We may earn an affiliate commission when you visit our partners.

John Tsitsiklis,
Patrick Jaillet,
Qing He,
Jimmy Li,
Jagdish Ramakrishnan,
Katie Szeto,
Kuang Xu,
Dimitri Bertsekas,
Eren Can Kizildag,
and
Karene Chu

John Tsitsiklis,
Patrick Jaillet,
Qing He,
Jimmy Li,
Jagdish Ramakrishnan,
Katie Szeto,
Kuang Xu,
Dimitri Bertsekas,
Eren Can Kizildag,
and
Karene Chu

Read more

Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.

The course covers all of the basic probability concepts, including:

- multiple discrete or continuous random variables, expectations, and conditional distributions
- laws of large numbers
- the main tools of Bayesian inference methods
- an introduction to random processes (Poisson processes and Markov chains)

The contents of this courseare heavily based upon the corresponding MIT class -- Introduction to Probability -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class but will enable you to apply the tools of probability theory to real-world applications or to your research.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

Register for this course and see more details by visiting:
**
OpenCourser.com/course/dldr0j/probability
**

- The basic structure and elements of probabilistic models
- Random variables, their distributions, means, and variances
- Probabilistic calculations

- Inference methods
- Laws of large numbers and their applications
- Random processes

- The basic structure and elements of probabilistic models
- Random variables, their distributions, means, and variances
- Probabilistic calculations
- Inference methods
- Laws of large numbers and their applications
- Random processes

Unit 1: Probability models and axioms

Probability models and axioms

Mathematical background: Sets; sequences, limits, and series; (un)countable sets.

Read more

Unit 2: Conditioning and independence

Conditioning and Bayes' rule

Independence

Unit 3: Counting

Counting

Unit 4: Discrete random variables

Probability mass functions and expectations

Variance; Conditioning on an event; Multiple random variables

Conditioning on a random variable; Independence of random variables

Unit 5: Continuous random variables

Probability density functions

Conditioning on an event; Multiple random variables

Conditioning on a random variable; Independence; Bayes' rule

Unit 6: Further topics on random variables

Derived distributions

Sums of independent random variables; Covariance and correlation

Conditional expectation and variance revisited; Sum of a random number of independent random variables

Unit 7: Bayesian inference

Introduction to Bayesian inference

Linear models with normal noise

Least mean squares (LMS) estimation

Linear least mean squares (LLMS) estimation

Unit 8: Limit theorems and classical statistics

Inequalities, convergence, and the Weak Law of Large Numbers

The Central Limit Theorem (CLT)

An introduction to classical statistics

Unit 9: Bernoulli and Poisson processes

The Bernoulli process

The Poisson process

More on the Poisson process

Unit 10 (Optional): Markov chains

Finite-state Markov chains

Steady-state behavior of Markov chains

Absorption probabilities and expected time to absorption

Good to know

Know what's good ,
what to watch for , and
possible dealbreakers

Examines a broad spectrum of probability principles, from foundational theories to applications in data science

Led by a seasoned group of instructors recognized for their expertise in probability, statistics, and data science

Its rigorous and precise approach, grounded in mathematical modeling, provides a solid theoretical foundation

Provides a thorough introduction to probability theory, suitable for beginners with minimal background in the subject

Covers a wide range of probability concepts, including random variables, distributions, and inference methods

Emphasizes practical applications of probability theory, making it relevant to real-world scenarios in various fields

Save Probability - The Science of Uncertainty and Data to your list so you can find it easily later:

Learners say this probability course is a top-rated offering with challenging but engaging assignments. The course is highly rigorous and demanding but provides a solid mathematical foundation for understanding the fundamentals of probability. Instructors present clear lectures and explanations, making complex concepts more approachable. The course team and forum discussions are also quite helpful. Overall, this course is highly recommended for anyone interested in probability, whether for academic or professional pursuits.

The course team and forum discussions provide helpful support to students.

"The course team help a lot."

"Many thanks to the teaching teams who were always helpful and the other students for their great discussions in the forum."

Instructors present clear lectures and explanations, making complex probability concepts more understandable.

"Clear explanation of theory and representative examples presented by a top scientist."

"The teacher is clear and the his explanations really help to understand notion that can appear complicated at first glance."

The course offers challenging and engaging assignments that help students develop practical skills.

"You will get the practical skills."

"The exercices are designed to help the understanding."

"After taking many probability courses, this is the first time I feel I really understand it. It is fun and rigorous at the same."

The course covers a lot of material and is quite rigorous and demanding.

"This course is just perfect! One of the best moocs you can find ever! It covers a lot, and it's rigorous and demanding."

"After completed all the course of the MicroMaster, and now reviewing for the final exam, my rate didn't change."

The course may not be suitable for beginners who have no prior knowledge of probability or statistics.

"I would not recommend it to somebody that doesn't have any idea of probability or statistics."

Learners who complete Probability - The Science of Uncertainty and Data will develop knowledge and skills
that may be useful to these careers:

Statistician

A Statistician collects, analyzes, interprets, and presents data to provide insights for decision-making. This course, Probability - The Science of Uncertainty and Data, provides a solid foundation in probability and statistics, which are essential for understanding the role of data in decision-making. The course covers topics such as probability distributions, random variables, statistical inference, and regression analysis, which are all highly relevant to the field of statistics.

Data Scientist

A Data Scientist extracts knowledge and insights from data by applying statistical and machine learning techniques. This course, Probability - The Science of Uncertainty and Data, provides a solid foundation in probability and statistics, which are essential for understanding the role of data in machine learning and artificial intelligence. The course covers topics such as probability distributions, random variables, statistical inference, and regression analysis, which are all highly relevant to data science.

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course, Probability - The Science of Uncertainty and Data, provides a solid foundation in probability and statistics, which are essential for understanding the role of probability and randomness in machine learning. The course covers topics such as probability distributions, random variables, statistical inference, and regression analysis, which are all highly relevant to machine learning engineering.

Risk Manager

A Risk Manager identifies, assesses, and mitigates risks to an organization. This course, Probability - The Science of Uncertainty and Data, is designed for those interested in gaining a deep understanding of probability and statistics, which are essential for understanding risk assessment and management. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to risk management.

Actuary

An Actuary analyzes and manages financial risks for insurance companies and other financial institutions. The course, Probability - The Science of Uncertainty and Data, can be particularly useful for those interested in gaining a strong foundation in probability and statistics, which are essential for understanding actuarial science. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to actuarial work.

Quantitative Analyst

A Quantitative Analyst develops and uses mathematical and statistical models to analyze financial data and make investment decisions. This course, Probability - The Science of Uncertainty and Data, can be particularly useful for those interested in building a strong foundation in probability and statistics, which are essential for understanding financial modeling and risk assessment. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to quantitative analysis.

Biostatistician

A Biostatistician applies statistical methods to solve problems in biology and medicine. The course, Probability - The Science of Uncertainty and Data, can be particularly useful for those interested in gaining a strong foundation in probability and statistics, which are essential for understanding biostatistical research. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to biostatistics.

Epidemiologist

An Epidemiologist investigates the causes and patterns of health and disease in populations. The course, Probability - The Science of Uncertainty and Data, can be particularly useful for those interested in gaining a strong foundation in probability and statistics, which are essential for understanding epidemiological research. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to epidemiology.

Financial Analyst

A Financial Analyst analyzes investment opportunities and provides recommendations to clients. This course, Probability - The Science of Uncertainty and Data, can be particularly useful for those interested in building a solid foundation in probability and statistics, which are essential for understanding financial modeling and risk assessment. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to financial analysis.

Market Researcher

A Market Researcher collects and analyzes data about markets, customers, and competitors. The course, Probability - The Science of Uncertainty and Data, can be helpful for those interested in gaining a strong foundation in probability and statistics, which are essential for understanding data collection and analysis techniques. The course covers topics such as probability distributions, random variables, and statistical inference, which are all highly relevant to market research.

Data Analyst

A Data Analyst typically analyzes a large amount of data by applying statistical methods and techniques. This course, Probability - The Science of Uncertainty and Data, may be useful because it covers several topics that are relevant to data science, including probability models, random variables, and statistical inference. This course can be particularly relevant to those who wish to gain a solid understanding of the foundational concepts of probability and statistics, which are essential for many data analyst roles.

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical techniques to solve complex problems in business and industry. This course, Probability - The Science of Uncertainty and Data, may be useful because it covers several topics that are relevant to operations research, including probability models, optimization, and decision theory.

Operations Manager

An Operations Manager plans and oversees the production and delivery of goods and services. The course, Probability - The Science of Uncertainty and Data, may be useful because it covers several topics that are relevant to operations management, including probability models, random variables, and statistical inference.

Economist

An Economist studies the production, distribution, and consumption of goods and services. The course, Probability - The Science of Uncertainty and Data, may be useful because it covers several topics that are relevant to economics, including probability models, random variables, and statistical inference.

Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course, Probability - The Science of Uncertainty and Data, may be useful because it covers several topics that are relevant to software engineering, including probability models, random variables, and statistical inference.

For more career information including salaries, visit:
**
OpenCourser.com/course/dldr0j/probability
**

We've selected 18 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
Probability - The Science of Uncertainty and Data.

For more information about how these books relate to this course, visit:
**
OpenCourser.com/course/dldr0j/probability
**

Here are nine courses similar to
Probability - The Science of Uncertainty and Data.

Manufacturing Systems I

OpenCourser.com/course/if8pzj/manufacturing

Manufacturing Systems I

Most relevant

Fundamentals of Statistics

OpenCourser.com/course/qpmty2/fundamentals

Fundamentals of Statistics

Most relevant

Data Science: Probability

OpenCourser.com/course/54e7vf/data

Data Science: Probability

Most relevant

Probability Theory, Statistics and Exploratory Data Analysis

OpenCourser.com/course/bzdrti/probability

Probability Theory, Statistics and Exploratory Data...

Most relevant

Introduction to Probability

OpenCourser.com/course/rz4zmu/introduction

Introduction to Probability

Most relevant

Probability and Statistics in Data Science using Python

OpenCourser.com/course/nkbj82/probability

Probability and Statistics in Data Science using Python

Most relevant

Data Analysis: Statistical Modeling and Computation in Applications

OpenCourser.com/course/z9hkif/data

Data Analysis: Statistical Modeling and Computation in...

Most relevant

Probability Theory: Foundation for Data Science

OpenCourser.com/course/zdy2qs/probability

Probability Theory: Foundation for Data Science

Most relevant

Mathematical Methods for Quantitative Finance

OpenCourser.com/course/qxe3du/mathematical

Mathematical Methods for Quantitative Finance

Most relevant

Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser