University of Colorado Boulder - Statistical Inference for Estimation in Data Science
- Offered byCoursera
Statistical Inference for Estimation in Data Science at Coursera Overview
Duration | 25 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Statistical Inference for Estimation in Data Science at Coursera Highlights
- 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.
- Intermediate Level Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
- Approx. 25 hours to complete
- English Subtitles: English
Statistical Inference for Estimation in Data Science at Coursera Course details
- This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings.
- 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.
Statistical Inference for Estimation in Data Science at Coursera Curriculum
Point Estimation
Welcome to Statistical Inference
Discrete Random Variables and Probability Mass Functions
Continuous Random Variables and Probability Density Functions
Joint Distributions
Transformations of Distributions
Expectation and Properties of Expectation
Variance and Covariance
Estimators and Sampling Distributions
Distributions of Sums
Method of Moments Estimators
Course Resources
Important Discrete Distributions
Important Continuous Distributions
Table Summarizing Important Distributions
Video Slides
Video Slides
Video Slides
Video Slides
Video Slides
Video Slides
Recognizing Discrete Distributions
Calculations with Continuous Distributions
Probability, Expectation, and Variance
Method of Moments Estimation
Maximum Likelihood Estimation
A Motivating Example
Notation, Terminology, and First Complete Examples
MLEs for Multiple and Support Parameters
The Invariance Property
Mean Squared Error, Bias, and Relative Efficiency
Video Slides
Video Slides
Video Slides
Video Slides
Video Slides
Finding MLEs
Invariance, Mean-Squared Error, and Efficiency
Large Sample Properties of Maximum Likelihood Estimators
Fisher Information and the Cramer-Rao Lower Bound
Computational Simplifications for the CRLB
The Weak Law of Large Numbers
The Central Limit Theorem
Large Sample Properties of MLEs
The Cramer-Rao Lower Bound
Further Computations with MLEs
Confidence Intervals Involving the Normal Distribution
Let's Build a Confidence Interval!
The Chi-Squared and t- Distributions
t-Distribution Confidence Intervals
Confidence Intervals for the Difference Between Population Means
Small Sample Confidence Intervals for the Difference Between Population Means
Confidence Intervals Involving the Normal Distribution
Confidence Intervals for Differences Between Means
Beyond Normality: Confidence Intervals Unleashed!
A Confidence Interval for Proportions
Confidence Intervals for Variances
A Confidence Interval for a Ratio of Variances
Who Needs Normality?
General Confidence Intervals 2
Confidence Intervals for Proportions and Variances
Build Your Own Confidence Intervals
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceData Science BasicsStatistics for Data ScienceStatistical Inference for Estimation in Data Science