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Rafael Irizarry
In this course, part of our Professional Certificate Program in Data Science,you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
Read more
In this course, part of our Professional Certificate Program in Data Science,you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
Read more
In this course, part of our Professional Certificate Program in Data Science,you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance.
Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.
Register for this course and see more details by visiting:
OpenCourser.com/course/54e7vf/data
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What's inside
Learning objectives
- Important concepts in probability theory including random variables and independence
- How to perform a monte carlo simulation
- The meaning of expected values and standard errors and how to compute them in r
- The importance of the central limit theorem
- Important concepts in probability theory including random variables and independence
- How to perform a monte carlo simulation
- The meaning of expected values and standard errors and how to compute them in r
- The importance of the central limit theorem
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners with key concepts in probability theory, including random variables and independence
Develops core skills in probability theory for data scientists, such as expected values, standard errors, and the Central Limit Theorem
Teaches foundational probability concepts for analyzing data affected by chance, essential for data scientists
Introduces important concepts in probability theory through hands-on simulations and real-world examples
Taught by Rafael Irizarry, a renowned biostatistician and data scientist
Save this course
Save Data Science: Probability to your list so you can find it easily later:
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Reviews summary
Introductory probability course
Learners say this course provides a concise and clear introduction to probability. They also say that the course is very good for beginners and that it helped them understand the basics of the subject. However, some learners say that the course lacks theoretical depth and explanations and that it focuses more on the practical use of R.
This course focuses on the practical use of R for probability.
"More focused on the practical use of R to do some Probabilities."
This course is a great introduction to probability.
"very good *intro* to the subject."
"very clear to understand, and made me dig deeper on my own :)"
"notice that this is an entry-level to the subject"
This course lacks theoretical depth and explanations.
"Concise and clear, but lack of theoretical depth and explanations."
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 Data Science: Probability with these
activities:
Review Probability Basics
Show steps
Helps refresh and recall previously learned probability concepts to strengthen the foundation for this course.
Show steps
-
Review the definition of probability and different types of probability distributions.
-
Solve practice problems on calculating probabilities using basic formulas and rules.
-
Read through examples and case studies to understand the practical applications of probability.
Read: 'Probability and Statistics' by Morris H. DeGroot
Show steps
Provides a comprehensive reference for further exploration and reinforcement of probability concepts.
View
Probability and Statistics
on Amazon
Show steps
-
Read selected chapters relevant to the course material.
-
Review key concepts and examples to enhance understanding.
Random Variable Exercises
Show steps
Provides focused practice on working with random variables, enhancing understanding of their behavior and properties.
Browse courses on
Random Variables
Show steps
-
Work through a set of exercises on identifying and classifying random variables.
-
Practice calculating probability distributions for different types of random variables.
-
Analyze real-world scenarios involving random variables to apply knowledge.
Three other activities
Expand to see all activities and additional details
Show all six activities
Central Limit Theorem Discussion Group
Show steps
Fosters collaboration and deepens understanding through peer discussions on the Central Limit Theorem and its applications.
Browse courses on
Central Limit Theorem
Show steps
-
Join a study group or online forum to connect with peers.
-
Participate in discussions, sharing insights and asking questions about the Central Limit Theorem.
-
Work together to solve problems and clarify concepts related to the theorem.
Monte Carlo Simulation Tutorial
Show steps
Builds practical skills in performing Monte Carlo simulations, enabling students to apply them in real-world scenarios.
Browse courses on
Monte Carlo Simulation
Show steps
-
Follow an interactive tutorial on the principles and steps of Monte Carlo simulation.
-
Implement a Monte Carlo simulation in R or Python to gain hands-on experience.
-
Analyze the results of the simulation to draw inferences and make predictions.
Expected Values and Standard Errors Project
Show steps
Encourages students to apply their knowledge by calculating expected values and standard errors in a practical context.
Browse courses on
Expected Values
Show steps
-
Gather real-world data related to a specific research question.
-
Calculate the expected value and standard error for the data collected.
-
Write a report summarizing the findings and implications of the analysis.
Review Probability Basics
Show steps
Helps refresh and recall previously learned probability concepts to strengthen the foundation for this course.
Show steps
Show steps
Show steps
- Review the definition of probability and different types of probability distributions.
- Solve practice problems on calculating probabilities using basic formulas and rules.
- Read through examples and case studies to understand the practical applications of probability.
Read: 'Probability and Statistics' by Morris H. DeGroot
Show steps
Provides a comprehensive reference for further exploration and reinforcement of probability concepts.
View
Probability and Statistics
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters relevant to the course material.
- Review key concepts and examples to enhance understanding.
Random Variable Exercises
Show steps
Provides focused practice on working with random variables, enhancing understanding of their behavior and properties.
Browse courses on
Random Variables
Show steps
Show steps
Show steps
- Work through a set of exercises on identifying and classifying random variables.
- Practice calculating probability distributions for different types of random variables.
- Analyze real-world scenarios involving random variables to apply knowledge.
Three other activities
Expand to see all activities and additional details
Show all six activities
Central Limit Theorem Discussion Group
Show steps
Fosters collaboration and deepens understanding through peer discussions on the Central Limit Theorem and its applications.
Browse courses on
Central Limit Theorem
Show steps
Show steps
Show steps
- Join a study group or online forum to connect with peers.
- Participate in discussions, sharing insights and asking questions about the Central Limit Theorem.
- Work together to solve problems and clarify concepts related to the theorem.
Monte Carlo Simulation Tutorial
Show steps
Builds practical skills in performing Monte Carlo simulations, enabling students to apply them in real-world scenarios.
Browse courses on
Monte Carlo Simulation
Show steps
Show steps
Show steps
- Follow an interactive tutorial on the principles and steps of Monte Carlo simulation.
- Implement a Monte Carlo simulation in R or Python to gain hands-on experience.
- Analyze the results of the simulation to draw inferences and make predictions.
Expected Values and Standard Errors Project
Show steps
Encourages students to apply their knowledge by calculating expected values and standard errors in a practical context.
Browse courses on
Expected Values
Show steps
Show steps
Show steps
- Gather real-world data related to a specific research question.
- Calculate the expected value and standard error for the data collected.
- Write a report summarizing the findings and implications of the analysis.
Career center
Learners who complete Data Science: Probability will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians use probability theory to develop models for making inferences about populations. This course will provide you with a strong foundation in probability theory and teach you如何应用这些概念来构建统计模型。With these skills, you'll be well-prepared to succeed as a Statistician.
Data Analyst
Data Analyst
Data Analysts use statistical methods to uncover patterns in data. Understanding probability theory is essential to drawing conclusions from data and making sound business decisions. This course will provide you with a strong foundation in probability theory and teach you how to apply these concepts to real-world data analysis. With these skills, you'll be well-equipped to succeed as a Data Analyst.
Data Scientist
Data Scientist
Data Scientists use probability theory to develop models for making predictions and classifications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use probability theory to develop models for making investment decisions.
Actuary
Actuary
Actuaries use probability theory to develop models for assessing risk.
Risk Manager
Risk Manager
Risk Managers use probability theory to develop models for assessing risk.
Financial Analyst
Financial Analyst
Financial Analysts use probability theory to develop models for making investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use probability theory to develop models for optimizing business operations.
Market Researcher
Market Researcher
Market Researchers use probability theory to develop models for understanding consumer behavior.
Insurance Underwriter
Insurance Underwriter
Insurance Underwriters use probability theory to develop models for assessing risk.
Epidemiologist
Epidemiologist
Epidemiologists use probability theory to develop models for understanding the spread of disease.
Economist
Economist
Economists use probability theory to develop models for understanding economic behavior.
Biostatistician
Biostatistician
Biostatisticians use probability theory to develop models for understanding biological data.
Teacher
Teacher
Teachers may use probability theory to help students understand statistics and probability.
Software Engineer
Software Engineer
Software Engineers may use probability theory to develop models for software reliability.
For more career information including salaries, visit:
OpenCourser.com/course/54e7vf/data
Reading list
We've selected 14 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
Data Science: Probability.
A comprehensive textbook that covers a wide range of topics in statistical learning. Useful as a reference for more advanced topics.
A comprehensive textbook that covers Bayesian data analysis. Useful as a reference for more advanced topics.
A comprehensive textbook that covers a wide range of topics in statistical inference. Useful as a reference for more advanced topics.
Save
A reference book that covers a wide range of topics in financial data and risk management. Useful as a reference for more advanced topics.
A textbook that covers stochastic calculus for finance. May be useful as additional reading.
A well-respected textbook that covers the basics of mathematical statistics. Useful as a reference for more advanced topics.
Teaches the practical aspects of machine learning using R. May be useful as a complementary resource.
Provides an introduction to risk management and financial institutions. May be useful as additional reading.
Provides an introduction to causal inference. May be useful as additional reading.
A textbook that covers econometrics. May be useful as additional reading.
Provides an introduction to machine learning for finance. May be useful as additional reading.
A well-written textbook that covers the basics of probability theory. Useful as a reference or for additional reading.
Provides an introduction to data science and its applications in business. May be useful as background reading.
Provides a solid foundation in probability and statistics. May be useful as background reading.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/54e7vf/data
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Effort
1 to 2 hours per week for 8 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructor
Rafael Irizarry
Sessions
October 16, 2024
- June 18, 2025
April 17, 2024
- December 18, 2024
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
This course belongs to a
collection
called
Data Science. It's comprised of nine courses:
Good to know
Builds a strong foundation for beginners with key concepts in probability theory, including random variables and independence
Develops core skills in probability theory for data scientists, such as expected values, standard errors, and the Central Limit Theorem
Teaches foundational probability concepts for analyzing data affected by chance, essential for data scientists
Introduces important concepts in probability theory through hands-on simulations and real-world examples
Taught by Rafael Irizarry, a renowned biostatistician and data scientist
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