Methods: Statistical methods that do not assume a normal distribution.
  • Statistical Software: Hands-on experience with statistical software for data analysis and visualization.