- Level Professional
- المدة 48 ساعات hours
- الطبع بواسطة University of Colorado Boulder
-
Offered by
عن
Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder Logo adapted from photo by Christopher Burns on Unsplash.الوحدات
Welcome to Probability Theory
1
Discussions
- Introduce Yourself
1
Labs
- Introduction to Jupyter Notebooks and R
3
Readings
- Earn Academic Credit for your Work!
- Course Support
- Course Resources and Reading
Introduction to Probability
1
Videos
- Intro to Probability
1
Readings
- Intro to Probability
Remaining Lectures
2
Videos
- Axioms of Probability
- Counting: Permutations and Combinations
Assessments
- Homework: Axioms of Probability
1
Labs
- Guided Exploratory Ungraded Lab
1
Readings
- Introducing the formula sheet for this course
1
Quiz
- Homework: Descriptive Statistics and the Axioms of Probability
Conditional Probability and Bayes Theorem
1
Videos
- Conditional Probability and Bayes Theorem
1
Readings
- Conditional Probability and Bayes Theorem
Independent Events
1
Videos
- Independent Events
Assessments
- Homework: Bayes Theorem
1
Labs
- Guided Exploratory Ungraded Lab
1
Quiz
- Homework: Conditional Probability
Introduction to Discrete Random Variables
1
Videos
- Discrete Random Variables
1
Readings
- Discrete Random Variables
Examples of Discrete Random Variables
3
Videos
- Bernoulli and Geometric Random Variables
- Expectation and Variance
- Binomial and Negative Binomial Random Variables
Assessments
- Homework: Calculations with Discrete Random Variables
1
Labs
- Guided Exploratory Ungraded Lab
1
Quiz
- Homework: Discrete Random Variables
Introduction to Continuous Random Variables
1
Videos
- Continuous Random Variables
1
Readings
- Continuous random variables
Normal (Gaussian) Random Variable
2
Videos
- The Gaussian (normal) Random Variable Part 1
- The Normal Random Variable Part 2
1
Readings
- Normal Random Variable
Additional Continuous Random Variables
1
Videos
- The Poisson and Exponential Random Variables
Assessments
- Homework: Continuous Random Variables and Normal Random Variables
1
Labs
- Guided Exploratory Ungraded Lab
1
Quiz
- Homework: Continuous Random Variables
Introduction to Covariance and Correlation
1
Videos
- Covariance and Correlation
1
Readings
- Covariance and Correlation
Additional topics in Expectation and Variance
2
Videos
- More on Expectation and Variance
- Jointly Distributed Random Variables
Assessments
- Homework: Calculations of Covariance and Correlation in Various Examples
1
Quiz
- Homework: Joint Distributions and Covariance
The Central Limit Theorem
2
Videos
- Introduction to the Central Limit Theorem
- Central Limit Theorem Examples
1
Readings
- Central Limit Theorem
Assessments
- Homework: Working with Normal Random Variables and the CLT
1
Labs
- Guided Exploratory Ungraded Lab
1
Quiz
- Homework: Central Limit Theorem
Auto Summary
Discover the foundations of probability and its critical role in data science with this engaging course led by Coursera. Dive into calculating probabilities, understanding independent and dependent outcomes, and exploring conditional events. Study discrete and continuous random variables, Gaussian distributions, and the Central Limit Theorem. Perfect for professionals in maths, statistics, and data science, this course is part of CU Boulder’s MS-DS degree. Available through Starter and Professional subscriptions, it offers 2880 minutes of in-depth content for a comprehensive learning experience.

Anne Dougherty

Jem Corcoran