Statistical Inference Courses and Certifications

Eindhoven University of Technology

Improving your statistical inferences

805 ratings at Coursera

Learn to interpret p-values, effect sizes, confidence intervals and Bayes factors, run a-priori power analyses and p-curve analysis, and pre-register studies on the Open Science Framework.

• Coursera
• 8 weeks, 3 hours a week
• Self-Paced
• Paid Course

Career Certificate

Data Science Decisions in Time

Make confident decisions on streaming data: apply sequential hypothesis testing, information theory and games, and causal information to data arriving over time.

• Coursera
• 12 weeks, 15 hours a week
• Self-Paced
• Paid Course
• 4 courses

Inferential Statistics

615 ratings at Coursera

Learn to infer population relations from samples: significance testing, z- and t-tests, Chi-square, simple and multiple regression, analysis of variance, and non-parametric tests.

• Coursera
• 8 weeks, 2 hours a week
• Self-Paced
• Paid Course

Data Analysis and Statistical Inference

41 ratings

Use R to summarize and visualize data, apply confidence intervals, hypothesis tests, ANOVA and chi-square, and model relationships with simple and multiple linear regression.

• Coursera
• Not Currently Offered
• Paid Course

Data Science: Inference and Modeling

51 ratings at edX

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

• edX
• 8 weeks, 1-2 hours a week
• Self-Paced
• Free Online Course (Audit)

Statistics / Data Analysis in SPSS: Inferential Statistics

2088 ratings at Udemy

Increase Your Data Analytic Skills – Highly Valued And Sought After By Employers

• Udemy
• 4 hours 51 minutes
• Self-Paced
• Paid Course

Statistics and Data Analysis with Excel: Intermediate

23 ratings at Coursera

Apply inferential statistics in Excel: build pivot tables and pivot charts, run goodness-of-fit and two-sample tests, and analyse correlation and simple regression.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Engineering Probability and Statistics Part 2

Apply statistical inference to engineering data: build confidence intervals with z- and t-distributions, run one- and two-sample hypothesis tests, and perform one-way ANOVA with Tukey post-hoc tests.

• Coursera
• 7 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Evaluate LLMs: Test and Prove Significance

Move beyond "it seems better" to "we can prove it": quantify LLM evaluation uncertainty with confidence intervals, Chi-Square tests, and error-bar charts in SciPy and Matplotlib.

• Coursera
• 3 hours 13 minutes
• Self-Paced
• Paid Course

Coursera

Analyze Healthcare Data with Confidence

Compute confidence intervals for means in healthcare survey data, distinguish Type I from Type II errors, and run Chi-square tests on categorical clinical variables.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Run Inference & Hypothesis Tests

Apply confidence intervals to compare conversion rates, weigh Type I/II error trade-offs, run two-sample t-tests in Python or R, and analyze power across sample sizes.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Data Science Fundamentals, Part 2: Estimation and Evaluation

Draw reliable conclusions from data: use estimation, hypothesis tests, confidence intervals, and sampling-based methods, apply A/B testing to web optimization, and separate correlation from causation.

• Coursera
• 6 hours 9 minutes
• Self-Paced
• Paid Course

Fundamentals of Statistics

Construct estimators via method of moments and maximum likelihood, quantify uncertainty with confidence intervals and hypothesis tests, fit linear and generalized linear models, and apply PCA.

• MITx Online
• Self-Paced
• Free Online Course

Statistics and R

20 ratings

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

• edX
• 4 weeks, 2-4 hours a week
• Self-Paced
• Free Online Course (Audit)

Inferential Statistics

In this course, learners will gain the tools to test hypotheses, draw conclusions, and measure uncertainty in business settings using t-tests, chi-squared tests, ANOVA, and A/B experiments.

• edX
• 5 weeks, 4-8 hours a week
• Self-Paced
• Free Online Course (Audit)