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Ульянкин Филипп Валерьевич
В этом онлайн-курсе мы будем смотреть на данные через призму математической статистики. Поговорим о том, какое место она занимает среди наук о данных. Научимся аккуратно переводить повседневные задачи на язык статистики и выбирать методы для работы с ними....
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В этом онлайн-курсе мы будем смотреть на данные через призму математической статистики. Поговорим о том, какое место она занимает среди наук о данных. Научимся аккуратно переводить повседневные задачи на язык статистики и выбирать методы для работы с ними....
Read more
В этом онлайн-курсе мы будем смотреть на данные через призму математической статистики. Поговорим о том, какое место она занимает среди наук о данных. Научимся аккуратно переводить повседневные задачи на язык статистики и выбирать методы для работы с ними.
К концу курса мы научимся грамотно выбирать методы для проверки гипотез, строить доверительные интервалы, а также реализовывать это всё на Python.
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Good to know
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Know what's good ,
what to watch for , and
possible dealbreakers
An excellent foundational course in math statistics
Well-grounded in scientific understanding and academic principles
Offers hands-on labs and interactive materials
Builds a strong foundation for beginners
Taught in Python, a highly relevant tool
Content is sufficiently detailed and comprehensive
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Reviews summary
Highly rated math stats and ab testing course
This course is highly rated by students, who praise its structured and informative content, engaging explanations, and practical Python practice. Despite minor technical difficulties, learners report finding the course valuable for applied data analysis.
Students consistently describe the course as 'excellent,' with clear and informative content.
"Отличный курс!"
"Этот курс дал мне очень много."
"Отличный курс! Все очень структурированно и информативно."
"Отличный курс! Преподаватель Филипп очень хорошо все объясняет, доходчиво и интересно!"
The instructor's explanations are praised for their clarity and ability to make complex concepts understandable.
"Преподаватель Филипп очень хорошо все объясняет, доходчиво и интересно!"
"Местами было не просто, но очень интересно и увлекательно."
The course incorporates hands-on Python practice using libraries like Pandas, NumPy, SciPy, and Statsmodels, which students find valuable for applied data analysis.
"хорошие теоретические объяснения, практические примеры реализации и конечно же много практики для решения задач на Python"
"много практики для решения задач на Python c помощью таких библиотек как Pandas, NumPy, SciPy и Statsmodels."
Some students mentioned minor technical difficulties, which did not significantly impact their overall satisfaction.
"не без косяков технических конечно, но это мелочи"
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 Математическая статистика и А/В тестирование with these
activities:
Follow tutorials on Python for Statistical Analysis
Show steps
These tutorials will provide you with practical experience in using Python for statistical analysis, which will be essential for the course.
Show steps
-
Find online tutorials or courses on Python for Statistical Analysis
-
Follow the tutorials and complete the exercises
Read 'Statistical Inference' by George Casella and Roger Berger
Show steps
This book provides a comprehensive and rigorous treatment of statistical inference, which will supplement the course material.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
-
Read the assigned chapters
-
Take notes and summarize the key concepts
-
Complete the practice exercises at the end of each chapter
Review Frequentist and Bayesian Statistics
Show steps
Reviewing these foundational topics will help strengthen your understanding of the course material.
Browse courses on
Mathematical Statistics
Show steps
-
Review the key concepts of Frequentist and Bayesian Statistics
-
Solve practice problems to test your understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a study group with classmates
Show steps
Collaborating with peers will help you understand the material more deeply and identify areas where you need further clarification.
Show steps
-
Form a study group with other classmates
-
Meet regularly to discuss the course material
-
Work together on assignments and practice problems
Complete practice problems on hypothesis testing
Show steps
Repeated practice will help you master the concepts and improve your problem-solving skills.
Browse courses on
Hypothesis Testing
Show steps
-
Work through a variety of practice problems
-
Check your answers and identify areas for improvement
Data Analysis Project
Show steps
Applying your knowledge to a real-world project will deepen your understanding and demonstrate your skills.
Browse courses on
Data Analysis
Show steps
-
Define your research question and gather data
-
Clean and preprocess the data
-
Analyze the data using statistical methods
-
Interpret the results and draw conclusions
Participate in Data Science Competitions
Show steps
Participating in competitions will challenge you to apply your skills and knowledge in a real-world setting, fostering innovation and critical thinking.
Browse courses on
Kaggle Competitions
Show steps
-
Find data science competitions that align with your interests
-
Form a team or work independently
-
Develop and implement your data analysis and modeling approach
-
Submit your results and analyze your performance
Follow tutorials on Python for Statistical Analysis
Show steps
These tutorials will provide you with practical experience in using Python for statistical analysis, which will be essential for the course.
Show steps
Show steps
Show steps
- Find online tutorials or courses on Python for Statistical Analysis
- Follow the tutorials and complete the exercises
Read 'Statistical Inference' by George Casella and Roger Berger
Show steps
This book provides a comprehensive and rigorous treatment of statistical inference, which will supplement the course material.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters
- Take notes and summarize the key concepts
- Complete the practice exercises at the end of each chapter
Review Frequentist and Bayesian Statistics
Show steps
Reviewing these foundational topics will help strengthen your understanding of the course material.
Browse courses on
Mathematical Statistics
Show steps
Show steps
Show steps
- Review the key concepts of Frequentist and Bayesian Statistics
- Solve practice problems to test your understanding
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a study group with classmates
Show steps
Collaborating with peers will help you understand the material more deeply and identify areas where you need further clarification.
Show steps
Show steps
Show steps
- Form a study group with other classmates
- Meet regularly to discuss the course material
- Work together on assignments and practice problems
Complete practice problems on hypothesis testing
Show steps
Repeated practice will help you master the concepts and improve your problem-solving skills.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Work through a variety of practice problems
- Check your answers and identify areas for improvement
Data Analysis Project
Show steps
Applying your knowledge to a real-world project will deepen your understanding and demonstrate your skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define your research question and gather data
- Clean and preprocess the data
- Analyze the data using statistical methods
- Interpret the results and draw conclusions
Participate in Data Science Competitions
Show steps
Participating in competitions will challenge you to apply your skills and knowledge in a real-world setting, fostering innovation and critical thinking.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- Find data science competitions that align with your interests
- Form a team or work independently
- Develop and implement your data analysis and modeling approach
- Submit your results and analyze your performance
Career center
Learners who complete Математическая статистика и А/В тестирование will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their skills in mathematics, statistics, and computer programming to extract insights from data. They work in a variety of fields, such as healthcare, finance, and retail, to help businesses make better decisions. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by data scientists in their work.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They use their skills in mathematics, statistics, and computer programming to develop and implement algorithms that can learn from data and make predictions. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by machine learning engineers in their work.
Business Analyst
Business Analyst
Business Analysts help businesses to improve their performance by analyzing data and recommending changes to processes or strategies. They use their skills in mathematics, statistics, and business knowledge to identify opportunities for improvement. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by business analysts in their work.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of organizations. They use their skills in mathematics, statistics, and computer programming to develop and implement solutions to problems in a variety of areas, such as supply chain management, inventory management, and scheduling. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by operations research analysts in their work.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use their skills in mathematics, statistics, and computer programming to solve problems in a variety of fields. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by statisticians in their work.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their skills in mathematics, statistics, and computer programming to create software that meets the needs of users. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by software engineers in their work. This course would be particularly helpful for software engineers who are working on projects that involve data analysis or machine learning.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use their skills in mathematics, statistics, and computer programming to identify trends and patterns in data. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by data analysts in their work.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They use their skills in mathematics, statistics, and business knowledge to define product requirements, prioritize features, and track progress. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by product managers in their work. This course would be particularly helpful for product managers who are working on products that involve data analysis or machine learning.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. They use their skills in mathematics, statistics, and business knowledge to help businesses solve problems and improve their performance. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by consultants in their work. This course would be particularly helpful for consultants who are working on projects that involve data analysis or machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They use their skills in mathematics, statistics, and computer programming to develop and implement trading strategies. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by quantitative analysts in their work.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of fields, such as insurance, finance, and healthcare, to help businesses make informed decisions about risk management. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by actuaries in their work.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in specified populations. They use their skills in mathematics, statistics, and epidemiology to design and conduct studies, analyze data, and write reports. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by epidemiologists in their work.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the field of medicine. They use their skills in mathematics, statistics, and biology to design and conduct clinical trials, analyze data, and write reports. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by biostatisticians in their work.
Market Researcher
Market Researcher
Market Researchers collect and analyze data on consumer behavior to help businesses develop and market their products and services. They use their skills in mathematics, statistics, and research methods to design and conduct surveys, analyze data, and write reports. A course in mathematical statistics and A/B testing would provide you with a strong foundation in the statistical methods and techniques that are used by market researchers in their work.
Statistician, Social Sciences
Statistician, Social Sciences
A Statistician, Social Sciences may find the topics covered in this course, such as hypothesis testing and confidence intervals, to be directly applicable to their work. By taking this course, they can strengthen their understanding of these concepts and gain a deeper understanding of how to use them to solve problems in their field.
Applied Statistician
Applied Statistician
An Applied Statistician may find the topics covered in this course, such as data analysis and modeling, to be directly applicable to their work. By taking this course, they can gain hands-on experience with these concepts and learn how to use them to solve problems in the real world.
For more career information including salaries, visit:
OpenCourser.com/course/rbzkkl/matematicheskaia
Reading list
We've selected eight 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
Математическая статистика и А/В тестирование.
Provides a comprehensive overview of mathematical statistics, with a focus on applications in the social sciences. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis.
Provides a comprehensive overview of statistical inference. It covers a wide range of topics, including hypothesis testing, confidence intervals, and Bayesian inference.
Provides a comprehensive overview of Bayesian data analysis. It covers a wide range of topics, including Bayesian inference, Markov chain Monte Carlo methods, and Bayesian model selection.
Provides a comprehensive overview of statistical methods that are commonly used in psychology. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis.
Provides a comprehensive overview of statistical methods that are commonly used in business and economics. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis.
Provides a modern and accessible introduction to probability and statistics. It covers all the topics that are essential for students taking a first course in probability and statistics, including probability theory, hypothesis testing, and confidence intervals.
Provides a modern and accessible introduction to mathematical statistics. It covers all the topics that are essential for students taking a first course in mathematical statistics, including probability theory, hypothesis testing, and confidence intervals.
Provides a practical and accessible introduction to statistics. It covers all the topics that are essential for students taking a first course in statistics, including descriptive statistics, inferential statistics, and regression analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rbzkkl/matematicheskaia
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Time
28 hours
Via
Coursera
Institution
HSE University
Instructor
Ульянкин Филипп Валерьевич
Good to know
An excellent foundational course in math statistics
Well-grounded in scientific understanding and academic principles
Offers hands-on labs and interactive materials
Builds a strong foundation for beginners
Taught in Python, a highly relevant tool
Content is sufficiently detailed and comprehensive
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