Statistics & Probability Courses and Certifications

Statistics and Data Analysis with Excel, Part 1

Learn foundational statistics concepts and apply them using Excel. Covers descriptive statistics, probability, and probability distributions. Ideal for beginners in data science and analytics.

• Coursera
• 16 hours 31 minutes
• Self-Paced
• Paid Course

Modern Regression Analysis in R

Foundational statistical modeling tools for data science, covering linear models, parameter estimation, diagnostics, and ethical implications. Practical skills in R for regression analysis and model selection.

• Coursera
• 1 day 21 hours 27 minutes
• Self-Paced
• Paid Course

Statistical Inference and Hypothesis Testing in Data Science Applications

Learn hypothesis testing for data science applications, covering theory, implementation, and ethical considerations. Develop skills to make informed decisions from data using statistical inference techniques.

• Coursera
• 1 day 12 hours 40 minutes
• Self-Paced
• Paid Course

Linear Regression in R for Public Health

1 rating

Aprenda a criar modelos estatísticos em R para analisar fatores que afetam doenças e resultados de saúde, focando em regressão linear e suas aplicações em saúde pública.

• Coursera
• 15 hours 2 minutes
• Self-Paced
• Paid Course

Integral Calculus and Numerical Analysis for Data Science

Intuitive approach to integral calculus and numerical analysis, covering integration, root-finding, matrix decomposition, and partial derivatives for data science applications.

• Coursera
• 3 hours 32 minutes
• Self-Paced
• Paid Course

ANOVA and Experimental Design

Explore ANOVA, ANCOVA, and experimental design for data science applications. Learn key concepts like randomization, blocking, and factorial design to conduct meaningful experiments and analyze results.

• Coursera
• 1 day 15 hours 57 minutes
• Self-Paced
• Paid Course

Career Certificate

Statistical Modeling for Data Science Applications

Develop advanced statistical modeling skills for data science, including linear regression, ANOVA, and generalized linear models, with practical applications using R programming.

• Coursera
• 12 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Generalized Linear Models and Nonparametric Regression

Explore advanced statistical modeling tools including GLMs, nonparametric regression, and GAMs. Learn to apply these techniques for classification, data analysis, and ethical decision-making in data science.

• Coursera
• 1 day 18 hours 4 minutes
• Self-Paced
• Paid Course

Career Certificate

Data Science Methods for Quality Improvement

Gain data analysis skills for quality improvement in business and industry. Learn RStudio, RMarkdown, and techniques to enhance performance, quality, and reliability across various sectors.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Discrete-Time Markov Chains and Monte Carlo Methods

Explore discrete-time Markov chains, Monte Carlo methods, and their applications in AI, reinforcement learning, and stochastic simulation with mathematical rigor.

• Coursera
• 1 day 8 hours 32 minutes
• Self-Paced
• Paid Course

Statistics and Data Analysis with Excel, Part 2

Explore advanced statistical techniques using Excel, including hypothesis testing, regression analysis, and ANOVA. Build on foundational concepts to enhance data analysis skills.

• Coursera
• 21 hours 11 minutes
• Self-Paced
• Paid Course

Statistics and Data Analysis with R

Master statistical analysis using RStudio through hands-on practice with probability, hypothesis testing, regression, and ANOVA. Learn to manipulate data, create visualizations, and perform complex calculations using R programming.

• Coursera
• 3 days 23 hours 28 minutes
• Self-Paced
• Paid Course

Modeling Climate Anomalies with Statistical Analysis

Learn to model climate data using statistical analysis in Python, covering data visualization, predictive modeling, and regression techniques with emphasis on practical application to climate datasets.

• Coursera
• 7 hours 21 minutes
• Self-Paced
• Paid Course

Regression Analysis

Master regression analysis techniques to model relationships, prevent overfitting, and make data-driven decisions. Explore linear and polynomial regression, regularization, cross-validation, and ensemble methods through hands-on practice.

• Coursera
• 1 day 16 hours 1 minute
• Self-Paced
• Paid Course

Introduction to Bayesian Statistics for Data Science

Explore the theoretical foundations of Bayesian statistics and apply them to data science problems, covering probability theory, Bayes' theorem, prior distributions, practical applications, and ethical considerations.

• Coursera
• 1 day 13 hours 29 minutes
• Self-Paced
• Paid Course