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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

Applied Regression Analysis

This unit provides learners with the knowledge and skills to apply regression techniques in analyzing relationships between variables in real-world datasets. The focus is on practical implementation of simple and multiple linear regression models, diagnostics, and interpretation of results using statistical software. Students will learn how to formulate regression models, estimate parameters, assess model fit, and validate assumptions. The unit emphasizes hands-on data analysis using tools such as R, SPSS, or Excel, equipping learners to make data-driven decisions in fields such as economics, education, health, business, and engineering.

Statistical Computing and Data Analysis

In this course, we are going to learn about the fundamentals of statistical computing and how to apply data analysis techniques using software tools such as R, SPSS, and Excel. The course emphasizes practical skills in data handling, visualization, statistical modeling, and interpretation of results.

Learners will explore key concepts in descriptive and inferential statistics, perform real-world data analysis, and develop basic programming skills in R for automating tasks. This course is designed to build competence in applying statistical techniques to problems in fields such as research, education, business, and health.

By the end of the course, students will be able to manage datasets, perform various statistical procedures, and effectively communicate their findings through well-structured reports and visualizations.