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Ульянкин Филипп Валерьевич
В этом онлайн-курсе НИУ ВШЭ мы поговорим про статистические методы анализа данных. Мы подробно обсудим, как статистика связана с машинным обучением и какое место она занимает среди наук о данных. Поговорим о том, что есть два великих вопроса: "Как устроен мир?" и "Что будет дальше?". В зависимости от того, на какой вопрос мы ищем ответ, мы должны использовать разные методы.
Мы изучим метод максимального правдоподобия, поговорим про статистические свойства различных функций потерь. Поговорим о линейной регрессии и временных рядах. В конце курса мы обсудим байесовский подход к статистике и выясним, чем он отличается от частотного.
Register for this course and see more details by visiting:
OpenCourser.com/course/d6a7na/statisticheskie
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Guides learners how to approach data analysis, a ubiquitous skill used in myriad areas
Suitable for learners curious about machine learning and data science, fields that use statistics to explore data and extract insights
Explores both frequentist and Bayesian statistics, giving learners a robust understanding of these two main approaches
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Reviews summary
Highly rated statistics course
Based on three 5-star reviews, this online course on Statistical Methods of Data Analysis from HSE University receives high praise for its clear explanations of complex topics and engaging presentation. Students appreciate the instructor's ability to make challenging concepts accessible and find the course content valuable and informative.
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 Статистические методы анализа данных with these
activities:
Review basic mathematical concepts
Show steps
Ensure a strong foundation in basic mathematical concepts to enhance your understanding of statistical methods.
Browse courses on
Algebra
Show steps
-
Review key concepts in algebra, such as equations, inequalities, and functions.
-
Go over basic calculus concepts, including limits, derivatives, and integrals.
Read existing literature on statistical methods
Show steps
Review the fundamentals of statistical methods to strengthen your foundation for this course.
Browse courses on
Statistical Analysis
Show steps
-
Identify key concepts in statistical analysis, such as probability, sampling, and hypothesis testing.
-
Read research papers and articles to gain insights into different statistical techniques.
-
Practice solving statistical problems to reinforce your understanding.
Organize and review course materials
Show steps
Stay organized and enhance your understanding by compiling and reviewing course materials regularly.
Show steps
-
Gather all course materials, including lecture notes, slides, assignments, and readings.
-
Create a structured system to organize these materials, such as folders or a digital notebook.
-
Review the materials periodically to reinforce your learning and identify areas for further study.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on statistical analysis software
Show steps
Gain proficiency in using statistical analysis software to enhance your data analysis skills.
Browse courses on
Statistical Software
Show steps
-
Choose a statistical analysis software (e.g., R, Python) and identify relevant tutorials.
-
Follow step-by-step tutorials to learn the basics of data manipulation, statistical modeling, and visualization.
-
Apply your newfound knowledge to practice datasets to solidify your understanding.
Solve practice problems on statistical methods
Show steps
Strengthen your understanding of statistical concepts through regular practice.
Browse courses on
Statistical Analysis
Show steps
-
Identify online platforms or textbooks that provide practice problems.
-
Set aside dedicated time to solve problems regularly, focusing on different statistical techniques.
-
Analyze your solutions and identify areas for improvement.
Participate in peer study groups
Show steps
Engage with peers to discuss course concepts, share insights, and provide support.
Show steps
-
Join or create a study group with classmates who share similar interests.
-
Meet regularly to discuss course material, work on assignments together, and clarify doubts.
-
Engage in active listening, share perspectives, and provide constructive feedback to peers.
Contribute to open-source statistical projects
Show steps
Gain practical experience in statistical analysis by contributing to real-world projects.
Browse courses on
Statistical Analysis
Show steps
-
Identify open-source projects related to statistical analysis and data science.
-
Explore the codebase and identify areas where you can contribute.
-
Join the project's community and engage in discussions to understand the project's needs.
-
Make code contributions, write documentation, or participate in testing and debugging.
Develop a statistical model for a real-world problem
Show steps
Apply your statistical knowledge to solve a real-world problem and enhance your problem-solving skills.
Show steps
-
Identify a problem that can be addressed using statistical analysis.
-
Gather and clean data relevant to the problem.
-
Develop a statistical model to analyze the data and draw conclusions.
-
Evaluate the model's performance and make necessary adjustments.
-
Communicate your findings through a report or presentation.
Review basic mathematical concepts
Show steps
Ensure a strong foundation in basic mathematical concepts to enhance your understanding of statistical methods.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Review key concepts in algebra, such as equations, inequalities, and functions.
- Go over basic calculus concepts, including limits, derivatives, and integrals.
Read existing literature on statistical methods
Show steps
Review the fundamentals of statistical methods to strengthen your foundation for this course.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Identify key concepts in statistical analysis, such as probability, sampling, and hypothesis testing.
- Read research papers and articles to gain insights into different statistical techniques.
- Practice solving statistical problems to reinforce your understanding.
Organize and review course materials
Show steps
Stay organized and enhance your understanding by compiling and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Gather all course materials, including lecture notes, slides, assignments, and readings.
- Create a structured system to organize these materials, such as folders or a digital notebook.
- Review the materials periodically to reinforce your learning and identify areas for further study.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on statistical analysis software
Show steps
Gain proficiency in using statistical analysis software to enhance your data analysis skills.
Browse courses on
Statistical Software
Show steps
Show steps
Show steps
- Choose a statistical analysis software (e.g., R, Python) and identify relevant tutorials.
- Follow step-by-step tutorials to learn the basics of data manipulation, statistical modeling, and visualization.
- Apply your newfound knowledge to practice datasets to solidify your understanding.
Solve practice problems on statistical methods
Show steps
Strengthen your understanding of statistical concepts through regular practice.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Identify online platforms or textbooks that provide practice problems.
- Set aside dedicated time to solve problems regularly, focusing on different statistical techniques.
- Analyze your solutions and identify areas for improvement.
Participate in peer study groups
Show steps
Engage with peers to discuss course concepts, share insights, and provide support.
Show steps
Show steps
Show steps
- Join or create a study group with classmates who share similar interests.
- Meet regularly to discuss course material, work on assignments together, and clarify doubts.
- Engage in active listening, share perspectives, and provide constructive feedback to peers.
Contribute to open-source statistical projects
Show steps
Gain practical experience in statistical analysis by contributing to real-world projects.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Identify open-source projects related to statistical analysis and data science.
- Explore the codebase and identify areas where you can contribute.
- Join the project's community and engage in discussions to understand the project's needs.
- Make code contributions, write documentation, or participate in testing and debugging.
Develop a statistical model for a real-world problem
Show steps
Apply your statistical knowledge to solve a real-world problem and enhance your problem-solving skills.
Show steps
Show steps
Show steps
- Identify a problem that can be addressed using statistical analysis.
- Gather and clean data relevant to the problem.
- Develop a statistical model to analyze the data and draw conclusions.
- Evaluate the model's performance and make necessary adjustments.
- Communicate your findings through a report or presentation.
Career center
Learners who complete Статистические методы анализа данных will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use statistical methods to analyze data and extract insights. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in data analysis. Taking this course will help you develop the skills and knowledge you need to be a successful Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use statistical methods to develop and deploy machine learning models. This course provides a solid foundation in statistical methods, which is essential for success in machine learning. You will learn about maximum likelihood estimation, linear regression, and time series analysis, as well as the Bayesian approach to statistics. This knowledge will help you build and deploy machine learning models that are accurate and reliable.
Data Scientist
Data Scientist
Data Scientists use statistical methods to analyze data and solve business problems. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in data science. Taking this course will help you develop the skills and knowledge you need to be a successful Data Scientist.
Statistician
Statistician
Statisticians use statistical methods to analyze data and draw conclusions. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in statistics. Taking this course will help you develop the skills and knowledge you need to be a successful Statistician.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze financial data and make investment decisions. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in quantitative finance. Taking this course will help you develop the skills and knowledge you need to be a successful Quantitative Analyst.
Actuary
Actuary
Actuaries use statistical methods to assess risk and make financial decisions. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in actuarial science. Taking this course will help you develop the skills and knowledge you need to be a successful Actuary.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in biostatistics. Taking this course will help you develop the skills and knowledge you need to be a successful Biostatistician.
Epidemiologist
Epidemiologist
Epidemiologists use statistical methods to study the distribution and determinants of health-related states or events in specified populations. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in epidemiology. Taking this course will help you develop the skills and knowledge you need to be a successful Epidemiologist.
Economist
Economist
Economists use statistical methods to analyze economic data and make policy recommendations. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in economics. Taking this course will help you develop the skills and knowledge you need to be a successful Economist.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical methods to solve problems in business and industry. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in operations research. Taking this course will help you develop the skills and knowledge you need to be a successful Operations Research Analyst.
Market Researcher
Market Researcher
Market Researchers use statistical methods to collect and analyze data about consumers and markets. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in market research. Taking this course will help you develop the skills and knowledge you need to be a successful Market Researcher.
Research Analyst
Research Analyst
Research Analysts use statistical methods to analyze data and make recommendations. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in research. Taking this course will help you develop the skills and knowledge you need to be a successful Research Analyst.
Software Engineer
Software Engineer
Software Engineers use statistical methods to develop and test software. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in software engineering. Taking this course will help you develop the skills and knowledge you need to be a successful Software Engineer.
Data Architect
Data Architect
Data Architects use statistical methods to design and implement data management systems. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in data architecture. Taking this course will help you develop the skills and knowledge you need to be a successful Data Architect.
Business Analyst
Business Analyst
Business Analysts use statistical methods to analyze data and make recommendations. This course provides a comprehensive overview of statistical methods, including maximum likelihood estimation, linear regression, and time series analysis. It also covers the Bayesian approach to statistics, which is becoming increasingly popular in business analysis. Taking this course will help you develop the skills and knowledge you need to be a successful Business Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/d6a7na/statisticheskie
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
Статистические методы анализа данных.
This popular book covers a wide range of statistical topics, from basic concepts to advanced methods like supervised and unsupervised learning.
This introductory book on statistical learning, which is freely available online, is commonly used for academic courses and valuable reference in industry.
This classic book provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning. It valuable reference for practitioners and researchers in machine learning.
Provides a comprehensive and mathematical treatment of pattern recognition and machine learning. It valuable reference for researchers in machine learning and related fields.
Save
Provides a comprehensive and up-to-date overview of deep learning, including theoretical foundations and practical applications. It valuable reference for researchers and practitioners in deep learning.
Provides a comprehensive and up-to-date overview of probabilistic graphical models. It valuable reference for researchers and practitioners in machine learning and related fields.
Provides a comprehensive and accessible introduction to information theory, inference, and learning algorithms. It is suitable for beginners and those with some background in mathematics.
Provides a comprehensive and modern introduction to machine learning from a probabilistic perspective.
Provides a comprehensive and accessible introduction to reinforcement learning. It is suitable for beginners and those with some background in machine learning.
Provides a solid foundation in statistical methods, with a particular focus on applications to machine learning. While chapters are primarily theoretical, they are accessible to those without a background in statistics.
Provides a comprehensive and accessible introduction to Bayesian statistics, including theoretical foundations and practical applications.
Provides a practical introduction to Bayesian statistics using R and Stan. It is particularly useful for those with a background in statistics who want to learn Bayesian methods.
Provides a comprehensive and accessible introduction to convex optimization. It is suitable for beginners and those with some background in mathematics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d6a7na/statisticheskie
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Time
24 hours
Via
Coursera
Institution
HSE University
Instructor
Ульянкин Филипп Валерьевич
Good to know
Guides learners how to approach data analysis, a ubiquitous skill used in myriad areas
Suitable for learners curious about machine learning and data science, fields that use statistics to explore data and extract insights
Explores both frequentist and Bayesian statistics, giving learners a robust understanding of these two main approaches
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