Academics 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 a 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, modelling, 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. -
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.
Stat 223: Applied Regression Analysis
Model building; diagnostic checking; remedial measures; applications.
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.
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.
OTHER COURSES & ELECTIVES
Stat 290: Statistical Consulting
Application of statistical concepts and methodologies to data of researchers seeking statistical consultancy services.
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.
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.
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.
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.
Stat 235: Survey of Stochastic Processes
Markov chains; Markov processes; Poisson processes; renewal processes; martingales.
Stat 240: High Dimensional Data
High dimensional data; high dimensional data visualization; high dimensional data analysis; dimension reduction; pattern search; clustering; applications.
Stat 242: Econometric Methods
Distributed lag models; structural change; simultaneous equations; limited dependent variables; ARCH, GARCH processes; cointegration; applications.
Stat 243: Categorical Data Analysis
Cross-classified tables, multidimensional tables; loglinear model; logit models, measures of association; inference for categorical data; applications.
Stat 245: Survival Analysis
Functions of survival time; estimation of survival functions; survival distributions and their applications; distribution fitting and goodness-of-fit tests.
Stat 246: Response Surface Methods
Product design and development; optimal designs; response surface models; response surface optimization; applications.
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.
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
Stat 260: Quantitative Risk Management
Market risk; financial time series; copulas; extreme value theory; credit risk models; operational risks.
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.
Stat 263: Bayesian Analysis
Bayesian inference; empirical and hierarchical analysis; robustness; numerical procedures.
Stat 266: Applied Nonparametric Methods
Methods for single, two and k samples; trends and association; nonparametric bootstrap.
Stat 267: Advanced Applied Multivariate Analysis
Confirmatory factor analysis; multidimensional scaling; correspondence analysis; classification trees; CHAID; procrustes analysis; neural networks; structural equation modeling
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.
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 sampling; MIL STD tables and JIS tables; off-line quality control; introduction to response surface analysis; Taguchi method; applications.
Stat 274: Market Research
The marketing research; data and data generation in marketing research; analytical methods; consumer behavior modeling
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
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.
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. -
Master of Science in Statistics (MS Statistics) Program
The Master of Science in Statistics (MS Statistics) program prepares its graduates for advanced level capability in the profession as well as provides them the necessary foundation for high quality PhD 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.
Stat 232: Parametric Inference
Exponential family of densities; point estimation: sufficiency, completeness, unbiasedness, equivariance; hypothesis testing
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.
Stat 234: Multivariate Analysis
Distribution theory for multivariate analysis; the multivariate one-and-two sample models; the multivariate linear model.
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.
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.
Stat 300: Thesis
In the Thesis, the student should be able to demonstrate capability in conducting basic research in statistics. The work should contribute in the body of knowledge in the statistical science. Such new knowledge generated from the thesis can be derived analytically or computationally (simulations).
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.
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.
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.
Stat 235: Survey of Stochastic Processes
Markov chains; Markov processes; Poisson processes; renewal processes; martingales.
Stat 240: High Dimensional Data
High dimensional data; high dimensional data visualization; high dimensional data analysis; dimension reduction; pattern search; clustering; applications.
Stat 242: Econometric Methods
Distributed lag models; structural change; simultaneous equations; limited dependent variables; ARCH, GARCH processes; cointegration; applications.
Stat 243: Categorical Data Analysis
Cross-classified tables, multidimensional tables; loglinear model; logit models, measures of association; inference for categorical data; applications.
Stat 245: Survival Analysis
Functions of survival time; estimation of survival functions; survival distributions and their applications; distribution fitting and goodness-of-fit tests.
Stat 246: Response Surface Methods
Product design and development; optimal designs; response surface models; response surface optimization; applications.
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.
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
Stat 260: Quantitative Risk Management
Market risk; financial time series; copulas; extreme value theory; credit risk models; operational risks.
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.
Stat 263: Bayesian Analysis
Bayesian inference; empirical and hierarchical analysis; robustness; numerical procedures.
Stat 266: Applied Nonparametric Methods
Methods for single, two and k samples; trends and association; nonparametric bootstrap.
Stat 267: Advanced Applied Multivariate Analysis
Confirmatory factor analysis; multidimensional scaling; correspondence analysis; classification trees; CHAID; procrustes analysis; neural networks; structural equation modeling
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.
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 sampling; MIL STD tables and JIS tables; off-line quality control; introduction to response surface analysis; Taguchi method; applications.
Stat 274: Market Research
The marketing research; data and data generation in marketing research; analytical methods; consumer behavior modeling
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
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.
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. -
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.
Stat 303: Stochastic Processes
The theory of stochastic processes; some stochastic processes.
Stat 311: Theory of Statistical Inference I
Sufficiency, completeness, exponential families, unbiasedness; equivariance; Bayes estimation; minimax estimation; admissibility.
Stat 312: Theory of Statistical Inference II
Uniformly most powerful tests; unbiased tests; invariance; linear hypothesis; minimax principle.
OTHER COURSES & ELECTIVES
Stat 390: Reading Course
This must be taken three times.
Stat 396: Seminar
Faculty and graduate student discussions of current researches 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.
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.
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.
Stat 235: Survey of Stochastic Processes
Markov chains; Markov processes; Poisson processes; renewal processes; martingales.
Stat 240: High Dimensional Data
High dimensional data; high dimensional data visualization; high dimensional data analysis; dimension reduction; pattern search; clustering; applications.
Stat 242: Econometric Methods
Distributed lag models; structural change; simultaneous equations; limited dependent variables; ARCH, GARCH processes; cointegration; applications.
Stat 243: Categorical Data Analysis
Cross-classified tables, multidimensional tables; loglinear model; logit models, measures of association; inference for categorical data; applications.
Stat 245: Survival Analysis
Functions of survival time; estimation of survival functions; survival distributions and their applications; distribution fitting and goodness-of-fit tests.
Stat 246: Response Surface Methods
Product design and development; optimal designs; response surface models; response surface optimization; applications.
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.
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
Stat 260: Quantitative Risk Management
Market risk; financial time series; copulas; extreme value theory; credit risk models; operational risks.
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.
Stat 263: Bayesian Analysis
Bayesian inference; empirical and hierarchical analysis; robustness; numerical procedures.
Stat 266: Applied Nonparametric Methods
Methods for single, two and k samples; trends and association; nonparametric bootstrap.
Stat 267: Advanced Applied Multivariate Analysis
Confirmatory factor analysis; multidimensional scaling; correspondence analysis; classification trees; CHAID; procrustes analysis; neural networks; structural equation modeling
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.
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 sampling; MIL STD tables and JIS tables; off-line quality control; introduction to response surface analysis; Taguchi method; applications.
Stat 274: Market Research
The marketing research; data and data generation in marketing research; analytical methods; consumer behavior modeling
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
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.
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. -
Professional Master in Data Science (Analytics) Program
The Professional Master in Data Science (Analytics) program is suited for professionals who have quantitative background 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 at 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 identification of DSA use cases or applications, following the framework on business analysis; focus on deep dive on the Financial Services Industry and Business Process Outsourcing Industry.
Highlights
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