California State University, East Bay | 2017 - 2019 | M.S. Statistics (Data Science Concentration)
Data Science Courses
STAT 653 - Statistical Natural Language Processing: Statistical topics used for processing natural language. Text wrangling and cleaning. Creating a word list corpus. Text classification. Web scraping. Social media mining.
STAT 650 - Advanced R for Data Science: Professional R programming techniques. Data wrangling. R packages. Connections to databases. Use of R for reproducible research. Data Visualization. Use of R in cloud computing. Topics include applied statistics, data science, and statistical/machine learning.
STAT 641 - Bootstrapping Methods: Implementation of computationally-advanced statistical methods. Use of modern computing software (e.g. R, Python, SAS). Topics include: bootstrap, Monte Carlo, and applied statistics.
STAT 6620 - Statistical Learning with R: Introduction to machine learning, including supervised learning such as regression, logistic regression and classification methods. Re-sampling methods such as cross-validation and bootstrap. Unsupervised learning. Applications to data mining, statistical pattern recognition, and data processing.
Core Statistical Courses
STAT 673 - Nonparametric Statistical Methods: Nonparametric methods and distribution-free inferential methods. Topics include: Permutation methods, bootstrapping, and re-sampling methods. Hypothesis testing and estimation procedures. Exact, Monte-Carlo, and asymptotic p-values. Measures of association.
STAT 640 - Advanced Statistical Theory: Theory of point and interval estimation and hypothesis testing from the Neyman-Pearson point of view. Topics include: decision theory, non-parametric inference, multivariate analysis, Bayesian methods, computer intensive methods, and statistical bootstrapping and simulation.
STAT 6509 - Theory and Application of Regression: Theory of least squares in model fitting. Computational methods in regression, including variable selection, ANOVA and ANCOVA. Model assessment, graphical techniques and assumption checking.
STAT 6305 - Analysis of Variance Models: Models for factorial designs: expected mean squares, random effects, nesting, power/sample size, missing data, ANOVA. Model assessment.
STAT 6304 - Advanced Statistical Inference: Random variables, sampling distributions, conditional probability. Expectation. Estimation, method of moments, maximum likelihood. Confidence intervals. Hypothesis testing. Computer-aided computations and simulations. Topics include: t-tests, correlation, regression, proportions, chi-squared, ANOVA, nonparametrics, bootstrapping.
STAT 6205 - Statistical Theory: Maximum likelihood and least squares estimation, applications to one-sample, two-sample and regression problems, hypothesis testing, confidence intervals, significance level, bias, precision.
STAT 6204 - Probability Theory: Theory of probability. Random variables; joint, marginal, conditional distributions; important distributions (binomial, Poisson, normal, etc.); moments; moment generating functions. Multivariate distributions. Inequalities; limit theorems. Multidimensional transformations; derivation of random variables.
University of California, Santa Cruz | 2012 - 2016 | B.S. Mathematics & Economics
Mathematic Courses
MATH 105A - Real Analysis: The basic concepts of one-variable calculus are treated rigorously. Set theory, the real number system, numerical sequences and series, continuity, differentiation.
MATH 100 - Intro Proof & Problem Solving: Introduction to sets, relations, elementary mathematical logic, proof by contradiction, mathematical induction, and counting arguments.
MATH 23B - Vector Calculus: Double integral, changing the order of integration. Triple integrals, maps of the plane, change of variables theorem, improper double integrals. Path integrals, line integrals, parametrized surfaces, area of a surface, surface integrals. Green’s theorem, Stokes’ theorem, conservative fields, Gauss’ theorem. Applications to physics and differential equations, differential forms.
MATH 23A - Vector Calculus: Vectors in n-dimensional Euclidean space. The inner and cross products. The derivative of functions from n-dimensional to m-dimensional Euclidean space is studied as a linear transformation having matrix representation. Paths in 3-dimensions, arc length, vector differential calculus, Taylor’s theorem in several variables, extrema of real-valued functions, constrained extrema and Lagrange multipliers, the implicit function theorem.
MATH 21 - Linear Algebra: Systems of linear equations, matrices, determinants. Introduction to abstract vector spaces, linear transformation, inner products, geometry of Euclidean space, and eigenvalues.
MATH 11B - Calculus with Applications: Starting with the fundamental theorem of calculus and related techniques, the integral of functions of a single variable is developed and applied to problems in geometry, probability, physics, and differential equations. Polynomial approximations, Taylor series, and their applications
MATH 11A - Calculus with Applications: A modern course stressing conceptual understanding, relevance, and problem solving. The derivative of polynomial, exponential, and trigonometric functions of a single variable is developed and applied to a wide range of problems involving graphing, approximation, and optimization.
Statistic Courses
AMS 147 - Computational Methods and Applications: Applications of computational methods to solving mathematical problems using Matlab. Topics include solution of nonlinear equations, linear systems, differential equations, sparse matrix solver, and eigenvalue problems.
AMS 131 - Intro to Probability Theory: Introduction to probability theory and its applications. Combinatorial analysis, axioms of probability and independence, random variables (discrete and continuous), joint probability distributions, properties of expectation, Central Limit Theorem, Law of Large Numbers, Markov chains.
AMS 7 - Statistical Methods (Biological, Environmental, and Health Sciences): Case-study-based introduction to statistical methods as practiced in the biological, environmental, and health sciences. Descriptive methods, experimental design, probability, interval estimation, hypothesis testing, one- and two-sample problems, power and sample size calculations, simple correlation and simple linear regression, one-way analysis of variance, categorical data analysis.
Economic Courses
ECON 169 - Economic Analysis of the Law: The application of the theories and methods of neoclassical economics to the central institutions of the legal system, including the common law doctrines of negligence, contract, and property; bankruptcy and corporate law; and civil, criminal, and administrative procedure.
ECON 141 - International Finance: Topics include national accounting, balance of payments theories, parity conditions in international finance, exchange rate determination models, forward-looking financial instruments, international monetary systems, country interdependence and exchange rate regimes, international monetary integration, and Eurocurrency market.
ECON 115 - Intro to Management Sciences: The scientific study of management decision making. Topics include linear, integer, and non-linear programming. Special emphasis on a wide variety of practical applications, including production scheduling, optimal transportation assignments, and optimal inventory policy.
ECON 113 - Intro to Econometrics: Practical methods for organizing and analyzing economic data, testing economic hypotheses, and measuring economic relationships. Regression analysis is the main empirical method, and basic statistical and probability theory is included.
ECON 100B - Intermediate Macroeconomics: Covers major theoretical issues arising in the study of income, employment, interest rates, and the price level. Examines the role of monetary and fiscal policy in economic stabilization. Also considers these issues as they relate to the global economy.
ECON 100A - Intermediate Microeconomics: Covers major theoretical issues arising in the study of resource allocation, the function of markets, consumer behavior, and the determination of price, output, and profits in competitive, monopolistic, and oligopolistic market structures. Also considers issues of welfare and public policy.
