Statistics & Probability Courses and Certifications

High-Dimensional Data Analysis

15 ratings at edX

A focus on several techniques that are widely used in the analysis of high-dimensional data.

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

Probability Foundations for Data Science and AI

296 ratings at Coursera

Learn to calculate probabilities, apply Bayes' formula and independence, work with discrete and continuous random variables, joint distributions and covariance, and the Central Limit Theorem.

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

Computational Probability and Inference

3 ratings

Learn fundamentals of probabilistic analysis and inference. Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spa

• edX
• 12 weeks, 4-6 hours a week
• Self-Paced
• Free Certificate

Survival Analysis in R for Public Health

331 ratings at Coursera

Run survival analysis in R: import patient data, build Kaplan-Meier plots and log-rank tests, fit multiple Cox proportional hazards models, and test proportionality with residuals.

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

Bayesian Machine Learning in Python: A/B Testing

8050 ratings at Udemy

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

• Udemy
• 10 hours 27 minutes
• Self-Paced
• Paid Course

Coursera

Linear Regression with Python

442 ratings at Coursera

Implement linear regression from scratch with Python and NumPy, coding gradient descent to see how the optimization algorithm and training process actually work.

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

Math behind Moneyball

98 ratings at Coursera

Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.

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

Deep Learning Prerequisites: Linear Regression in Python

6815 ratings at Udemy

Data science, machine learning, and artificial intelligence in Python for students and professionals

• Udemy
• 6 hours 21 minutes
• Self-Paced
• Paid Course

Data Science : Complete Data Science & Machine Learning

3566 ratings at Udemy

Learn and master the Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science

• Udemy
• 1 day 1 hour 49 minutes
• Self-Paced
• Paid Course

Factorial and Fractional Factorial Designs

86 ratings at Coursera

Design multifactor experiments: build 2^k factorial and two-level fractional factorial designs, apply blocking and confounding, and analyze the resulting data with ANOVA.

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

R Programming for Statistics and Data Science

5851 ratings at Udemy

R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R

• Udemy
• 6 hours 42 minutes
• Self-Paced
• Paid Course

Response Surfaces, Mixtures, and Model Building

67 ratings at Coursera

Optimize processes after factor screening: build response surface designs, fit regression models, run mixture and computer experiments, and apply robust parameter design.

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

Data Analytics, Data Science, ML, DL & NLP - All in 1 Course

5132 ratings at Udemy

Complete Career Track to Become An Expert in Data Analysis, Data Science, ML, DL, NLP, AI, Python, Excel, SQL, PowerBI.

• Udemy
• 66 hours
• Self-Paced
• Paid Course

Random Models, Nested and Split-plot Designs

37 ratings at Coursera

Design and analyze experiments with random factors: estimate variance components for measurement system capability, and handle nested factors, split-plot designs, nonnormal responses, and covariates.

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

NPTEL;  IIT Bombay

Introduction to Biostatistics

7 ratings

In this course, we will discuss different statistical tools required to (i) analyze our observations, (ii) design new experiments, and (iii) integrate a large number of observations in a single unified model.

• YouTube
• 19 hours
• Self-Paced
• Free Video