
Comprehensive financial risk management training covering key concepts, strategies, and practical applications through case studies. Learn to develop frameworks, manage people, and assess processes effectively.

First course for incoming Harvard Government Department PhD students; also taken by students in other departments, and students elsewhere through the Harvard Extension School. Instructor: Gary King.

Explore random variables, probability distributions, and stochastic processes through intuitive explanations and practical applications in parameter estimation and noisy measurements.

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.

Comprehensive preparation for CBSE/ISC 12th grade mathematics, covering key topics with practice questions, notes, and exam strategies for high scores.

Comprehensive biostatistics tutorial covering key concepts from basics to advanced topics, including data analysis, probability, medical decision-making, epidemiology, and research design.

Comprehensive video series covering fundamental statistical concepts, from data analysis to hypothesis testing, with practical examples and in-depth explanations.

Comprehensive O Levels Mathematics course covering key topics like algebra, geometry, trigonometry, and statistics. Includes exam techniques and practice questions for thorough preparation.

Explore moment generating functions in-depth with Professor Knudson's comprehensive 8-part video series, covering theory and applications in probability and statistics.

Concise explanations of key statistical concepts, from probabilities to financial metrics, offering quick insights for learners and professionals in data analysis and finance.

Learn biostatistics with R: from basics to advanced topics like probability, inferential statistics, mixed modeling, and regression analysis. Gain practical skills for data analysis in biological sciences.

Comprehensive exploration of statistical concepts, from basics to advanced topics, covering descriptive and inferential statistics, probability, distributions, hypothesis testing, and various statistical tests.

Learn fundamental probability concepts including unions, intersections, complements, conditional probability, independence, and De Morgan's Laws through clear explanations and examples.

Comprehensive introduction to probability theory, covering fundamental concepts, distributions, and applications. Ideal for understanding statistics, science, and randomness in various fields.

Exploring algorithmic fairness and privacy in computer science, addressing concerns about incorporating societal values into technology as it increasingly impacts our lives.

Explore project risk analysis using interactive simulations and stochastic libraries. Learn cutting-edge methods applicable across industries, balancing theory with practical tools for effective risk assessment and management.

Explore the future of probability in computer science with Stanford's expert insights, covering advanced concepts and their applications in emerging technologies.

Comprehensive exploration of big data analytics, covering programming, databases, statistics, machine learning, visualization, and cloud computing for practical applications.

Comprehensive exploration of probability theory, statistical distributions, sampling, estimation, and hypothesis testing, providing a solid foundation for data analysis and statistical inference.

Learn Python-based data analytics, covering statistics, probability, hypothesis testing, regression, ANOVA, clustering, and decision trees. Gain practical skills for data-driven decision making and statistical analysis.

Explore probability and random processes in wireless communications, covering axioms, conditional probability, random variables, and applications in fading channels and array processing.