Advanced Statistics for Data Science Specialization

Familiarize yourself with fundamental concepts in probability and statistics, data analysis and linear models for Data Science.

Instructor: Brian Caffo, PhD

What you'll learn

  •   Learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and more.
  •   Understand the matrix algebra of linear regression models.
  •   Learn about canonical examples of linear models to relate them to techniques that you may already be using.

    • Skills you'll gain

  •   Statistical Methods
  •   Regression Analysis
  •   Mathematical Modeling
  •   Sampling (Statistics)
  •   R Programming
  •   Predictive Modeling
  •   Statistical Analysis
  •   Probability Distribution
  •   Statistical Hypothesis Testing
  •   Linear Algebra
  •   Bayesian Statistics
  •   Statistical Modeling

    • Specialization - 4 course series

    The Advanced Statistics for Data Science Specialization incorporates a series of rigorous graded quizzes to test the understanding of key concepts such as probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.

    This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

    Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

    - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

    - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

    Mathematical Biostatistics Boot Camp 2

    Advanced Linear Models for Data Science 1: Least Squares

    Advanced Linear Models for Data Science 2: Statistical Linear Models

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