University of Colorado Boulder
Statistical Inference for Estimation in Data Science
University of Colorado Boulder

Statistical Inference for Estimation in Data Science

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

Taught in English

Some content may not be translated

Jem Corcoran

Instructor: Jem Corcoran

5,733 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.0

(60 reviews)

Intermediate level

Recommended experience

29 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll learn

  • Identify characteristics of “good” estimators and be able to compare competing estimators.

  • Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.

  • Construct and interpret confidence intervals for one and two population means, one and two population proportions, and a population variance.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.0

(60 reviews)

Intermediate level

Recommended experience

29 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Data Science Foundations: Statistical Inference Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 6 modules in this course

Welcome to the course! This module contains logistical information to get you started!

What's included

1 video7 readings1 app item1 ungraded lab

In this module you will learn how to estimate parameters from a large population based only on information from a small sample. You will learn about desirable properties that can be used to help you to differentiate between good and bad estimators. We will review the concepts of expectation, variance, and covariance, and you will be introduced to a formal, yet intuitive, method of estimation known as the "method of moments".

What's included

10 videos12 readings4 quizzes1 programming assignment1 ungraded lab

In this module we will learn what a likelihood function is and the concept of maximum likelihood estimation. We will construct maximum likelihood estimators (MLEs) for one and two parameter examples and functions of parameters using the invariance property of MLEs.

What's included

5 videos6 readings2 quizzes1 programming assignment1 ungraded lab

In this module we will explore large sample properties of maximum likelihood estimators including asymptotic unbiasedness and asymptotic normality. We will learn how to compute the “Cramér–Rao lower bound” which gives us a benchmark for the smallest possible variance for an unbiased estimator.

What's included

5 videos6 readings2 quizzes1 programming assignment1 ungraded lab

In this module we learn about the theory of “interval estimation”. We will learn the definition and correct interpretation of a confidence interval and how to construct one for the mean of an unseen population based on both large and small samples. We will look at the cases where the variance is known and unknown.

What's included

5 videos6 readings2 quizzes1 programming assignment2 ungraded labs

In this module, we will generalize the lessons of Module 4 so that we can develop confidence intervals for other quantities of interest beyond the distribution mean and for other distributions entirely. This module covers two sample confidence intervals in more depth, and confidence intervals for population variances and proportions. We will also learn how to develop confidence intervals for parameters of interest in non-normal distributions.

What's included

5 videos7 readings2 quizzes1 ungraded lab

Instructor

Instructor ratings
4.2 (22 ratings)
Jem Corcoran
University of Colorado Boulder
6 Courses24,930 learners

Offered by

Recommended if you're interested in Probability and Statistics

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 60

4.0

60 reviews

  • 5 stars

    55%

  • 4 stars

    21.66%

  • 3 stars

    5%

  • 2 stars

    10%

  • 1 star

    8.33%

DP
5

Reviewed on Jan 27, 2024

KH
5

Reviewed on Feb 27, 2024

New to Probability and Statistics? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions