TOP Programming Courses from Open Education

The program is an introduction to the interdisciplinary field of LegalTech. It provides an overview of the fundamentals and basic principles of legal practice necessary to understand LegalTech, as well as an analysis of existing solutions. In parallel, issues of digitalization, digital transformation, artificial intelligence and issues of ethics and safety in the use of LegalTech tools are considered. Having mastered the program, the student will develop an understanding of this area and possible directions for further development in it.

The purpose of the course is for students to gain knowledge about the technology of aerial photography using unmanned aerial vehicles (UAVs). The objectives of the course include familiarization with: types of aircraft and payloads, the design of aircraft, the creation geodetic justification for surveying, with flight planning and execution, with the main stages of data processing and with the use of the obtained materials. A separate section is devoted to the legal aspects of using aerial photography from UAVs.

Course Objectives Learn QisKit - the most important tool in the arsenal of a quantum algorithm developer. Explore other features of the IBM Quantum Experience platform - Learn some new (and surprisingly useful) algorithms. Enjoy! Those who believe that theory is dead without practice - join us! Let's practice and save the hard-earned theoretical knowledge about the field of quantum computing from death!

This program is a continuation of the programs “Introduction to LegalTech” and “Automation in Legal Activities: Analysis of Practices”. Like previous programs, it aims to develop the necessary understanding to promote understanding of such an interdisciplinary field as LegalTech. However, the main feature of this program is that it focuses primarily on the analysis of tools and practices for the implementation and development of LegalTech solutions. Having mastered it, the student will form a more complete understanding of this interdisciplinary field and possible directions for further development in it.

This program is a continuation of the Introduction to LegalTech program. Like the previous program, the “Automation in Legal Activities: Analysis of Practices” program is aimed at formation of the necessary ideas that contribute to the understanding of such an interdisciplinary field as LegalTech. However, the main feature of this program is that it fully focuses on the analysis of existing automation practices in the legal field. Having mastered it, the student will form a more complete understanding of this interdisciplinary field and possible directions for further development in it.

Biologically Inspired Cognitive Architectures (BICA) – This is a promising, actively developing direction at the intersection of artificial intelligence, biology and cognitive Sci. One evidence of this is the increased number of scientific publications, including special editions, as well as conferences and funded programs, one way or another related to this area.

This course will provide you with fundamentals of Unix and Linux operating systems. It will show you how such systems are organized, and demonstrate how to use them at an advanced level. After completing this course, you will have a good understanding of the principles of how these systems work. In applying these skills, you will be able to perform fundamental operational tasks, whether your Unix/Linux machine or on a remote system. The course is on English level with Russian subtitles.

The course provides access to a basic set of knowledge of probability theory, mathematical statistics and the mathematical basis of algorithms for solving machine learning problems. Also provides practical skills in data analysis and creating machine learning models in the Python programming language.

The course examines the basic approaches and libraries for data processing and visualization in Python. Students learn methods of working with different types of data - from semi-structured to tabular, and They also learn to solve practical data preparation tasks using open data sets and API. In the course, students are introduced to the libraries that are necessary to effectively solve a wide range of analytical problems, such as Ipython, Pandas, Numpy, Matplotlib and Scikit-learn, etc.