Statistics & Probability Courses and Certifications

From Data to Decisions: Making Predictions with AI

18 ratings at Coursera

Run bivariate and multiple regression with generative AI as a partner: structure research questions, interpret coefficients and R-squared, and communicate data-driven predictions.

• Coursera
• 1 hour 45 minutes
• Self-Paced
• Paid Course

Discrete-Time Markov Chains and Monte Carlo Methods

20 ratings at Coursera

Compute transition matrices, limiting and stationary distributions, and first-step analyses for Markov chains, then simulate with accept-reject, Metropolis-Hastings, Gibbs sampling, and Markov decision processes.

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

Math for MBA and GMAT Prep

52 ratings at Coursera

Build the math foundation for business school: means, medians and standard deviations, algebra refreshers, marginal analysis, and regression, using Excel formulas and visualization tools.

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

Certified Quality Engineer Training

2657 ratings at Udemy

Comprehensive Certified Quality Engineer (CQE) Exam Preparation Course (Latest 2022 Body of Knowledge) + 894 Questions

• Udemy
• 1 day 13 hours 41 minutes
• Self-Paced
• Paid Course

Regression Analysis

10 ratings at Coursera

Learn regression as supervised learning: fit linear and polynomial models, apply Ridge, Lasso and Elastic Net, run cross-validation, and combine models with bagging, boosting and stacking.

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

Customer Data Analytics for Marketers

13 ratings at Coursera

Learn to analyze marketing data with variance, normal distributions, hypothesis testing, regression, and Structural Equation Modeling, telling correlation from causality while avoiding ethical and interpretation pitfalls.

• Coursera
• 4 weeks, 6 hours a week
• Self-Paced
• Paid Course

Statistics and Data Analysis with R

Learn to run statistics in R and RStudio: import data, compute descriptive statistics and probability distributions, run one- and two-sample hypothesis tests, regression, and ANOVA.

• Coursera
• 6 weeks, 16 hours a week
• Self-Paced
• Paid Course

Resampling, Selection and Splines

Apply generalized least squares, ridge regression, LASSO, and PCA, and use k-fold cross-validation and bootstrapping to evaluate models and quantify uncertainty.

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

Modeling Climate Anomalies with Statistical Analysis

Analyze climate data in Python: pull NOAA, USGS and NWIS datasets via APIs with requests, visualize trends with pandas and Matplotlib, and build linear and logistic regression models.

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

Introduction to Bayesian Statistics for Data Science

Learn Bayesian statistical inference, computing posterior distributions with conjugate families, improper and objective priors, Monte Carlo simulation, and Bayesian linear regression in R.

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

BiteSize Stats: Data and Descriptive Statistics

Learn to classify variables by level of measurement, design probability samples, and summarize data with mean, median, IQR, standard deviation, histograms, and box plots.

• Coursera
• 5 weeks, 15 hours a week
• Self-Paced
• Paid Course

Computational Bayesian Statistics for Data Science

Approximate posteriors when algebra fails: compute MAP estimates by gradient ascent, run rejection sampling, Gibbs and Metropolis-Hastings MCMC, and fit Bayesian models with Stan in R.

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

BiteSize Stats: Key Probability Distributions

Build probability models from random variables and PMFs: compute binomial, Poisson, and Normal probabilities, standardize with Z-scores, and apply the Central Limit Theorem to sample means.

• Coursera
• 5 weeks, 17 hours a week
• Self-Paced
• Paid Course

BiteSize Stats: Hypothesis Testing for Single Samples

Learn to test single-sample claims using p-values, one-sample Z- and t-tests, chi-square goodness-of-fit, confidence intervals, and power-based sample-size planning.

• Coursera
• 5 weeks, 15 hours a week
• Self-Paced
• Paid Course

Career Certificate

BiteSize Statistics for Intermediate Learners

Apply hypothesis testing to single and two samples, use z-, t-, and F-tests, then analyze Pearson/Spearman correlation and regression diagnostics.

• Coursera
• 24 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses