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Probability and Statistics: To p or not to p?

Approx. 16 hours to complete

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Course Summary

This course is an introduction to probability and statistics with a focus on real-world applications. Students will learn the fundamentals of probability theory, statistical inference, and data analysis.

Key Learning Points

The course covers a wide range of topics, from basic probability concepts to statistical inference and hypothesis testing.
Students will gain hands-on experience with data analysis using real-world examples and case studies.
The course emphasizes the practical applications of probability and statistics in fields such as business, healthcare, and social sciences.

Learning Outcomes

Understand the fundamentals of probability theory and statistical inference
Gain hands-on experience with data analysis using real-world examples
Develop critical thinking and problem-solving skills

Prerequisites or good to have knowledge before taking this course

Basic knowledge of algebra and calculus
Familiarity with programming languages such as Python or R

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video Lectures

Similar Courses

Data Science Essentials
Introduction to Data Science in Python

Related Education Paths

Notable People in This Field

Statistician and Founder of FiveThirtyEight
Founder of Google Brain and Co-founder of Coursera

Related Books

Description

We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events.

To study, or not to study? To invest, or not to invest? To marry, or not to marry? While uncertainty makes decision-making difficult, it does at least make life exciting! If the entire future was known in advance, there would never be an element of surprise. Whether a good future or a bad future, it would be a known future. In this course we consider many useful tools to deal with uncertainty and help us to make informed (and hence better) decisions - essential skills for a lifetime of good decision-making. Key topics include quantifying uncertainty with probability, descriptive statistics, point and interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications of key terms and concepts seen throughout the course.

Outline

Dealing with Uncertainty and Complexity in a Chaotic World
Welcome!
1.1 The Monty Hall Problem
1.2 Decision Making Under Uncertainty
1.3 Uncertainty in the News
1.4 Simplicity vs. Complexity - The Need for Models
1.5 Safe to Assume? Beware, When Model Assumptions Go Wrong!
1.6 Roadmap of the Course
Week One Summary and Key Takeaways
1.1 The Monty Hall Problem
1.2 Decision Making Under Uncertainty
1.3 Uncertainty in the News
1.4 Simplicity vs. Complexity - The Need for Models
1.5 Safe to Assume? Beware, When Model Assumptions Go Wrong!
1.6 Roadmap of the Course
Week One Quiz

Quantifying Uncertainty With Probability
2.1 Probability Principles
2.2 Simple Probability Distributions
2.3 Expectation of Random Variables
2.4 Bayesian Updating
2.5 Parameters
2.6 The Distribution Zoo
Week Two Summary and Key Takeaways
2.1 Probability Principles
2.2 Simple Probability Distributions
2.3 Expectation of Random Variables
2.4 Bayesian Updating
2.5 Parameters
2.6 The Distribution Zoo
Week Two Quiz

Describing The World The Statistical Way
3.1 Classify Your Variables!
3.2 Data Visualisation
3.3 Descriptive Statistics - Measures of Central Tendency
3.4 Descriptive Statistics - Measures of Spread
3.5 The Normal Distribution
3.6 Variance of Random Variables
Week Three Summary and Key Takeaways
3.1 Classify Your Variables!
3.2 Data Visualisation
3.3 Descriptive Statistics - Measures of Central Tendency
3.4 Descriptive Statistics - Measures of Spread
3.5 The Normal Distribution
3.6 Variance of Random Variables

On Your Marks, Get Set, Infer!
4.1 Introduction to Sampling
4.2 Random Sampling
4.3 Further Random Sampling
4.4 Sampling Distributions
4.5 Sampling Distribution of the Sample Mean
4.6 Confidence Intervals
Week Four Summary and Key Takeaways
4.1 Introduction to Sampling
4.2 Random Sampling
4.3 Further Random Sampling
4.4 Sampling Distributions
4.5 Sampling Distribution of the Sample Mean
4.6 Confidence Intervals

To p Or Not To p?
5.1 Statistical Juries
5.2 Type I and Type II errors
5.3 P-values, Effect Size and Sample Size Influences
5.4 Testing a Population Mean Claim
5.5 The Central Limit Theorem
5.6 Proportions: Confidence Intervals and Hypothesis Testing
Week Five Summary and Key Takeaways
5.1Statistical Juries
5.2 Type I and Type II errors
5.3 P-values, Effect Size and Sample Size Influences
5.4 Testing a Population Mean Claim
5.5 The Central Limit Theorem
5.6 Proportions: Confidence Intervals and Hypothesis Testing

Applications
6.1 Decision Tree Analysis
6.2 Risk
6.3 Linear Regression
6.4 Linear Programming
6.5 Monte Carlo Simulation
6.6 Overview of the Course and Next Steps
6.1 Decision Tree Analysis
6.2 Risk
6.3 Linear regression
6.4 Linear Programming
6.5 Monte Carlo Simulation

Summary of User Reviews

Find out what users are saying about the Probability and Statistics course on Coursera. Learn about the overall quality of the course and what users found to be most helpful. Discover pros and cons that users mention most frequently.

Key Aspect Users Liked About This Course

The course is comprehensive and covers a wide range of topics.

Pros from User Reviews

Well-structured and organized course materials
Engaging video lectures and interactive quizzes
Exercises and assignments help to reinforce learning
Accessible to learners with different levels of knowledge

Cons from User Reviews

Some students found the course too challenging
Course content can be too basic for advanced learners
Limited interaction with instructors and other students
Some technical issues reported with the platform

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