University of Colorado Boulder
Data Science Foundations: Statistical Inference Specialization
University of Colorado Boulder

Data Science Foundations: Statistical Inference Specialization

Build Your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science

Taught in English

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Anne Dougherty
Jem Corcoran

Instructors: Anne Dougherty

10,223 already enrolled

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.3

(136 reviews)

Intermediate level

Recommended experience

3 months at 10 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

Details to know

Shareable certificate

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.3

(136 reviews)

Intermediate level

Recommended experience

3 months at 10 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

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Specialization - 3 course series

Probability Theory: Foundation for Data Science

Course 142 hours4.5 (150 ratings)

What you'll 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'll gain

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

Statistical Inference for Estimation in Data Science

Course 229 hours4.0 (60 ratings)

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.

What you'll learn

  • Define a composite hypothesis and the level of significance for a test with a composite null hypothesis.

  • Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form of a rejection region.

  • Perform tests concerning a true population variance.

  • Compute the sampling distributions for the sample mean and sample minimum of the exponential distribution.

Instructors

Anne Dougherty
University of Colorado Boulder
2 Courses22,258 learners

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