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Валентин Промыслов
Онлайн-курс «Теория вероятностей и ее приложения» входит в специализацию «Математика для анализа данных» от НИУ ВШЭ и не требует предварительных знаний, кроме материала, пройденного ранее в рамках этой специализации. Программа дистанционного курса рассчитана...
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Онлайн-курс «Теория вероятностей и ее приложения» входит в специализацию «Математика для анализа данных» от НИУ ВШЭ и не требует предварительных знаний, кроме материала, пройденного ранее в рамках этой специализации. Программа дистанционного курса рассчитана на желающих заниматься компьютерными науками и насыщена примерами применения теоретического материала на практике. На лекциях будут даны базовые математические инструменты анализа реальных жизненных ситуаций и процессов, которые можно закрепить, выполнив практические задания. В рамках курса будут изучены: – понятия дискретного и непрерывного вероятностного пространства; – независимость, условная вероятность и связанные с ними формулы (в том числе формула полной вероятности, формула Байеса и т. д.); – случайная величина и её свойства; – плотность случайной величины, одномерная и многомерная функция распределения; – условное распределение случайных величин и способы анализа совместного распределения; – математическое ожидание, причем особое внимание будет уделено условному математическому ожиданию; – базовые способы анализа больших отклонений; – дисперсия, ковариация, коэффициент корреляции и их геометрическая интерпретация; – закон больших чисел и центральная предельная теорема. Приоритетом при составлении курса являлось формирование глубокого понимания используемых в анализе данных вероятностных инструментов. Поэтому все понятия будут подробно рассмотрены с разных сторон, обоснованы и тщательно разобраны в решаемых задачах. Большое количество примеров в курсе тоже служит для этой цели — не просто узнать, а научиться использовать изученную технику.
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Develops a deep understanding of the probabilistic tools used in data analysis, which is in line with what data science companies expect
Provides solid mathematical foundation for computer science students who want to delve into data science
Emphasizes mathematical tools for analyzing real-life situations to solve data science problems
Includes hands-on practical assignments to reinforce theoretical concepts
Teaches foundational concepts like discrete and continuous probability spaces and random variables
Uses examples to illustrate theoretical techniques in practice
Requires prerequisite knowledge in probability theory covered in earlier courses in the specialization

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Теория вероятностей и ее приложения with these activities:
Review Mathematical Statistics
Refresh your prior knowledge of Mathematical Statistics before the course begins to ensure a stronger foundational understanding
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  • Review necessary formulas and theorems
  • Solve sample practice problems
  • Review sampling distributions
An Introduction to Probability and Mathematical Statistics
Review the core concepts of probability and mathematical statistics covered in the textbook
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  • Read the relevant chapters
  • Take notes on important concepts and definitions
Create Probability and Statistics Flashcards
Create flashcards to reinforce your understanding of key probability and statistics concepts
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  • Identify key concepts and definitions
  • Write a question on one side of the card and the answer on the other
  • Review the flashcards regularly
Five other activities
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Solve Probability and Statistics Practice Problems
Practice solving probability and statistics problems to improve your problem-solving skills
Browse courses on Probability
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  • Find practice problems online or in textbooks
  • Solve the problems and check your answers
Follow Online Probability and Statistics Tutorials
Follow online tutorials to supplement your understanding of probability and statistics concepts
Browse courses on Probability
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  • Find reputable online tutorials
  • Watch the tutorials and take notes
Join a Probability and Statistics Study Group
Join a study group to discuss concepts, solve problems, and quiz each other
Browse courses on Probability
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  • Find a study group or create your own
  • Meet regularly to discuss the course material
Collect and Analyze Data on a Real-World Problem
Apply your probability and statistics knowledge to a real-world problem by collecting and analyzing data
Browse courses on Probability
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  • Identify a problem that can be addressed using probability and statistics
  • Collect data relevant to the problem
  • Analyze the data using probability and statistics techniques
Create a Presentation on Probability and Statistics Applications
Create a presentation to demonstrate your understanding of probability and statistics applications
Browse courses on Probability
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  • Choose a topic related to probability and statistics applications
  • Research and gather information on the topic

Career center

Learners who complete Теория вероятностей и ее приложения will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst uses analytical skills to collect, clean, and analyze data. They interpret data to discover patterns and trends. They then present their findings to help businesses make better decisions. This course in Probability Theory and its Applications can help build a foundation for success as a Data Analyst by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing data to make informed decisions.
Quantitative Analyst
A Quantitative Analyst (Quant) uses mathematical and statistical models to analyze financial data. They use these models to make investment decisions and develop trading strategies. This course can help build a foundation for success as a Quant by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing financial data to make sound investment decisions.
Statistician
A Statistician collects, analyzes, and interprets data to help solve problems and make decisions. They use their knowledge of probability and statistics to design studies, analyze data, and draw conclusions. This course in Probability Theory and its Applications can help build a foundation for success as a Statistician by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing data to make informed decisions.
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to help businesses improve their efficiency and effectiveness. They use their knowledge of probability and statistics to model and analyze business processes. This course in Probability Theory and its Applications can help build a foundation for success as an Operations Research Analyst by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing business processes to make informed decisions.
Risk Manager
A Risk Manager identifies, assesses, and manages risks to help organizations make informed decisions. They use their knowledge of probability and statistics to analyze risks and develop mitigation strategies. This course in Probability Theory and its Applications can help build a foundation for success as a Risk Manager by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing risks to make informed decisions.
Actuary
An Actuary uses mathematical and statistical techniques to assess and manage financial risks. They use their knowledge of probability and statistics to model and analyze risks and develop mitigation strategies. This course in Probability Theory and its Applications can help build a foundation for success as an Actuary by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing financial risks to make informed decisions.
Data Scientist
A Data Scientist uses a combination of programming, statistics, and machine learning to analyze data. They use their knowledge of probability and statistics to develop models and make predictions. This course in Probability Theory and its Applications can help build a foundation for success as a Data Scientist by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing data to make informed decisions.
Market Researcher
A Market Researcher uses research methods to collect, analyze, and interpret data about consumer behavior. They use their knowledge of probability and statistics to design studies, analyze data, and draw conclusions. This course in Probability Theory and its Applications can help build a foundation for success as a Market Researcher by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing data to make informed decisions.
Financial Analyst
A Financial Analyst uses their knowledge of finance and economics to analyze and make recommendations on investments. They use their knowledge of probability and statistics to analyze financial data and make investment decisions. This course in Probability Theory and its Applications can help build a foundation for success as a Financial Analyst by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing financial data to make informed decisions.
Insurance Underwriter
An Insurance Underwriter uses their knowledge of insurance and risk management to assess and manage risks. They use their knowledge of probability and statistics to analyze risks and develop mitigation strategies. This course in Probability Theory and its Applications can help build a foundation for success as an Insurance Underwriter by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing risks to make informed decisions.
Business Analyst
A Business Analyst uses their knowledge of business and technology to analyze and improve business processes. They use their knowledge of probability and statistics to analyze data and make recommendations for improvement. This course in Probability Theory and its Applications can help build a foundation for success as a Business Analyst by teaching the fundamental principles of probability and statistics. These concepts are essential for understanding and analyzing data to make informed decisions.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They use their knowledge of probability and statistics to develop and test software systems. This course in Probability Theory and its Applications may be useful for building a foundation in probability and statistics for a Software Engineer. The concepts of probability and statistics are especially applicable to the testing and quality assurance of software.
Computer Programmer
A Computer Programmer writes and maintains computer programs. They use their knowledge of probability and statistics to develop and test computer programs. This course in Probability Theory and its Applications may be useful for building a foundation in probability and statistics for a Computer Programmer. The concepts of probability and statistics are especially applicable to the testing and quality assurance of software.
Data Engineer
A Data Engineer designs, builds, and maintains data systems. They use their knowledge of probability and statistics to develop and test data systems. This course in Probability Theory and its Applications may be useful for building a foundation in probability and statistics for a Data Engineer. The concepts of probability and statistics are especially applicable to the testing and quality assurance of data systems.
Database Administrator
A Database Administrator designs, builds, and maintains databases. They use their knowledge of probability and statistics to develop and test databases. This course in Probability Theory and its Applications may be useful for building a foundation in probability and statistics for a Database Administrator. The concepts of probability and statistics are especially applicable to the testing and quality assurance of databases.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Теория вероятностей и ее приложения.
This classic textbook provides a rigorous and comprehensive introduction to probability theory. It great choice for students who want to learn the subject in depth, and it is also a valuable reference for researchers and practitioners.
This graduate-level textbook provides a comprehensive and modern treatment of probability theory. It great choice for students who want to specialize in probability, and it is also a valuable reference for researchers and practitioners.
This textbook provides a comprehensive introduction to statistical methods for computer science. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to reinforcement learning. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to deep learning. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to convex optimization. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to information theory, inference, and learning algorithms. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to statistical learning. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.
This textbook provides a comprehensive introduction to applied predictive modeling. It covers a wide range of topics, from basic concepts to more advanced techniques, and provides plenty of examples and exercises to help you practice what you learn.

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