Welcome to Bussma
All you need is an idea. We handle the rest.
All you need is an idea. We handle the rest.
This course provides a comprehensive introduction to probability theory, focusing on its applications in data science, machine learning, and real-world decision-making. Students will learn to model uncertainty, quantify risk, and analyze random events using fundamental probabilistic concepts. Key topics include probability rules, conditional probability, Bayes’ Theorem, discrete and continuous random variables, probability distributions (Binomial, Poisson, Normal, Exponential), expectation, variance, and the Central Limit Theorem (CLT). Emphasis is placed on practical problem-solving, simulations, and real-data applications using Python. By the end of the course, learners will be able to: Understand and apply core probability principles Analyze and visualize random variables and their distributions Simulate probability experiments using Python (NumPy, SciPy) Apply probability to A/B testing, data analysis, and model uncertainty This course is ideal for students in computing, data science, engineering, and anyone seeking a solid foundation in probability for analytical roles.
Tamer Elateeq
Data Management consultant
This course provides a comprehensive introduction to probability theory, focusing on its applications in data science, machine learning, and real-world decision-making. Students will learn to model uncertainty, quantify risk, and analyze random events using fundamental probabilistic concepts. Key topics include probability rules, conditional probability, Bayes’ Theorem, discrete and continuous random variables, probability distributions (Binomial, Poisson, Normal, Exponential), expectation, variance, and the Central Limit Theorem (CLT). Emphasis is placed on practical problem-solving, simulations, and real-data applications using Python. By the end of the course, learners will be able to: Understand and apply core probability principles Analyze and visualize random variables and their distributions Simulate probability experiments using Python (NumPy, SciPy) Apply probability to A/B testing, data analysis, and model uncertainty This course is ideal for students in computing, data science, engineering, and anyone seeking a solid foundation in probability for analytical roles.
Lessons
3
Duration
6 hours Hours
Skill Level
intermediate
Views
10