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Теория вероятностей - наука о случайности

Анна Владимировна Китаева
Курс познакомит с основными правилами исчисления вероятностей, обращая внимание на базовые идеи и концепции, научит решать вероятностные задачи, пользуясь формальным аппаратом. Курс позволит освоить элементарные вероятностные методы и применять их в быту и...
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Курс познакомит с основными правилами исчисления вероятностей, обращая внимание на базовые идеи и концепции, научит решать вероятностные задачи, пользуясь формальным аппаратом. Курс позволит освоить элементарные вероятностные методы и применять их в быту и профессиональной деятельности, будет способствовать развитию рационального мышления и способности выражать мысли в математической форме. Понятие вероятности становится необходимым рациональным инструментом ориентации в современном мире полном неопределенности. Касается ли это проблем бизнеса, управления, науки, повседневной жизни, нам, как правило, приходится принимать решения в условиях риска и неопределенности. Вообще говоря, теорию вероятностей можно рассматривать как математическую модель интуитивного понятия неопределенности. Курс является введением в элементарную теорию вероятностей и снабжен многочисленными примерами разной степени сложности, часто взятыми из жизни, показывающими как строятся вероятностные модели. Даются англоязычные аналоги основных терминов.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops fundamental probability theory concepts and applications, catering to beginners
Facilitates probabilistic problem-solving with formal mathematical tools
Applicable in various fields, promoting rational thinking and mathematical expression
Introduces probability as an essential tool for decision-making in uncertain environments
Provides a solid foundation for statistical learning and data analysis

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Reviews summary

Theoretical probability and chance

A highly theoretical course that is heavy on math and statistics. This is a challenging course that supplements video lectures with external references.
Course assignments may require external research.
"Необходимы дополнительные материалы и литература для получения ответов на вопросы которые не понял по видеокурсам."
Course is heavy on theory.
"Слишком формально."
"Мало практики и объяснений."

Activities

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Career center

Learners who complete Теория вероятностей - наука о случайности will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts work extensively with probabilities and statistics to analyze data and build predictive models. The probability theory taught in this course provides a foundation in the mathematical principles that underpin probability models used in data analysis. By taking this course, you will enhance your ability to construct and evaluate probability models, which can lead to success in the field of Data Analytics.
Actuary
Actuaries leverage probability theory to evaluate and manage financial risks in various industries, including insurance and pensions. The probability concepts covered in this course provide a solid foundation for understanding actuarial models, enabling you to assess and mitigate risk more effectively. By taking this course, you will be well-equipped for a successful career as an Actuary.
Quantitative Analyst
Quantitative Analysts use probability theory to assess the risk and return of financial investments. This course delves into the core principles of probability, which can bolster your understanding of risk assessment and portfolio management. Mastering the concepts taught in this course will augment your skillset as a Quantitative Analyst and enhance your ability to make informed decisions.
Statistical Analyst
Statistical Analysts use probability theory and statistical methods to analyze data and draw meaningful conclusions. This course provides a solid foundation in probability, which is essential for understanding the statistical models and techniques used in data analysis. By taking this course, you will acquire the skills and knowledge necessary to succeed as a Statistical Analyst.
Risk Manager
Risk Managers play a crucial role in identifying, assessing, and managing risks across organizations. The probability theory taught in this course will equip you with the analytical tools and techniques necessary to quantify and manage risk effectively. By mastering the concepts covered in this course, you will enhance your ability to develop and implement risk management strategies and excel in the Risk Management field.
Operations Research Analyst
Operations Research Analysts use probability theory and mathematical techniques to optimize decision-making in complex systems. The probability concepts covered in this course will provide you with a solid foundation for understanding and applying operations research models. By taking this course, you will enhance your ability to analyze and solve problems in areas such as logistics, supply chain management, and healthcare.
Financial Analyst
Financial Analysts use probability theory to assess the risk and return of financial investments. This course provides a strong foundation in probability, which is essential for understanding financial models and making informed investment decisions. By taking this course, you will enhance your ability to analyze financial data, evaluate investment opportunities, and make sound recommendations.
Data Scientist
Data Scientists use probability theory and statistical methods to analyze data and build predictive models. This course provides a strong foundation in probability, which is essential for understanding the statistical models and techniques used in data science. By taking this course, you will enhance your ability to extract insights from data and build predictive models, which can lead to success as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineers use probability theory to develop and implement machine learning algorithms. This course will provide you with a solid foundation in probability, which is essential for understanding the underlying principles of machine learning. By taking this course, you will enhance your ability to design, train, and evaluate machine learning models, which can lead to success as a Machine Learning Engineer.
Software Engineer
Software Engineers often use probability theory to model and analyze the behavior of software systems. This course provides a solid foundation in probability, which is essential for understanding the principles of software reliability and performance modeling. By taking this course, you will enhance your ability to design and implement software systems that are reliable and efficient, which can lead to success as a Software Engineer.
Data Architect
Data Architects use probability theory to design and implement data management systems. This course provides a solid foundation in probability, which is essential for understanding the principles of data modeling and storage. By taking this course, you will enhance your ability to design and implement data management systems that are scalable, reliable, and secure, which can lead to success as a Data Architect.
Business Analyst
Business Analysts often use probability theory to analyze data and make recommendations for business decisions. This course provides a strong foundation in probability, which is essential for understanding the statistical methods used in business analysis. By taking this course, you will enhance your ability to analyze business data, identify trends, and make recommendations that can improve business outcomes, which can lead to success as a Business Analyst.
Insurance Analyst
Insurance Analysts use probability theory to assess the risk and pricing of insurance policies. This course provides a strong foundation in probability, which is essential for understanding the principles of insurance pricing and risk management. By taking this course, you will enhance your ability to analyze insurance data, evaluate risk, and make sound underwriting decisions, which can lead to success as an Insurance Analyst.
Statistician
Statisticians use probability theory to collect, analyze, and interpret data. This course provides a solid foundation in probability, which is essential for understanding the principles of statistical inference and hypothesis testing. By taking this course, you will enhance your ability to design and conduct statistical studies, analyze data, and draw meaningful conclusions, which can lead to success as a Statistician.
Market Researcher
Market Researchers use probability theory to design and conduct market research studies. This course provides a strong foundation in probability, which is essential for understanding the principles of sampling and survey design. By taking this course, you will enhance your ability to collect and analyze market research data, identify trends, and make recommendations that can improve marketing strategies, which can lead to success as a Market Researcher.

Reading list

We've selected 13 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 - наука о случайности.
Classic work on the foundations of probability theory and decision theory. It is well-written and clear, and it provides a deep understanding of the subject.
Provides a modern introduction to probability theory and mathematical statistics, with a focus on applications in data science and machine learning.
Provides a comprehensive introduction to Markov chains and stochastic processes. It is well-written and clear, and it provides a deep understanding of the subject.
Classic textbook for a first course in Bayesian statistics. It is well-written and clear, and it provides a solid foundation in the subject.
Provides a modern introduction to Bayesian statistics, with a focus on applications in data science and machine learning.
Provides a comprehensive introduction to statistical signal processing, with a focus on estimation theory. It is well-written and clear, and it provides a deep understanding of the subject.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It is well-written and clear, and it provides a deep understanding of the subject.
Provides a comprehensive introduction to machine learning, with a focus on probabilistic models. It is well-written and clear, and it provides a deep understanding of the subject.
Provides a comprehensive introduction to deep learning, with a focus on theoretical foundations and practical applications. It is well-written and clear, and it provides a deep understanding of the subject.
Provides a comprehensive introduction to probability, statistics, and data, with a focus on applications in data science and machine learning. It is well-written and clear, and it provides a deep understanding of the subject.

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