University of Colorado Boulder - Statistical Inference and Hypothesis Testing in Data Science Applications
- Offered byCoursera
Statistical Inference and Hypothesis Testing in Data Science Applications at Coursera Overview
Duration | 29 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Statistical Inference and Hypothesis Testing in Data Science Applications 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. 29 hours to complete
- English Subtitles: English
Statistical Inference and Hypothesis Testing in Data Science Applications at Coursera Course details
- This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse.
- This course can be taken for academic credit as part of CU Boulders 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 Boulders 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 .
Statistical Inference and Hypothesis Testing in Data Science Applications at Coursera Curriculum
Fundamental Concepts of Hypothesis Testing
What is Hypothesis Testing?
Types of Hypotheses
Normal Computations
Errors in Hypothesis Testing
Test Statistics and Significance
A First Test
Introduction to Hypothesis Testing
Composite Tests, Power Functions, and P-Values
Composite Hypotheses and Level of Significance
One-Tailed Tests
Power Functions
Hypothesis Testing with P-Values
Two Tailed Tests
CLT: A Brief Review
Hypothesis Tests for Proportions
Constructing Tests
t-Tests and Two-Sample Tests
The t and Chi-Squared Distributions
The Sample Variance for the Normal Distribution
t-Tests
Two Sample Tests for Means
Two Sample t-Tests for a Difference of Means
Welch's t-Test and Paired Data
Comparing Population Proportions
More Hypothesis Tests!
Beyond Normality
Properties of the Exponential Distribution
Two Tests
Best Tests
UMP Tests
A Test for the Variance of the Normal Distribution
The F-Distribution and a Ratio of Variances
Best Tests and Some General Skills
Uniformly Most Powerful Tests and F-Tests
Likelihood Ratio Tests and Chi-Squared Tests
MLEs
The GRLT
Wilks' Theorem
Chi-Squared Goodness of Fit Test
Independence and Homogeneity
Adventures in GLRTs
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