Introduction to Probability and Data with R
- 4.7
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Course Summary
This course provides an introduction to probability, including basic concepts such as random variables, distributions, and expected values.Key Learning Points
- Learn key probability concepts and their applications
- Understand the basics of random variables, distributions, and expected values
- Practice solving problems and applying probability in real-world scenarios
Related Topics for further study
Learning Outcomes
- Understand the fundamental concepts of probability theory
- Apply probability to real-world scenarios
- Solve basic problems involving random variables and distributions
Prerequisites or good to have knowledge before taking this course
- Basic algebra skills
- Familiarity with calculus (recommended)
Course Difficulty Level
BeginnerCourse Format
- Online self-paced
- Video lectures
- Interactive quizzes
Similar Courses
- Data Analysis and Statistical Inference
- Introduction to Data Science in Python
- Statistics with R
Related Education Paths
Notable People in This Field
- Nate Silver
- Edward Tufte
Related Books
Description
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
Outline
- About Introduction to Probability and Data
- Introduction to Statistics with R
- More about Introduction to Probability and Data
- Introduction to Data
- Introduction
- Data Basics
- Observational Studies & Experiments
- Sampling and sources of bias
- Experimental Design
- (Spotlight) Random Sample Assignment
- Lesson Learning Objectives
- Suggested Readings and Practice
- Week 1 Practice Quiz
- Week 1 Quiz
- Introduction to Data Project
- About Lab Choices (Read Before Selection)
- Week 1 Lab Instructions (RStudio)
- Week 1 Lab: Introduction to R and RStudio
- Exploratory Data Analysis and Introduction to Inference
- Visualizing Numerical Data
- Measures of Center
- Measures of Spread
- Robust Statistics
- Transforming Data
- Exploring Categorical Variables
- Introduction to Inference
- Lesson Learning Objectives
- Lesson Learning Objectives
- Suggested Readings and Practice
- Week 2 Practice Quiz
- Week 2 Quiz
- Exploratory Data Analysis and Introduction to Inference Project
- Week 2 Lab Instructions (RStudio)
- Week 2 Lab Instructions (RStudio Cloud)
- Week 2 Lab: Introduction to Data
- Introduction to Probability
- Introduction
- Disjoint Events + General Addition Rule
- Independence
- Probability Examples
- (Spotlight) Disjoint vs. Independent
- Conditional Probability
- Probability Trees
- Bayesian Inference
- Examples of Bayesian Inference
- Lesson Learning Objectives
- Lesson Learning Objectives
- Suggested Readings and Practice
- Week 3 Practice Quiz
- Week 3 Quiz
- Introduction to Probability Project
- Week 3 Lab Instructions (RStudio)
- Week 3 Lab Instructions (RStudio Cloud)
- Week 3 Lab: Probability
- Probability Distributions
- Normal Distribution
- Evaluating the Normal Distribution
- Working with the Normal Distribution
- Binomial Distribution
- Normal Approximation to Binomial
- Working with the Binomial Distribution
- Lesson Learning Objectives
- Lesson Learning Objectives
- Suggested Readings and Practice
- Data Analysis Project Example
- Week 4 Practice Quiz
- Week 4 Quiz
- Data Analysis Project
- Project Information
Summary of User Reviews
Find out what people are saying about Coursera's Probability - The Science of Uncertainty and Data course. Discover the pros and cons of this course, including a focus on real-world applications. Overall, users have given this course high marks.Key Aspect Users Liked About This Course
Many users found the real-world examples and applications to be very helpful in understanding the concepts.Pros from User Reviews
- Real-world examples and applications were helpful
- Course content was well-organized and easy to follow
- Instructors were knowledgeable and engaging
- Course materials were high quality
- Assignments and quizzes were challenging but fair
Cons from User Reviews
- Some users found the material to be too basic or simplistic
- A few users had technical issues with the online platform
- Some users felt that the course moved too quickly and required more time to absorb the material
- A few users found the course to be too theoretical and not practical enough
- Some users felt that the course lacked depth in certain areas
Mine Çetinkaya-Rundel
4.7 Raiting
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