Coursework

This page contains a list of coursework and serves to summarize major topics we have learned throughout my time here at OSU. If / when time permits, I wish to turn them into posts.

Overview

First year of Master's at OSU, major parts of our coursework focused on building the fundamentals: statistical methods and theories. Second year of Master's, we expanded beyond statistical methods and theories to include additional statistical models (e.g. GLMs) and simulation / optimization methods (e.g. EM). I also gained experience working with big (> 100 GB) data using cloud computing platforms (e.g. GCP, PySpark / Apache Spark).

First year of PhD's also at OSU, we dived into advanced topics in statistics (e.g. decision theory, theories for GLMs/GLMMs). I also took a sneak peek of advanced techniques (e.g. reduced-rank regression, spatial GLMMs, INLA, MCMC), while completing course & independent projects.

Second year of PhD's, I spend most time in Bayesian spatial models such as Gaussian processes (GPs), variant GPs, NNGPs and NNMPs, and Markovian time series models such as MTDs.

(some of the major topics discussed)
[some of the work using R / Python / Query Language]

Scheduled coursework

PhD, Statistics, Oregon State Univeristy, 2021 — Present

Research

• Dissertation (Bayesian Network, State Space Model, Finite Mixture Model, Copula Model and Dependence, Bayesian Hierarchical Model, MCMC Algorithms)
  

Completed coursework

PhD, Statistics, Oregon State Univeristy, 2021 — Present

Theories

• Advanced Theory of Statistics II (Minimax, Admissibility, Shrinkage, Hypothesis Testing: UMP, MLR, UMPU)
  
• Advanced Theory of Statistics III (Large Sample Theory; First-order and Higher-order Asymptotics, Edgeworth and Saddlepoint Approximations, M- and Z-Estimation)
  
• Probability Theory (Measure Theory, Convergence, Central Limit Theorem, Characteristic Functions, Martingales)
  
• Linear Models Theory I (Least Squares Estimation, Best Linear Unbiased Estimation BLUE, Multivariate Normal Distributions, Random Vectors and Matrices)
  Project Title: [Reduced-Rank Regression Model: A Review]
• Linear Models Theory II (Generalized linear models GLM, Generalized linear mixed-effects models GLMM)
  Project Title: [Reparameterized SGLMM (Spatial Generalized Linear Mixed Model): A Review]

Methods

• Bayesian Statistics (Prior, Posterior and Predictive Distributions, Gibbs Sampler, Hierarchical Modeling)
  Project Title: [INLA for GMRFs (e.g. GLMMs, Spatial Models) with Spatial Examples of Leukemia Incident Cases and Heavy Metal Concentrations]
• GLMs II / Survival Analysis (Survival and Hazard Function, Kaplan–Meier Estimator, Nelson-Aalen Estimator, Accelerated Failure Time Model (Exponential and Weibull Regression), Cox Proportional-Hazards Model)
  Project Title: [Random Survival Forests with An Example of Systolic Heart Failure]

CS

• Data Structures (Time Complexity Analysis, Aynamic Array and Bag ADT, Linked List, Stack and Queue ADT, BST/AVL Tree, MinHeap, HashMap)
  Programming Assignments: [Data-Structures-2023]

MS, Statistics, Oregon State Univeristy, 2019 — 2021

Research

• Master’s Project (Counting Process, Poisson Process, Cox and Cluster Process, Hawkes Process, Spatio-Temporal Self-Exciting Process, Thinning Algorithm, 1D & 2D Simulations)

More Theories…

• Advanced Theory of Statistics I (Measure, Decision Theory, Loss Functions, Exponential Families, Unbiasedness, Equivariance, Bayes)
  
• Advanced Theory of Statistics II (Minimax, Admissibility, Shrinkage, Hypothesis Testing: UMP, MLR, UMPU)
  

Data Science

• Data Science Tools and Programming / Big Data (Relational Databases: SQL; Non-Relational Databases: Spark; BigQuery, Data Wrangling, Data Visualization)
  using Cloud Computing Services, R, Python and Query Language [8 HWs] [See my project]

Simulation

• Computational Statistics / Probability, Computing, and Simulation (Monte Carlo methods, Bootstrap estimates, Variance Reduction techniques, Optimization techniques)
  using R and GitHub [8 HWs] [See my project]

Methods

• GLMs I / Generalized Regression Models (Exponential Families, Logistic Regression, Regression-type models for binomial, multinomial and Poisson data, Log-linear models for multidimensional contingency tables, Multinomial / Softmax Regression)
  using R [6 HWs] [See my project]
• Time Series (Autoregressive Integrated Moving Average (ARIMA) model, Forecasting, Spectral Analysis)
  using R [6 HWs] [See our project]
• Spatial Statistics (Geostatistics, Markov Random Field, Poisson Process)
  pending/auditing
• Statistical Methods (Parametric tests: t-test, χ2-test, F-test; Nonparametric tests, Tests for Proportions, Tests for Binary Data)
  using R [6 HWs]
• Linear Models (Simple & Multiple Linear Regression, Weighted Regression, Nonlinear Regression, Linear Models for Binary Data)
  using R [8 HWs]
• Designs of Experiments (Factorial Designs, Random & Mixed Effect models, ANCOVA)
  using SAS [7 HWs]

Theories

• Theory of Statistics I (Probability Distributions, Exponential Families, Functions of Random Variable: Transformations using cdf, pdf & mgf, Order Statistics)
• Theory of Statistics II (Convergence, Sufficiency, Point Estimation: Maximum Likelihood, Bayes, UMVUEs, Rao-Blackwell, Lehmann-Scheffe theorem)
• Theory of Statistics III (MP & UMP tests, CI, Large-Sample tests: LRT, Wald, score test)

Consulting

• Consulting Practicum (Cases: GLMs, Occupancy Modeling, Meta-survey Analysis)
  2022, 2021, 2021, and more [See more details]

Others (Sampling Methods, Statistical / Machine Learning, etc)

• Sampling Methods (Stratified Sampling, Cluster Sampling, Systematic Sampling, Multistage Sampling)
  using R [7 HWs + 1 Project]
• Machine Learning I / Multivariate Analysis (Multidimensional Scaling (MDS), Principal Components Analysis (PCA), Classification & Discriminant Analysis, Clustering)
  using R [6 HWs] [See my project]
• Machine Learning II / Statistical Methods for Large and Complex Data Sets (PCA, Penalized Logistic Regression, Random Forests, Ensemble Methods)
  using R [See our project]
• Probability and Mathematical Statistics I, II
  
  

MAS (deferred), Applied Statistics, Colorado State Univeristy, 2019

• Data Visualization Methods (Exploratory Data Analysis (EDA), Multipanel Conditioning, 3D Plotting)
  using R (base R, lattice, ggplot2) [See my project]
• Quantitative Reasoning (Confounding; Interaction; Biases & Statistical Fallacies: Regression Toward the Mean, Data Snooping, Post-Hoc Hypothesis Testing)
  using R (base R, car, ggplot2) [3 HWs & 1 quiz]
• Design and Data Analysis for Researchers II (Multiple Regression: ANCOVA, Model Selection & Diagnostics, Logistic Regression; Experimental Design: Fixed & Mixed Effects models)
  using R (base R, dplyr, car, ggplot2) [See my project]
• Linear Algebra
  
  

Non-matriculated Status, Mathematics and CS, University of Washington, 2016 — 2018

• Real Analysis (Advanced Calculus) I, Mathematical Reasoning, Linear Algebra, Differential Equations, Multivariate Calculus
  
• Computer Programming I using Java
  
  

Other relevant coursework

BS, Earth and Space Sciences, University of Washington

• Geographic Information Systems (GIS) using ArcGIS, Geoscience Communication, Statistical Methods using R, and Calculus I, II