Ementorhub

What you'll learn

Master techniques for simulating random variables, including the Inverse Transformation and Rejection Methods using R programming.

Analyze complex networks using Exponential Random Graph Models to model and interpret social structures and their dependencies.

Understand and apply probabilistic graphical models, including Bayesian networks, to reason about uncertainty and infer relationships in data.

Skills you'll gain

R Programming

Probability

Machine Learning

Network Analysis

Graph Theory

Bayesian Network

Applied Mathematics

Markov Model

Statistical Modeling

Statistical Analysis

Data Analysis

Probability Distribution

Simulations

There are 4 modules in this course

What sets this course apart is its emphasis on practical applications using the R programming language, empowering students to simulate random variables effectively and construct sophisticated models for real-world scenarios. Through hands-on projects and exercises, learners will not only deepen their theoretical understanding but also gain valuable experience in solving applied problems across various domains. Upon completion, you will be well-prepared to tackle challenges in data analysis, machine learning, and statistical modeling, making you a valuable asset in any data-driven field. Whether you're looking to enhance your expertise or start a new career, this course offers a unique blend of theory and practical skills that will enable you to excel in today’s data-centric world.

Simulation

Exponential Random Graph Models

Probabilistic Graphical Models