Degree Programs
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Bachelor of Science in Statistics (BS Statistics) Program
With the mission of the School in mind, UPSS ensures that the BS Statistics program continues to be adequate and sufficient to achieve this mission. The BS Statistics program is a four-year course that provides students with a sound understanding of statistical methods - their underlying theories and their applications.
GOALS OF THE PROGRAM
The goal of the program is to provide strong and holistic statistics and liberal education so the graduates are:
1. Technically competent, with core and applied skills needed by industry and the job market as a whole;
2. Adaptive, ready, and equipped to provide for the needs given limited resources of the company or organization;
3. Skillful in the use of data analysis tools in problem-solving;
4. Ready to take up further studies;
5. Flexible in their ability to specialize in other fields, with necessary skills that open other pursuits requiring quantitative analysis; and,
6. Skillful in collaboration, teamwork, and organizing and managing statistical projects.
PROGRAM OUTCOMES
The program outcomes of BS Statistics are for the students to:
1. Understand the concepts in the core domains of statistics (e.g. probability theory, inferential thought, modeling, sampling, and survey operation);
2. Identify appropriate techniques from different statistical paradigms to answer research objectives;
3. Evaluate statistical inquiries addressing national and global issues in various disciplines either as an individual professional or in collaborative work; and,
4. Communicate effectively the process and outcomes of a statistical inquiry.
CORE COURSES
Stat 114: Descriptive Statistics
Statistics; statistical measurement; statistical notations; collection, organization, and presentation of data; measures of central tendency, location, dispersion, skewness, kurtosis; letter values, boxplots, and stem-and-leaf display; measures of association and relationship; rates, ratios, and proportions; construction of index numbers and indicators.
Coreq: Math 20/equiv.
Stat 115: Basic Statistical Methods
Computer-assisted statistical analysis on the following: tests for means; tests for proportions; tests for independence; simple linear regression; Analysis of variance; forecasting using classical techniques.
Prereq: Stat 114/101/equiv.
Stat 117: Mathematics for Statistics
Principles of logic; methods of proof; fields, sigma fields, and sequences of sets; the real number system; sequences & series; combinatorial analysis.
Prereq: Math 20/equiv.
Stat 121: Probability Theory I
Elements of probability; random variables; discrete and continuous random variables; probability distributions; special distributions; mathematical expectations; functions of a random variable.
Prereq: Math 21, Stat 117/equiv.
Coreq: Math 22
Stat 122: Probability Theory II
Joint, marginal, and conditional distributions; independence of several random variables; distributions and expectations of functions of random variables; characterization of F, t, and χ2 distributions; limit theorems.
Prereq: Stat 121
Coreq: Math 23
Stat 124: Introduction to Programming
Introduction to microcomputer and operating systems; principles of programming; programming using a high-level computer language (e.g., PASCAL).
Prereq: Stat 114/101/equiv.
Stat 125: Applications Software & Software Packages
Use of Statistical software packages (e.g., SAS, SPSS) for database management & basic Statistical analysis.
Prereq: Stat 115/ 101/equiv, Stat 124/equiv.
Stat 131: Parametric Statistical Inference
Population and sample; Statistics and sampling distributions; point and interval estimation; Statistical hypothesis testing; inference based on the normal distribution and applications of z, t, χ2, and F distribution.
Prereq: Stat 122, Math 23
Stat 132: Nonparametric Statistical Inference
Levels of measurement; goodness of fit tests; sign and signed rank tests; distribution tests; association tests; tests for independence.
Prereq: Stat 131, Stat 125
Stat 133: Bayesian Statistical Inference
Elements of Bayesian probability inference; assessment of prior likelihood and posterior distributions; Bayesian estimation and hypothesis testing; predictive distribution and asymptotics; Bayesian Hierarchical Models; introduction to Empirical Bayes; use of Statistical software.
Prereq: Stat 131, Stat 124
Stat 134: Introduction to Scientific Writing for Statistics. Principles and methods in scientific writing in Statistics.
Prereq: Junior Standing (must have passed at least 60 units in the program)
Stat 135: Matrix Theory for Statistics
Matrix operations; properties of matrices; special matrices; matrix calculus; determinants; eigenvalues and eigenvectors; linear systems; vector spaces; use of software applications.
Prereq: Math 21, Stat 125
Stat 136: Introduction to Regression Analysis
Linear regression model; model selection; regression diagnostics; use of dummy variables; remedial measures.
Prereq: Stat 131, Stat 135
Stat 138: Introduction to Sampling Designs
Probability and non-probability sampling techniques; complex surveys; variance estimation; treatment of missing data; applications to various contexts.
Prereq: Stat 131, Stat 125
Stat 142: Introduction to Computational Statistics
Contemporary themes in computational Statistics; Survey of computationally-intensive methods in Statistics; Advanced data management; SQL programming; Resampling methods; Simulations; Macro programming.
Prereq: Stat 136
Stat 143: Survey Operations
Research process; techniques of data collection; principles of questionnaire design; data coding and encoding; data quality control; presentation of research findings.
Prereq: Senior standing (only a maximum of 19 units of required Statistics core courses to complete the curriculum, including Stat 143), Stat 134, Stat 138
Stat 145: Introduction to Time Series Analysis & Forecasting
Classical methods; ARIMA models; Box- Jenkins method; intervention analysis; GARCH Models; regression with time series data; applications.
Prereq: Stat 136
Stat 146: Introduction to Exploratory Data Analysis
Displaying and summarizing batches; re-expressing data; median polish; robust and resistant measures; fitting resistant lines.
Coreq: Stat 136
Stat 147: Introduction to Multivariate Analysis
Multivariate normal distribution; inference on mean vector and dispersion matrices; principal component analysis; factor analysis; cluster analysis; discriminant analysis; canonical correlation analysis; correspondence analysis and perceptual mapping; multivariate analysis of variance (MANOVA); applications to various contexts.
Prereq: Stat 136
Stat 148: Introduction to Experimental Designs
Principles of experimentation; completely randomized design; randomized complete block design; Latin-square design; factorial experiments; confounding; incomplete block design; analysis of covariance; nested and split-plot designs; response surface methodology; applications to various contexts.
Prereq: Stat 136
Stat 149: Introduction to Categorical Data Analysis
Categorical data; cross-classification tables; analysis using log-linear, logistic, and logit models.
Prereq: Stat 136
ELECTIVE COURSES
Stat 191: Special Topics in Biological and Medical Statistics
Prereq: COI
Stat 191.1: Introduction to Biostatistics
Prereq: Stat 125
Coreq: Stat 148
Stat 192.1: Statistics in Market Research
Coreq: Stat 147
Stat 192.2: Advanced Linear Models
Prereq: Stat 136
Stat 193: Special Topics in Industrial and Physical Science Statistics
Prereq: COI
Stat 193.1: Introduction to Statistical Quality Control
Prereq: Stat 125, Stat 131
Stat 194: Special Topics in Social and Psychological Statistics
Prereq: COI
Stat 197: Special Topics in Statistics
Prereq: COI
*May be repeated provided that the topics are different; topics to be indicated for record purposes
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DOWNLOADABLES: -
Master of Statistics (MoS) Program
The Master of Statistics (MoS) program aims to produce practitioners who are knowledgeable in statistical methodologies and the practice of statistics in key areas. Likewise, it prepares students to meet the needs of industry and government for statistical personnel at the supervisory or higher levels. Students are provided with sound understanding of statistical concepts and methods with variety of applications.
CORE COURSES
Stat 221: Introductory Probability
Combinatorial analysis; sample space and random variables, probability distribution function; expectation; stochastic independence; common probability distributions
Stat 222: Introduction to Statistical Inference
Sampling distributions; point and interval estimation; tests of hypothesis.
Prereq: Stat 221
Stat 223: Applied Regression Analysis
Model building; diagnostic checking; remedial measures; applications.
Prereq: Stat 222/equiv
Stat 250: Sampling Designs
Concepts in designing sample surveys; non-sampling errors; simple random sampling; systematic sampling; sampling with varying probabilities; stratification, use of auxiliary information; cluster sampling; multi-stage sampling.
Prereq: Stat 222/232
Stat 251: Survey Operations
Planning a survey; sample design and sample size, frame construction; tabulation plans; preparation of questionnaires and manual of instruction; field operations; processing of data, preparation of report.
Prereq: Stat 222/232/equiv/COI, Stat 223/233/equiv/COI
OTHER COURSES & ELECTIVES
Stat 290: Statistical Consulting
Application of statistical concepts and methodologies to data of researchers seeking statistical consultancy services; Must be taken sequentially (take "The Practice of Statistics" first before "Cases in Statistical Consulting")
Prereq: COI
Stat 298: Special Problem
In the Special Problem, the student should be able to demonstrate capability in statistical analysis through the application of contemporary statistical methods in solving real problems, or the novel application of statistical methods in solving real-life problems.
LIST OF AREAS OF CONCENTRATION OF ELECTIVES
- Industrial Statistics (Stat 210, Stat 211, Stat 224, Stat 241, Stat 243, Stat 244, Stat 245, Stat 246, Stat 266, Stat 269, Stat 270, Stat 271, Stat 272, Stat 273, Stat 276)
- Statistical Methods for Market Research (Stat 210, Stat 211, Stat 225, Stat 226, Stat 241, Stat 243, Stat 247, Stat 266, Stat 267, Stat 268, Stat 269, Stat 270, Stat 274, Stat 276)
- Social Statistics (Stat 210, Stat 224, Stat 225, Stat 226, Stat 242, Stat 243, Stat 266, Stat 267, Stat 269, Stat 270, Stat 275, Stat 276)
- Mathematical Statistics (Stat 211, Stat 226, Stat 235, Stat 240, Stat 241, Stat 249, Stat 252, Stat 261, Stat 262, Stat 263, Stat 264, Stat 265, Stat 267)
- Computational Statistics (Stat 210, Stat 211, Stat 226, Stat 240, Stat 241, Stat 247, Stat 249, Stat 252, Stat 262, Stat 265, Stat 267, Stat 269, Stat 277)
- Risk Assessment Methods (Stat 211, Stat 225, Stat 226, Stat 241, Stat 242, Stat 260, Stat 261, Stat 264, Stat 267, Stat 268)
ELECTIVE COURSES
Stat 210: Statistical Software
Database management and programming using statistical software
Stat 224: Experimental Designs
Completely randomized designs; randomized complete block design; Latin square design; factorial experiments; incomplete block design; higher-order designs.
Prereq: Stat 223/233/equiv/COI
Stat 225: Time Series Analysis
Classical procedures; stationarity; Box-Jenkins modeling procedure: autocorrelation function, partial autocorrelation function, identification, estimation, diagnostic checking, forecasting; transfer functions; applications.
Prereq: Stat 223/233/equiv/COI
Stat 226: Applied Multivariate Analysis
Multivariate normal distribution; principal components analysis; biplots and h-plots; factor analysis; discriminant analysis; cluster analysis; multidimensional scaling; correspondence analysis; canonical correlation analysis; graphical and data-oriented techniques; applications.
Prereq: Stat 223/233/equiv/COI
Stat 235: Survey of Stochastic Processes
Markov chains; Markov processes; Poisson processes; renewal processes; martingales.
Prereq: Stat 221/231/equiv/COI
Stat 240: High Dimensional Data
High dimensional data; high dimensional data visualization; high dimensional data analysis; dimension reduction; pattern search; clustering; applications.
Prereq: Stat 218/223/233/equiv/COI, Stat 217/226/equiv/COI
Stat 242: Econometric Methods
Distributed lag models; structural change; simultaneous equations; limited dependent variables; ARCH, GARCH processes; cointegration; applications.
Prereq: Stat 223/233/equiv/COI
Stat 243: Categorical Data Analysis
Cross-classified tables, multidimensional tables; loglinear models; logit models, measures of association; inference for categorical data; applications.
Prereq: Stat 223/233/equiv/COI
Stat 245: Survival Analysis
Functions of survival time; estimation of survival functions; survival distributions and their applications; distribution fitting and goodness-of-fit tests.
Prereq: Stat 207/222/232/equiv/COI
Stat 246: Response Surface Methods
Product design and development; optimal designs; response surface models; response surface optimization; applications.
Prereq: Stat 223/233/equiv/COI
Stat 247: Data Mining and Business Intelligence
Principles of data mining; methods of data mining; themes of data mining; applications of data mining in business intelligence.
Prereq: COI
Stat 249: Nonparametric Modeling
Smoothing methods; kernel smoothing; spline smoothing; regression trees; projection pursuit; nonparametric regression; cross-validation; scoring; high dimensional predictors; additive models; backfitting
Prereq: Stat 207/222/232/equiv//coi, Stat 218/223/233/equiv/COI
Stat 252: Bootstrap Methods
Empirical distribution functions; resampling and nonparametric statistical inference; optimality of the bootstrap; bootstrap in hypothesis testing; bootstrap in confidence intervals; bootstrap in regression models; bootstrap for dependent data.
Prereq: Stat 222/232/equiv/COI, Stat 223/233/equiv/COI
Stat 260: Quantitative Risk Management
Market risk; financial time series; copulas; extreme value theory; credit risk models; operational risks.
Prereq: Stat 218/223/233/equiv/COI, Stat 225/equiv/COI
Stat 261: Stochastic Calculus for Finance
Continuous-time model; Brownian motion; random walk; quadratic variation; Ito formula; Black-Scholes equation; risk-neutral measure; martingale representation theorem; fundamental theorems of asset pricing.
Prereq: Stat 221/231/equiv/COI
Stat 263: Bayesian Analysis
Bayesian inference; empirical and hierarchical analysis; robustness; numerical procedures.
Prereq: Stat 207/222/232/equiv/COI
Stat 266: Applied Nonparametric Methods
Methods for single, two, and k samples; trends and association; nonparametric bootstrap.
Prereq: Stat 222/equiv/COI, Stat 223/equiv/COI
Stat 267: Advanced Applied Multivariate Analysis
Confirmatory factor analysis; multidimensional scaling; correspondence analysis; classification trees; CHAID; procrustes analysis; neural networks; structural equation modeling
Prereq: Stat 226/equiv/COI
Stat 268: Advanced Time Series Analysis
Nonstationarity; cointegration; interventions models; state space models; transfer functions; frequency domain; panel data; nonparametric methods for time series; nonparametric prediction; AR-Sieve; block bootstrap.
Prereq: Stat 218/223/233/equiv/COI, Stat 225/equiv/COI
Stat 271: Statistical Quality Control
Overview of the statistical methods useful in quality assurance; statistical process control; control charts for variables and attributes, cusum chart, multivariate chart; process capability analysis; acceptance samplinMIL-STDSTD tables and JIS tables; off-line quality control; introduction to response surface analysis; Taguchi method; applications.
Prereq: Stat 222/232/equiv/COI
Stat 274: Market Research
The marketing research; data and data generation in marketing research; analytical methods; consumer behavior modeling
Prereq: Stat 223/233/equiv/COI, Stat 226/equiv/COI
Stat 275: Economic Statistics
The Philippine Statistical System; surveys being regularly conducted by the system: questionnaire designs, sampling designs, estimators, issues; official statistics being generated: national accounts, consumer price index, input-output table, poverty statistics, leading economic indicators, seasonally adjusted series; statistical methods useful in generating official statistics
Prereq: Stat 222/232/equiv/COI, Stat 250/ equiv/COI
Stat 276: Statistics for Geographic Information Systems
Components of a geographical information system, data structures and elements of spatial modeling; exploratory spatial data analysis; quadrat analysis, tesselations, and spatial autocorrelation; spatial modeling and prediction; some sampling theory; applications.
Prereq: COI
Stat 277: Statistics for Image Analysis
Radiometric enhancement techniques; geometric enhancement using image domain techniques; multispectral transformation of data; supervised classification techniques; clustering and unsupervised classification; applications.
Prereq: COI
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DOWNLOADABLES: -
Master of Science in Statistics (MS Statistics) Program
The Master of Science in Statistics (M.S. Statistics) program prepares its graduates for advanced level capability in the profession and provides them the necessary foundation for high-quality Ph.D. work both in the theoretical and practical aspects.
CORE COURSES
Stat 231: Probability Theory
Probability spaces and random variables; probability distributions and distribution functions; mathematical expectation; convergence of sequences of random variables; laws of large numbers; characteristic functions.
Coreq: Stat 230/equiv
Stat 232: Parametric Inference
Exponential family of densities; point estimation: sufficiency, completeness, unbiasedness, equivariance; hypothesis testing
Prereq: Stat 231
Stat 233: Linear Models
Subspaces and projections; multivariate normal distribution, non-central distributions, distribution of quadratic forms; the general linear model of full column rank, tests about the mean; tests about the variance; the general linear model not of full column rank; estimability and testability.
Prereq: Stat 232
Stat 234: Multivariate Analysis
Distribution theory for multivariate analysis; the multivariate one-and-two sample models; the multivariate linear model.
Prereq: Stat 233
Stat 250: Sampling Designs
Concepts in designing sample surveys; non-sampling errors; simple random sampling; systematic sampling; sampling with varying probabilities; stratification, use of auxiliary information; cluster sampling; multi-stage sampling.
Coreq: Stat 222/232
OTHER COURSES & ELECTIVES
Stat 230: Special Topics in Mathematics for Statistics
Special topics in mathematics and their applications in statistics. To be arranged according to the needs of students
Stat 290: Statistical Consulting
Application of statistical concepts and methodologies to data of researchers seeking statistical consultancy services; Must be taken sequentially (take "The Practice of Statistics" first before "Cases in Statistical Consulting")
Prereq: COI
Stat 300: Thesis
In the Thesis, the student should be able to demonstrate capability in conducting basic research in statistics. The work should contribute to the body of knowledge in statistical science. Such new knowledge generated from the Thesis can be derived analytically or computationally (simulations).
LIST OF AREAS OF CONCENTRATION OF ELECTIVES
- Industrial Statistics (Stat 210, Stat 211, Stat 223, Stat 224, Stat 241, Stat 243, Stat 244, Stat 245, Stat 246, Stat 266, Stat 269, Stat 270, Stat 271, Stat 272, Stat 273, Stat 276)
- Statistical Methods for Market Research (Stat 210, Stat 211, Stat 223, Stat 225, Stat 226, Stat 241, Stat 243, Stat 247, Stat 266, Stat 267, Stat 268, Stat 269, Stat 270, Stat 274, Stat 276)
- Social Statistics (Stat 210, Stat 223, Stat 224, Stat 225, Stat 226, Stat 242, Stat 243, Stat 251, Stat 266, Stat 267, Stat 269, Stat 270, Stat 275, Stat 276)
- Mathematical Statistics (Stat 211, Stat 226, Stat 235, Stat 240, Stat 241, Stat 249, Stat 252, Stat 261, Stat 262, Stat 263, Stat 264, Stat 265, Stat 267)
- Computational Statistics (Stat 210, Stat 211, Stat 226, Stat 240, Stat 241, Stat 247, Stat 249, Stat 252, Stat 262, Stat 265, Stat 267, Stat 269, Stat 277)
- Risk Assessment Methods (Stat 211, Stat 223, Stat 225, Stat 226, Stat 241, Stat 242, Stat 260, Stat 261, Stat 264, Stat 267, Stat 268)
ELECTIVE COURSES
Stat 210: Statistical Software
Database management and programming using statistical software
Stat 224: Experimental Designs
Completely randomized designs; randomized complete block design; Latin square design; factorial experiments; incomplete block design; higher-order designs.
Prereq: Stat 223/233/equiv/COI
Stat 225: Time Series Analysis
Classical procedures; stationarity; Box-Jenkins modeling procedure: autocorrelation function, partial autocorrelation function, identification, estimation, diagnostic checking, forecasting; transfer functions; applications.
Prereq: Stat 223/233/equiv/COI
Stat 226: Applied Multivariate Analysis
Multivariate normal distribution; principal components analysis; biplots and h-plots; factor analysis; discriminant analysis; cluster analysis; multidimensional scaling; correspondence analysis; canonical correlation analysis; graphical and data-oriented techniques; applications.
Prereq: Stat 223/233/equiv/COI
Stat 235: Survey of Stochastic Processes
Markov chains; Markov processes; Poisson processes; renewal processes; martingales.
Prereq: Stat 221/231/equiv/COI
Stat 240: High Dimensional Data
High dimensional data; high dimensional data visualization; high dimensional data analysis; dimension reduction; pattern search; clustering; applications.
Prereq: Stat 218/223/233/equiv/COI, Stat 217/226/equiv/COI
Stat 242: Econometric Methods
Distributed lag models; structural change; simultaneous equations; limited dependent variables; ARCH, GARCH processes; cointegration; applications.
Prereq: Stat 223/233/equiv/COI
Stat 243: Categorical Data Analysis
Cross-classified tables, multidimensional tables; loglinear models; logit models, measures of association; inference for categorical data; applications.
Prereq: Stat 223/233/equiv/COI
Stat 245: Survival Analysis
Functions of survival time; estimation of survival functions; survival distributions and their applications; distribution fitting and goodness-of-fit tests.
Prereq: Stat 207/222/232/equiv/COI
Stat 246: Response Surface Methods
Product design and development; optimal designs; response surface models; response surface optimization; applications.
Prereq: Stat 223/233/equiv/COI
Stat 247: Data Mining and Business Intelligence
Principles of data mining; methods of data mining; themes of data mining; applications of data mining in business intelligence.
Prereq: COI
Stat 249: Nonparametric Modeling
Smoothing methods; kernel smoothing; spline smoothing; regression trees; projection pursuit; nonparametric regression; cross-validation; scoring; high dimensional predictors; additive models; backfitting
Prereq: Stat 207/222/232/equiv//coi, Stat 218/223/233/equiv/COI
Stat 251: Survey Operations
Planning a survey; sample design and sample size, frame construction; tabulation plans; preparation
of questionnaires and manual of instruction; field operations; processing of data, preparation of report.
Prereq: Stat 222/232/equiv/COI, Stat 223/233/equiv/COI
Stat 252: Bootstrap Methods
Empirical distribution functions; resampling and nonparametric statistical inference; optimality of the bootstrap; bootstrap in hypothesis testing; bootstrap in confidence intervals; bootstrap in regression models; bootstrap for dependent data.
Prereq: Stat 222/232/equiv/COI, Stat 223/233/equiv/COI
Stat 260: Quantitative Risk Management
Market risk; financial time series; copulas; extreme value theory; credit risk models; operational risks.
Prereq: Stat 218/223/233/equiv/COI, Stat 225/equiv/COI
Stat 261: Stochastic Calculus for Finance
Continuous-time model; Brownian motion; random walk; quadratic variation; Ito formula; Black-Scholes equation; risk-neutral measure; martingale representation theorem; fundamental theorems of asset pricing.
Prereq: Stat 221/231/equiv/COI
Stat 262: Nonparametric Statistics
Distribution-free statistics; U-statistics; power functions; asymptotic relative efficiency of tests; confidence intervals and bounds; point estimation; linear rank statistics; other methods for constructing distribution-free procedures.
Prereq: Stat 232/equiv/COI
Stat 263: Bayesian Analysis
Bayesian inference; empirical and hierarchical analysis; robustness; numerical procedures.
Prereq: Stat 207/222/232/equiv/COI
Stat 264: Elements of Decision Theory
Basic concepts, risk function, Bayes and minimax solutions of decision problems, statistical decision functions, formulation of general decision problems
Prereq: Stat 231/equiv/COI
Stat 265: Robust Statistics
Breakdown point and robust estimators; M-, R- and L-estimates; robust tests; robust regression and outlier detection.
Prereq: Stat 232/ equiv/ COI
Stat 266: Applied Nonparametric Methods
Methods for single, two, and k samples; trends and association; nonparametric bootstrap.
Prereq: Stat 222/equiv/COI, Stat 223/equiv/COI
Stat 267: Advanced Applied Multivariate Analysis
Confirmatory factor analysis; multidimensional scaling; correspondence analysis; classification trees; CHAID; procrustes analysis; neural networks; structural equation modeling
Prereq: Stat 226/equiv/COI
Stat 268: Advanced Time Series Analysis
Nonstationarity; cointegration; interventions models; state space models; transfer functions; frequency domain; panel data; nonparametric methods for time series; nonparametric prediction; AR-Sieve; block bootstrap.
Prereq: Stat 218/223/233/equiv/COI, Stat 225/equiv/COI
Stat 271: Statistical Quality Control
Overview of the statistical methods useful in quality assurance; statistical process control; control charts for variables and attributes, cusum chart, multivariate chart; process capability analysis; acceptance samplinMIL-STDSTD tables and JIS tables; off-line quality control; introduction to response surface analysis; Taguchi method; applications.
Prereq: Stat 222/232/equiv/COI
Stat 272: Reliability Theory
Coherent systems; paths and cuts, life distribution; dependent components; maintenance policies and replacement models; domains of attraction.
Prereq: Stat 231
Stat 274: Market Research
The marketing research; data and data generation in marketing research; analytical methods; consumer behavior modeling
Prereq: Stat 223/233/equiv/COI, Stat 226/equiv/COI
Stat 275: Economic Statistics
The Philippine Statistical System; surveys being regularly conducted by the system: questionnaire designs, sampling designs, estimators, issues; official statistics being generated: national accounts, consumer price index, input-output table, poverty statistics, leading economic indicators, seasonally adjusted series; statistical methods useful in generating official statistics
Prereq: Stat 222/232/equiv/COI, Stat 250/ equiv/COI
Stat 276: Statistics for Geographic Information Systems
Components of a geographical information system, data structures and elements of spatial modeling; exploratory spatial data analysis; quadrat analysis, tesselations, and spatial autocorrelation; spatial modeling and prediction; some sampling theory; applications.
Prereq: COI
Stat 277: Statistics for Image Analysis
Radiometric enhancement techniques; geometric enhancement using image domain techniques; multispectral transformation of data; supervised classification techniques; clustering and unsupervised classification; applications.
Prereq: COI
NOTE: Stat 223 and Stat 251 are core courses in the Master of Statistics program, but these may be taken as elective courses in the M.S. (Statistics) and Ph.D. (Statistics) programs.
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DOWNLOADABLES: -
Doctor of Philosophy in Statistics (PhD Statistics) Program
The Doctor of Philosophy in Statistics (PhD Statistics) program provides students with advanced proficiency in statistics to enable them to participate in the development of statistical methods. Emphasis is placed on independent research. Seminar and reading courses are incorporated in the curriculum in order to meet the individual requirements of the student's program of research.
Non-MS Statistics graduates of the School may be required to take additional mathematics and statistics courses before admission to the program. Additional requirements shall be determined by the Graduate Committee of the School.
DOCTORAL COURSES
Stat 301: Theory of Probability I
Measure theory; probability spaces; random variables; integration; expectation and moments; convergence.
Stat 302: Theory of Probability II
Conditional expectations; dependence; martingales.
Prereq: Stat 301
Stat 303: Stochastic Processes
The theory of stochastic processes; some stochastic processes.
Prereq: Stat 302
Stat 311: Theory of Statistical Inference I
Sufficiency, completeness, exponential families, unbiasedness; equivariance; Bayes estimation; minimax estimation; admissibility.
Prereq: Stat 301
Stat 312: Theory of Statistical Inference II
Uniformly most powerful tests; unbiased tests; invariance; linear hypothesis; minimax principle.
Prereq: Stat 311
OTHER COURSES & ELECTIVES
Stat 390: Reading Course
This must be taken three times.
Stat 396: Seminar
Faculty and graduate student discussions of current research in statistics.
Stat 400: Dissertation
Electives based on Area of Concentration
- Industrial Statistics
- Mathematical Statistics
- Computational Statistics
- Market Research and Business Intelligence
- Social Statistics
- Risk Management and Stochastic Finance
ELECTIVE COURSES
Stat 210: Statistical Software
Database management and programming using statistical software
Stat 224: Experimental Designs
Completely randomized designs; randomized complete block design; Latin square design; factorial experiments; incomplete block design; higher-order designs.
Prereq: Stat 223/233/equiv/COI
Stat 225: Time Series Analysis
Classical procedures; stationarity; Box-Jenkins modeling procedure: autocorrelation function, partial autocorrelation function, identification, estimation, diagnostic checking, forecasting; transfer functions; applications.
Prereq: Stat 223/233/equiv/COI
Stat 226: Applied Multivariate Analysis
Multivariate normal distribution; principal components analysis; biplots and h-plots; factor analysis; discriminant analysis; cluster analysis; multidimensional scaling; correspondence analysis; canonical correlation analysis; graphical and data-oriented techniques; applications.
Prereq: Stat 223/233/equiv/COI
Stat 235: Survey of Stochastic Processes
Markov chains; Markov processes; Poisson processes; renewal processes; martingales.
Prereq: Stat 221/231/equiv/COI
Stat 240: High Dimensional Data
High dimensional data; high dimensional data visualization; high dimensional data analysis; dimension reduction; pattern search; clustering; applications.
Prereq: Stat 218/223/233/equiv/COI, Stat 217/226/equiv/COI
Stat 242: Econometric Methods
Distributed lag models; structural change; simultaneous equations; limited dependent variables; ARCH, GARCH processes; cointegration; applications.
Prereq: Stat 223/233/equiv/COI
Stat 243: Categorical Data Analysis
Cross-classified tables, multidimensional tables; loglinear models; logit models, measures of association; inference for categorical data; applications.
Prereq: Stat 223/233/equiv/COI
Stat 245: Survival Analysis
Functions of survival time; estimation of survival functions; survival distributions and their applications; distribution fitting and goodness-of-fit tests.
Prereq: Stat 207/222/232/equiv/COI
Stat 246: Response Surface Methods
Product design and development; optimal designs; response surface models; response surface optimization; applications.
Prereq: Stat 223/233/equiv/COI
Stat 247: Data Mining and Business Intelligence
Principles of data mining; methods of data mining; themes of data mining; applications of data mining in business intelligence.
Prereq: COI
Stat 249: Nonparametric Modeling
Smoothing methods; kernel smoothing; spline smoothing; regression trees; projection pursuit; nonparametric regression; cross-validation; scoring; high dimensional predictors; additive models; backfitting
Prereq: Stat 207/222/232/equiv//coi, Stat 218/223/233/equiv/COI
Stat 251: Survey Operations
Planning a survey; sample design and sample size, frame construction; tabulation plans; preparation
of questionnaires and manual of instruction; field operations; processing of data, preparation of report.
Prereq: Stat 222/232/equiv/COI, Stat 223/233/equiv/COI
Stat 252: Bootstrap Methods
Empirical distribution functions; resampling and nonparametric statistical inference; optimality of the bootstrap; bootstrap in hypothesis testing; bootstrap in confidence intervals; bootstrap in regression models; bootstrap for dependent data.
Prereq: Stat 222/232/equiv/COI, Stat 223/233/equiv/COI
Stat 260: Quantitative Risk Management
Market risk; financial time series; copulas; extreme value theory; credit risk models; operational risks.
Prereq: Stat 218/223/233/equiv/COI, Stat 225/equiv/COI
Stat 261: Stochastic Calculus for Finance
Continuous-time model; Brownian motion; random walk; quadratic variation; Ito formula; Black-Scholes equation; risk-neutral measure; martingale representation theorem; fundamental theorems of asset pricing.
Prereq: Stat 221/231/equiv/COI
Stat 262: Nonparametric Statistics
Distribution-free statistics; U-statistics; power functions; asymptotic relative efficiency of tests; confidence intervals and bounds; point estimation; linear rank statistics; other methods for constructing distribution-free procedures.
Prereq: Stat 232/equiv/COI
Stat 263: Bayesian Analysis
Bayesian inference; empirical and hierarchical analysis; robustness; numerical procedures.
Prereq: Stat 207/222/232/equiv/COI
Stat 266: Applied Nonparametric Methods
Methods for single, two, and k samples; trends and association; nonparametric bootstrap.
Prereq: Stat 222/equiv/COI, Stat 223/equiv/COI
Stat 267: Advanced Applied Multivariate Analysis
Confirmatory factor analysis; multidimensional scaling; correspondence analysis; classification trees; CHAID; procrustes analysis; neural networks; structural equation modeling
Prereq: Stat 226/equiv/COI
Stat 268: Advanced Time Series Analysis
Nonstationarity; cointegration; interventions models; state space models; transfer functions; frequency domain; panel data; nonparametric methods for time series; nonparametric prediction; AR-Sieve; block bootstrap.
Prereq: Stat 218/223/233/equiv/COI, Stat 225/equiv/COI
Stat 271: Statistical Quality Control
Overview of the statistical methods useful in quality assurance; statistical process control; control charts for variables and attributes, cusum chart, multivariate chart; process capability analysis; acceptance samplinMIL-STDSTD tables and JIS tables; off-line quality control; introduction to response surface analysis; Taguchi method; applications.
Prereq: Stat 222/232/equiv/COI
Stat 274: Market Research
The marketing research; data and data generation in marketing research; analytical methods; consumer behavior modeling
Prereq: Stat 223/233/equiv/COI, Stat 226/equiv/COI
Stat 275: Economic Statistics
The Philippine Statistical System; surveys being regularly conducted by the system: questionnaire designs, sampling designs, estimators, issues; official statistics being generated: national accounts, consumer price index, input-output table, poverty statistics, leading economic indicators, seasonally adjusted series; statistical methods useful in generating official statistics
Prereq: Stat 222/232/equiv/COI, Stat 250/ equiv/COI
Stat 276: Statistics for Geographic Information Systems
Components of a geographical information system, data structures and elements of spatial modeling; exploratory spatial data analysis; quadrat analysis, tesselations, and spatial autocorrelation; spatial modeling and prediction; some sampling theory; applications.
Prereq: COI
Stat 277: Statistics for Image Analysis
Radiometric enhancement techniques; geometric enhancement using image domain techniques; multispectral transformation of data; supervised classification techniques; clustering and unsupervised classification; applications.
Prereq: COI
NOTE: Stat 223 and Stat 251 are core courses in the Master of Statistics program, but these may be
taken as elective courses in the M.S. (Statistics) and Ph.D. (Statistics) programs.
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Professional Master in Data Science (Analytics) Program
The Professional Master in Data Science (Analytics) program is suited for professionals who have quantitative backgrounds and are hands-on in data processing and analysis or those who value the importance of empirical or evidenced-based decision-making. The program aims to equip professionals with a solid foundation in statistical science and proficiency in statistical machine learning to solve real-world problems.
Upon graduation from the program, students are expected to have strong technical aptitude and advanced skills in data science and analytics and evidence-based decision-making.
CORE COURSES
Stat 207: Statistical Inference for Data Science
Concepts in probability, probability distributions, and sampling distribution; classical statistical inference; computational inference; principles of data science.
Stat 208: Programming for Data Analytics
Programming tools and software packages for analytics; modular and efficient programming; advanced data management; SQL; working with different data structures (e.g. time series, unstructured, big data); high-performance programming.
Stat 217: Computational Statistics
Random numbers; Monte Carlo methods; Markov chain Monte Carlo; resampling methods; optimization methods; approaches for classification and regression problems; methods for feature extraction.
Stat 218: Statistical Machine Learning
Applications of statistical machine learning; generalized linear models; supervised learning; unsupervised learning; kernel methods; support vector machines; neural networks; ensemble learning; contemporary topics.
Stat 227: Knowledge Discovery in Data
Frameworks and processes of knowledge discovery in data, common data issues, data cleansing procedures, feature engineering, data exploration, data mining, data journalism, and storytelling.
CULMINATING COURSE
Stat 299: Special Project in Data Science
Integration and application of foundations, theories, and methods of data analytics to address problems in industry, government, and other sectors; design and implementation of individual or group capstone project that is either project-oriented (engagement with and solution for a client) or research-oriented (work on own or client’s agenda).
ELECTIVE COURSES
Stat 280: Forecasting Analytics
Time series graphics; Simple forecasting methods; Residual diagnostics; Exponential smoothing; ARIMA models; Forecasting hierarchical or grouped time series; Judgmental forecasts; Time series regression models; Time series decomposition; Practical forecasting issues
Stat 280: Bayesian Analytics
Fundamentals of Bayesian inference; Single-parameter models; Multiparameter models; Hierarchical models; Bayesian computation; Markov Chain simulation; Generalized linear models; Models for robust inference; Models for missing data; Parametric non-linear models; Gaussian process models; Finite mixture models; Dirichlet process models
Stat 280: Deep Learning
Basic perceptron algorithms; convolutional and recurrent neural networks (CNNs, RNNs), autoencoders, restricted Boltzmann machines (RBMs), and deep belief networks (DBNs); applications in the fields of business analytics, epidemiology, econometrics, agricultural metrics, climatology, and artificial intelligence, among
others.
Stat 280: Analytics Deployment 101
Analytics end-to-end process; Common use cases and deployment examples; Analytics strategy and building a roadmap; Deployment planning and considerations; Deployment execution; Model monitoring reports; Campaign/ deployment monitoring reports; Business value realization
Stat 280: Practical Machine Learning for Business
End-to-end discussion of three machine learning use cases used in business namely: recommender systems, fraud detection, and conversational chatbot; Discussion on concepts, processes, and hands-on analysis and modeling to address the business requirements for each use case; Use of Python programming.
Stat 280: Advanced Time Series Analysis for Analytics
Nonstationarity; cointegration; interventions models; state space models; transfer functions; frequency domain; panel data; nonparametric methods for time series; nonparametric prediction; AR-Sieve; block bootstrap; applications in analytics
Stat 280: Domain Deep Dive for Data Science and Analytics (DSA) Practitioners
Deep-dive into selected business domains that lead to the identification of DSA use cases or applications, following the framework of business analysis; focus on deep dive on the Financial Services Industry and Business Process Outsourcing Industry.
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Doctor of Philosophy (Data Science)
The program aims to produce PhD graduates equipped with a good scientific mindset, with adequate technical skills, and a professional perspective of expanding the science of data, with data as carriers of information.
DS 301: Foundations of Data Science
Data science history, concepts, and underlying philosophy, data cycle and handling and the associated legal & ethical frameworks; Prereq: None
DS 396: Graduate Seminar
Seminar course on recent work in developing concepts, tools, and methods in Data Science; Prereq: DS 301; Maybe taken up to three (3) times. Those admitted in the program with bachelor’s degree (Option 1) are required to have finished at least 12 units of course work under the curriculum.
DS 397: Special Topics in Data Science
Prereq: COI; May be taken up to three (3) times provided the subject titles are different.
DS 398: Advanced Studies in Data Science
Conduct of directed, specific research on a problem in the field of specialization, preparation and submission of scientific manuscript in a highly reputable refereed journal; Prereq: COI; Must be taken twice. Each course may be split into two separate semesters with two (2) units each.
DS 399: Research Methods
Development and discussion of applicable research methods for and consideration of ethics in dissertation topic proposal; Prereq: COI; Must have passed the Ph.D. Candidacy Exam and have graduating status (i.e. only DS 400 is left in the succeeding semester/s.
DS 400: PhD Dissertation
Must have passed the candidacy exam and completed all other course requirements; Maybe taken in parts, provided a total of 12 units of DS 400 has been taken before being allowed to graduate. Each part may only be broken into 3-unit, 4-unit and 6-unit courses, or their multiples (i.e., 3, 4, 6, 8, 9, or 12 units respectively corresponding to 3, 4, 6, 8, 9, or 12 hours of independent study).
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Highlights
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