Techniques and Models

• In this course we learn to:

  • Efficiently and effectively communicate the results of data analysis.
  • Use statistical modeling results to draw scientific conclusions.
  • Extend basic statistical models to account for correlated observations using hierarchical models.

• We will build the following skills:

  • R Programming
  • Regression Analysis
  • Probability
  • Bayesian Statistics
  • Statistical Inference
  • Statistical Modeling
  • Statistical Methods
  • Data Analysis
  • Markov Model
  • Statistical Analysis
  • Probability Distribution
  • Simulations

• There are five modules in this course:

  1. Statistical Modeling: We will learn about the objectives of statistical models, how to build them, and how to use them to draw conclusions.
  2. Markov Chain Monte Carlo: We will learn about the Markov Chain Monte Carlo (MCMC) method, which is a powerful tool for sampling from complex probability distributions.
  3. Common Statistical Models: We will learn about common statistical models, such as linear regression, logistic regression, ANOVA, and more.
  4. Count data and hierarchical models: We will learn about count data and hierarchical models, which are used to model data that is grouped into different levels.
  5. Capstone Project: We will apply the skills we have learned in a capstone project, where we will build a statistical model to analyze a real-world dataset.
  • The course instructor is Matthew Heiner UCSC who was a Doctoral Student at the University of California, Santa Cruz at the time of the course creation. He wrote his phd thesis Bayesian Mixture Modeling and Order Selection for Markovian Time Series