Discover powerful statistical methods and probability concepts to analyze data and make confident decisions in complex, uncertain environments.
Discover powerful statistical methods and probability concepts to analyze data and make confident decisions in complex, uncertain environments.
This course equips learners with essential tools to navigate uncertainty and make better decisions. Covering key topics in probability and statistics, the course progresses from basic concepts to advanced applications. Students will learn about probability principles, descriptive statistics, sampling methods, hypothesis testing, and practical applications like decision tree analysis and Monte Carlo simulation. Through a mix of theoretical understanding and practical examples, learners will develop skills to quantify uncertainty, interpret data, and apply statistical reasoning to real-world problems. Ideal for beginners, this course provides a solid foundation in statistical thinking and decision-making under uncertainty.
4.6
(1,449 ratings)
83,578 already enrolled
Instructors:
English
22 languages available
What you'll learn
Understand and apply basic probability concepts
Use descriptive statistics to summarize and visualize data
Perform sampling and construct confidence intervals
Conduct hypothesis tests and interpret p-values
Apply statistical concepts to real-world decision-making scenarios
Understand advanced topics like linear regression and Monte Carlo simulation
Skills you'll gain
This course includes:
443 Minutes PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This course provides a comprehensive introduction to probability and statistics, focusing on their practical applications in decision-making under uncertainty. The curriculum is structured into six modules, covering a wide range of topics from basic probability concepts to advanced statistical techniques. Students will learn about quantifying uncertainty with probability, descriptive statistics, sampling methods, confidence intervals, and hypothesis testing. The course also explores practical applications such as decision tree analysis, risk assessment, linear regression, and Monte Carlo simulation. Through a combination of video lectures, readings, quizzes, and peer-reviewed assignments, learners will develop a solid understanding of statistical concepts and their real-world applications. By the end of the course, students will be equipped with essential tools to analyze data, interpret statistical results, and make informed decisions in various professional and personal contexts.
Dealing with Uncertainty and Complexity in a Chaotic World
Module 1 · 2 Hours to complete
Quantifying Uncertainty With Probability
Module 2 · 2 Hours to complete
Describing The World The Statistical Way
Module 3 · 3 Hours to complete
On Your Marks, Get Set, Infer!
Module 4 · 2 Hours to complete
To p Or Not To p?
Module 5 · 3 Hours to complete
Applications
Module 6 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Assistant Professorial Lecturer in Statistics at LSE, Expert in Market Research and Forensic Statistics
James is an Assistant Professorial Lecturer in Statistics at the London School of Economics and Political Science (LSE), where he earned his PhD in Statistics in 2010 with a thesis titled "To p, or not to p?" He teaches large service-level undergraduate courses in mathematical statistics and quantitative methods, alongside elective courses in market research. His research interests span market research techniques and forensic statistics, exploring the intersection of statistics and the law. James has actively participated in LSE's Summer School and the University of London International Programmes for many years. Beyond academia, he has worked on various quantitative consultancy projects, including work in the art market and with the World Gold Council, and has presented seminars at the UK Parliament.
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4.6 course rating
1,449 ratings
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