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Programmer (Python)

Programming in Python - Stepik | RU

Basic knowledge and practics

Programmin in Python Part 2 - Stepik | RU

Continuation of the first course. Deep learning Python

Python Statistic

Statistic

Basic in statistic - Stepik | Ru

Approaches to describing data obtained in research, basic methods and principles of statistical analysis, interpretation and visualization of the results obtained. Students will become familiar with statistical analysis methods such as variance, regression and cluster analysis. We will learn how to compare groups with each other, calculate correlation coefficients and build regression equations.

The main emphasis is on mathematical ideas, intuition and logic, which determine the methods and calculation formulas. The material studied will be applicable to solving a wide range of problems arising within the framework of research work in almost any direction.

Basic in statistic #2 - Stepik | RU

This course will examine data analysis methods that are most often used in statistical processing of results in a wide range of scientific and applied fields. In addition to theoretical tasks, students will be exposed to practical tasks that must be performed using the R programming language. The knowledge gained as a result of completing this course will be enough to learn how to more quickly and efficiently solve various problems related to data analysis.

Basic in statistic #3 - Stepik | RU

In this course, we complete an introduction to the most common data analysis techniques. We will take a closer look at the methods of regression analysis, paying special attention to methods for diagnosing regression models. Let's get acquainted with such advanced methods as mixed regression models, learn what bootstrap is and how to use it to test hypotheses. In addition to theoretical tasks, students will be exposed to practical tasks that must be performed using the R programming language. This course will be useful for both students from academic and applied fields.