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.
Data Science Foundations: Statistical Inference Specialization
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Data Science Foundations: Statistical Inference Specialization
Build Your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science
Instructors: Anne Dougherty
15,621 already enrolled
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Get in-depth knowledge of a subject
4.3
(191 reviews)
Intermediate level
Recommended experience
3 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
4.3
(191 reviews)
Intermediate level
Recommended experience
3 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Skills you'll gain
- Probability
- Descriptive Statistics
- Statistics
- Applied Mathematics
- Probability Distribution
- Statistical Methods
- Statistical Inference
- Bayesian Statistics
- Sampling (Statistics)
- Data Ethics
- Data Analysis
- Artificial Intelligence
- Statistical Analysis
- Quantitative Research
- Data Science
- A/B Testing
- Probability & Statistics
- Statistical Hypothesis Testing
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
21 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from University of Colorado Boulder
Specialization - 3 course series
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: Probability
Category: Probability Distribution
Category: Probability & Statistics
Category: Bayesian Statistics
Category: Statistical Analysis
Category: Descriptive Statistics
Category: Statistical Inference
Category: Artificial Intelligence
Category: Data Analysis
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.
Skills you'll gain
Category: Statistical Inference
Category: Probability Distribution
Category: Probability & Statistics
Category: Statistical Methods
Category: Statistical Analysis
Category: Applied Mathematics
Category: Sampling (Statistics)
Category: Artificial Intelligence
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.
Skills you'll gain
Category: Statistical Hypothesis Testing
Category: Statistical Inference
Category: Probability & Statistics
Category: A/B Testing
Category: Quantitative Research
Category: Sampling (Statistics)
Category: Data Ethics
Category: Statistical Methods
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Build toward a degree
This Specialization is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
Instructors
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Frequently asked questions
Statistical Inference for Data Science Applications takes approximately 16 weeks of instruction.
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Courses do not have to be taken a specific order, though it's recommended that learners follow the sequence of courses if they have no previous experience with data structures or algorithm analysis and design.
More questions
Financial aid available,