About this Course

This course covers foundational knowledge in probability necessary for statistical theory and mathematical modeling, tailored for emerging fields in information science. It introduces basic probability concepts and rules, important distribution models with discrete random variables, and practical application of these models in data science.

What Students Will Learn

  • Understanding basic probability concepts and rules.

    • Pre-requisites for the Course

    Prospective students should have a background in calculus, including double integrals, and a basic understanding of combinatorics. A tutorial during the first lesson of the course will provide a refresher on combinatorics.

    Course Coverage

    • Introduction to Probability Concepts and Rules.
    • Exploration of Probability Mass Functions and CDFs.
    • Detailed Study on Joint Distributions and Expected Values.
    • Comprehensive Overview of Discrete Random Variables.
    • In-depth Discussion on Probability Distribution Models including Bernoulli, Binomial, and Poisson distributions.

    Target Audience

    This course is suitable for individuals aiming to build a career in data science, actuarial science, or those interested in enhancing their skills in statistical theory and mathematical modeling.

    Application of Skills in Real-World

    The skills acquired from this course can be applied in various analytical and data-intensive roles. Understanding probability and statistical models enables professionals to make informed decisions based on data analysis, predict future trends, and solve complex problems in different sectors such as finance, healthcare, logistics, and more.

    Course Syllabus

    1. Sample Space and Probability: Basics of probability including outcomes and events.
    2. Independent Events and Conditional Probability: Deep dive into conditional probability and Bayes’ Theorem.
    3. Random Variables: Study on various distributions and their functions.
    4. Expected Values: Discussing expected values of discrete random variables with practical examples.
    5. Models of Disymmetric Variables II: Further exploration of discrete models including Poisson and Hypergeometric distributions.

    Testimonials

    "I thought the course was very good. Especially useful were the large number of practice problems. Bravo!" – Previous Student.