Course Description
PSTAT 10. Principles of Data Science with R
Prerequisite: Math 2B or 3B with a minimum grade of C or better.
Fundamentals of programming for data science using R. Descriptive statistics, distributions and graphics in R. Relational database management systems including the relational model, relational algebra, database design principles and data manipulation using SQL. An introduction to the concept of big data.
PSTAT 109. Statistics for Economics
Prerequisite: Math 34A-B or Math 2A-B or 3A-B; Math 34B or 2B or 3B can be taken simultaneously. Course cannot be used to satisfy any Actuarial Science, Financial Math & Statistics or Statistical Science major or minor requirements.
An introduction to probabilistic modeling and statistical inference for students with basic knowledge of calculus: probability, discrete and continuous random variables, probability distributions, mean, variance, correlation, sampling, parameter estimation, unbiasedness and efficiency, confidence intervals, hypothesis testing. Computing labs with Excel.
PSTAT 120A. Probability and Statistics
Prerequisite: Math 3C or Math 4A or Math 4AI or Math 6A or Math 6AI completed with a minimum grade of C or better.
Recommended Preparation: Math 6A
Concepts of probability; random variables; combinatorial probability; discrete and continuous distributions; joint distributions, expected values; moment generating functions; law of large numbers and central limit theorems.
PSTAT 120B. Probability and Statistics
Prerequisite: PSTAT 120A with a grade of C or better.
Distribution of sample mean and sample variance; t, chi-squared and F distributions; summarizing data by statistics and graphs; estimation theory for single samples: sufficiency, efficiency, consistency, method of moments, maximum likelihood; hypothesis testing: likelihood ratio test; confidence intervals.
PSTAT 120C. Probability and Statistics
Prerequisite: PSTAT 120B with a grade C or better.
Hypothesis tests for means of independent samples and paired data; likelihood ratio tests; nonparametric hypothesis tests: sign, rank, and Mann-Whitney tests; chi-squared goodness-of-fit tests and contingency tables; Bayesian methods of estimating parameters and credible intervals.
PSTAT 122. Design and Analysis of Experiments
Prerequisite: PSTAT 10 and PSTAT 120B both with a minimum grade of C or better.
An introduction to statistical design and analysis of experiments. Covers: principles of randomization, blocking and replication; fixed, random and mixed effects models; block designs, factorial designs and nested designs; analysis of variance and multiple comparison.
PSTAT 126. Regression Analysis
Prerequisite: PSTAT 10 and PSTAT 120B both with a minimum grade of C or better.
Linear and multiple regression, analysis of residuals, transformations, variable and model selection including stepwise regression, and analysis of covariance. The course will stress the use of computer packages to solve real-world problems.
PSTAT 130. SAS Base Programming
Prerequisite: One upper division course in PSTAT, MATH, Computer Science or ECE.
Recommended Preparation: Computer Science 16 or equivalent programming class.
In depth SAS programming course. Topics include importing/exporting raw data files, manipulating/transforming data, combining SAS data sets, generating reports, handling syntax and logic errors. Provides preparation for the SAS Institute Certified Professional (Base Programming) Examination.
PSTAT 160A. Applied Stochastic Processes
Prerequisite: Mathematics 4A or 4AI or 5A, Mathematics 8, and PSTAT 120A. A minimum letter grade of C or better must be earned in each course.
Discrete probability models. Review of discrete and continuous probability. Conditional expectations. Simulation techniques for random variables. Discrete time stochastic processes: random walks and Markov chains with applications to Monte Carlo simulation and mathematical finance. Introduction to Poisson process.
PSTAT 171. Mathematics of Fixed Income Markets
Prerequisite: Mathematics 2B or 3B with a minimum grade of C
Introduction to fixed Income Markets. Topics include: measurement of interest, annuities certain, varying annuities, amortization schedules, sinking funds, bonds and related securities, depreciation.
PSTAT 174. Time Series
Prerequisite: PSTAT 10 and PSTAT 120B both with a minimum grade of C or better.
Stationary and non-stationary models, seasonal time series, ARMA models: calculation of ACF, PACF, mean and ACF estimation. Barlett’s formula, model estimation: Yule-Walker estimates, ML method. identification techniques, diagnostic checking forecasting, spectral analysis, the periodogram. Current software and applications.
MATH 104A. Introduction Into Numerical Analysis
Prerequisite: Mathematics 4B or 4BI, 6A or 6AI, and 6B; or 5A or 5AI, 5B or 5BI and 5C; and, Math 8, and Math 117 and, Computer Science 5AA-ZZ or 10 or 8 or 16 or Engineering 3, each with a grade of C or above.
Numerical methods for the solution of nonlinear equations (Newton method), for integration (quadrature formulas and composite integration), and for the initial value problem for ordinary differential equations (Euler and Kutta methods).
MATH 104B. Numerical Analysis
Prerequisite: Math 104A with a minimum grade of C.
Numerical methods for the solution of systems of linear equations (direct and iteractive methods), and the finite difference methods for boundary value problems for (ordinary and partial) differential equations.
MATH 104C. Advanced Topics in Numerical Analysis
Prerequisite: Math 104B with a minimum grade of C.
Topics in approximation theory; numerical methods for finding eigenvalues of a matrix; and advanced topics in numerical methods for ordinary and partial differential equations.
MATH 116. Combinatorial Analysis
Prerequisite: Mathematics 8 with a grade of “C” or better.
Elementary counting principles, binomial coefficients, generating functions, recurrence relations, the principle of inclusion and exclusion, distributions and partitions, systems of distinct representatives, applications to computation.
MATH 117. Methods of Analysis
Prerequisite: Mathematics 8 with a grade of “C” or better.
Introduction to methods of proof in analysis. topics include limits, sequences and series, continuity, compactness, as well as other topics. This course is intended to follow Mathematics 8 and to introduce students to the level of sophistication of upper-division mathematics.
ECON 10A. Intermediate Microeconomic Theory
Prerequisite: Economics 1 and 2; Mathematics 34A-B, 2A-B, or 3A-B.
Enrollment Comments: Designed for majors. Quarters usually offered: Winter, Spring, Summer, Fall. Course must be taken at UCSB.
Economic theory relating to demand, production, and competitive product markets with emphasis on applications of theory.
ECON 101. Intermediate Macroeconomic Theory
Prerequisite: Economics 10A and PStat 109 or 120A.
Recommended Preparation: Economics 100B.
Enrollment Comments: Designed for majors. Reduced credit of 2 units will be given to students who have taken both Economics 101 and 105.
Contemporary analysis of income, employment, price level, and public policy using static general equilibrium framework with emphasis on applications of theory. Long term economic growth is also covered.
CMPSC 8. Introduction to Computer Science
Enrollment Comments: Not open for credit to students who have completed Computer Science 16 or Engineering 3 or ECE 3.
Repeat Comments: Legal repeat for CMPSC 5AA-ZZ.
Introduction to computer program development for students with little to no programming experience. Basic programming concepts, variables and expressions, data and control structures, algorithms, debugging, program design, and documentation.
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