Probability Theory: Foundation for Data Science

Course Description

Welcome to "Probability Theory: Foundation for Data Science," an intermediate-level course designed to provide a comprehensive review of probability theory essential for success in engineering and data science fields. This course, offered by DelftX, is part of the Professional Certificate in Mastering Probability and Statistics. It's a self-contained, modular program that allows you to focus on specific areas of interest while refreshing your bachelor-level mathematics knowledge.

What You Will Learn

• Master the concepts of discrete and continuous random variables
• Learn to calculate and interpret properties of random variables, such as expectation and variance
• Understand the application of specific random variables in various contexts
• Explore the interaction between multiple random variables
• Grasp important limiting results, including the powerful Central Limit Theorem
• Develop skills in simulating real-life situations using probability theory

Prerequisites

• Prior knowledge of basic probability theory concepts
• Familiarity with calculus, including (partial) differentiation and (multiple) integration
• This is a review course, so you should have previously studied or be familiar with most of the material

Course Content

• Probability spaces and general concepts
• Discrete random variables and their distributions
• Continuous random variables and their distributions
• Multivariate random variables
• Limiting theorems (Law of Large Numbers and Central Limit Theorem)
• Monte Carlo simulation techniques

Who This Course Is For

• Students preparing for a master's degree in engineering or data science
• Professionals looking to solidify their knowledge in a work context
• Anyone wanting to brush up on fundamental probability theory concepts
• Individuals interested in applications such as modeling, finance, signal processing, and logistics

Real-World Applications

• Data analysis and interpretation
• Financial modeling and risk assessment
• Signal processing in telecommunications and audio engineering
• Logistics and supply chain optimization
• Machine learning and artificial intelligence
• Scientific research and experimental design
• Quality control in manufacturing
• Actuarial science and insurance

Syllabus

This course offers a unique opportunity to strengthen your foundation in probability theory, a critical skill for success in various engineering and data science fields. With its flexible, self-paced format and access to numerous exercises through the Grasple platform, you'll be able to practice and reinforce your learning with intelligent, personalized feedback. Don't miss this chance to elevate your mathematical prowess and unlock new possibilities in your academic or professional journey!