Ementorhub

What you'll learn

Master combinatorial techniques, including permutations, combinations, and multinomial coefficients, to solve counting and probability problems.

Apply probability axioms, construct Venn diagrams, and calculate sample space sizes to evaluate probabilities in various scenarios.

Utilize Bayes' formula, the multiplication rule, and conditional probability to assess event relationships and solve real-world problems.

Analyze discrete and continuous random variables using probability density functions, cumulative distribution functions, and expected values.

Skills you'll gain

Data Science

Statistical Methods

Bayesian Statistics

Combinatorics

Computational Thinking

Statistical Analysis

Probability Distribution

Probability

Simulations

Artificial Intelligence and Machine Learning (AI/ML)

Data Analysis

R Programming

Probability & Statistics

Applied Mathematics

There are 6 modules in this course

What makes this course unique is its practical approach: students will develop hands-on proficiency in the R programming language, which is widely used in data science and statistical modeling. The course also includes real-world applications, allowing learners to bridge theoretical knowledge with practical problem-solving skills. Whether you are aiming to pursue advanced studies in machine learning or develop data-driven solutions in professional settings, this course provides the solid foundation you need to excel. Designed for learners with a background in calculus and basic programming, this course prepares you to tackle more advanced topics in computational science.

Combinatorial Analysis

Probability

Conditional Probability and Independence

Discrete Random Variables

Continuous Random Variables