This course is part of the Data Science Foundations: Statistical Inference Specialization

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104 ratings

Anne Dougherty +1 more instructor

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Part of the Master of Science in Data Science degree
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Data Science Foundations: Statistical Inference SpecializationUniversity of Colorado Boulder

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Course 1 of 3 in the

Data Science Foundations: Statistical Inference Specialization

Intermediate Level

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

Approx. 47 hours to complete

English

What you will learn

Explain why probability is important to statistics and data science.
See the relationship between conditional and independent events in a statistical experiment.
Calculate the expectation and variance of several random variables and develop some intuition.

Skills you will gain

Probability
central limit theorem
continuous random variables
Bayes' Theorem
discrete random variables

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 3 in the

Data Science Foundations: Statistical Inference Specialization

Intermediate Level

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

Approx. 47 hours to complete

English

Instructors

Instructor rating

4.75/5 (38 Ratings)

Anne Dougherty

Senior Instructor and Teaching Professor

Applied Mathematics

14,113 Learners

1 Course

Jem Corcoran

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Start Here!

2 hours to complete

2 readings

7 hours to complete

Descriptive Statistics and the Axioms of Probability

Understand the foundation 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 it’s fundamental importance for all of statistics and data science.

7 hours to complete

4 videos (Total 65 min), 2 readings, 3 quizzes

Week2

Week 2

7 hours to complete

Conditional Probability

7 hours to complete

2 videos (Total 52 min), 1 reading, 3 quizzes

Week3

Week 3

8 hours to complete

Discrete Random Variables

8 hours to complete

4 videos (Total 78 min), 1 reading, 3 quizzes

Week4

Week 4

9 hours to complete

Continuous Random Variables

9 hours to complete

4 videos (Total 97 min), 2 readings, 3 quizzes

Reviews

4.4

32 reviews

5 stars
74.28%
4 stars
11.42%
3 stars
3.80%
2 stars
1.90%
1 star
8.57%

TOP REVIEWS FROM PROBABILITY THEORY: FOUNDATION FOR DATA SCIENCE

by MBJun 15, 2022

This is a great course on probability. Although I felt like it was too easy and should include more PDFs (such as Beta and Gamma) and random variable transformations.

by JCJan 29, 2023

Marvelous course.......

It is the best course in all coursera. Thanks a lot <3

by PPApr 17, 2022

Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.

by JBDec 9, 2022

This is an excellent course to review foundational probability concepts. The instructor speaks clearly and goes through examples thoroughly for each concept.

View all reviews

About the Data Science Foundations: Statistical Inference Specialization

This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language.

This specialization 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.

Data Science Foundations: Statistical Inference

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Probability Theory: Foundation for Data Science

About this Course

What you will learn

Skills you will gain