This course is available for purchase from now and until 31 July 2021, and examinations are offered until June 2023. This Ph.D.-level course is the second in a two-quarter sequence with Business 41901. 100 Units. This course covers tools from probability and the elements of statistical theory. With some additional statistical background (which can be acquired after the course), the participants will be able to read articles in the area. Throughout business and internet applications including machine intelligence, reinforcement learning, image and speech recognition will be used to illustrate the wide range of applications. Topics will include non-linear estimation, multi-variate and simultaneous systems of equations, and qualitative and limited dependent variables. It considers analytical issues caused by violations of the Gauss-Markov assumptions, including linearity (functional form), heteroscedasticity, and panel data. The methods will be illustrated from applications in various areas, such as biological science, biomedical science, public health, epidemiology, education, social science, economics, psychology, agriculture and engineering. Courses in quantitative methods are usually offered at the university level to graduate students. The course will survey and practically apply many of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. Note(s): Students may count either STAT 25100 or STAT 25150, but not both, toward the forty-two credits required for graduation. SOSC 36006: Foundations of Statistical Theory (Fall) The methods we will build on include regression techniques, maximum likelihood, method of moments estimators, as well as some non-parametric methods. The coverage of topics in probability is limited and brief, so students who have taken a course in probability find reinforcement rather than redundancy. This course will introduce students to regression analysis and explore its uses in policy analyses. STAT 22200. Techniques covered include an advanced overview of linear and logistic regression, model choice and false discovery rates, multinomial and binary regression, classification, decision trees, factor models, clustering, the bootstrap and cross-validation. ECON 31703    Topics in Econometrics Programming will be based on Python and R, but previous exposure to these languages is not assumed. One may view it as an “applied” version of Stat 30900 although it is not necessary to have taken Stat 30900; the only prerequisite for this course is basic linear algebra. Equivalent Course(s): FINM 33180, CAAM 32940, STAT 33910/FINM 33170 Financial Statistics: Time Series, Forecasting, Mean Reversion, and High Frequency Data. Equivalent Course(s): PBHS 32400 a grade of at least C, or STAT 22200 or 22600 or 24500 or 24510 or PBHS 32100, or AP Statistics credit for STAT 22000. For course description contact Psychology. PSYC 36210/CPNS 31000 Mathematical Methods for Biological Sciences I (Winter) This course provides a transition between statistical theory and practice. STAT 24620/32950 Multivariate Statistical Analysis: Applications and Techniques (Spring) All statistical concepts and methods will be illustrated with applications to a series of scientific inquiries organized around describing and understanding adolescent transitions into adulthood across demographic subpopulations in contemporary American society. As a result of technological advances over the past few decades, there is a tremendous wealth of genetic data currently being collected. PPHA 42000  Applied Econometrics I PPHA 42000, Applied Econometrics I (PhD Level) (Fall)  You decide to establish a start-up in marketing consulting. This course will include lectures on the following topics: review of asymptotics for low dimensional time series analysis (linear and nonlinear processes; nonparametric methods; spectral and time domain approaches); covariance, precision, and spectral density matrix estimation for high dimensional time series; factor models; estimation of high dimensional vector autoregressive processes; prediction; and high dimensional central limit theorems under dependence. Some knowledge of ODEs may also be helpful. STAT 24500  Statistical Theory and Methods II Prerequisite(s): Familiarity with calculus, linear algebra, and probability/statistics at the level of STAT 24400 or STAT 24410. Epidemiology is a quantitative field and draws on biostatistical methods. Analytic skills include stability analysis, phase portraits, limit cycles, and bifurcations. Part III examines the consequences of data that is “poorly behaved” and how to cope with the problem. Prerequisite(s): Linear algebra (MATH 19620 or 20250 or STAT 24300 or equivalent) and STAT 24400 or STAT 24410. 1003 reviews, Rated 4.6 out of five stars. Public Policy 42200, the third in a Three-part sequence, is a basic course in applied econometrics designed to provide students with the tools necessary to evaluate and conduct empirical research. (Fall) Prerequisite(s): STAT 22000 or 23400 with a grade of at least C+, or STAT 22400 or 22600 or 24500 or 24510 or PBHS 32100, or AP Statistics credit for STAT 22000. 100 Units. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, basic probability, random variables and expected values, confidence intervals and significance tests for one- and two-sample problems for means and proportions, chi-square tests, linear regression, and, if time permits, analysis of variance. The questionnaire has played a critical role in gathering data used to assist in making public policy, evaluating social programs, and testing theories about social behavior (among other uses). Prerequisite(s): STAT 34300 or consent of instructor, STAT 34800 Modern Methods in Applied Statistics (Spring) Graduate students in Statistics or Financial Mathematics can enroll without prerequisites. Models with stochastic growth are accommodated and their properties analyzed. Predictive Analytics. BUSN 41202   Analysis of Financial Time Series This class also presents an opportunity to reflect on big picture issues of how to treat uncertainty and risk; discount costs and benefits received in the future; value lives saved; and manage other difficult matters. Part of the course will be devoted to elementary asymptotic methods that are useful in the practice of statistics, including methods to derive asymptotic distributions of various estimators and test statistics, such as Pearson’s chi-square, standard and nonstandard asymptotics of maximum likelihood estimators and Bayesian estimators, asymptotics of order statistics and extreme order statistics, Cramer’s theorem including situations in which the second-order term is needed, and asymptotic efficiency. Equivalent Course(s): PBHS 32400 a grade of at least C, or STAT 22200 or 22600 or 24500 or 24510 or PBHS 32100, or AP Statistics credit for STAT 22000. Let’s look at a simple example. Prereq: Business 41901 or instructor consent. Throughout business and internet applications including machine intelligence, reinforcement learning, image and speech recognition will be used to illustrate the wide range of applications. This course is the second quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. In computer labs, students learn optimization methods and stochastic algorithms, e.g., Markov Chain, Monte Carlo, and Gillespie algorithm. (Spring) The course addresses a variety of practical consulting problems and their solutions. This course continues from either STAT 24400 or STAT 24410 and covers statistical methodology, including the analysis of variance, regression, correlation, and some multivariate analysis. BUSN 41910/ STAT 33500 Time-series Analysis for Forecasting and Model Building (Fall) Among other examples, we will apply these techniques to detecting spam in email, click-through rate prediction in online advertisement, image classification, face recognition, sentiment analysis and churn prediction. Open only for Graduate students and 3rd and 4th year undergraduates. Prerequisite(s): STAT 24500 w/B- or better or STAT 24510 w/C+ or better is required; alternatively STAT 22400 w/B- or better and exposure to multivariate calculus (MATH 16300 or MATH 16310 or MATH 19520 or MATH 20000 or MATH 20500 or MATH 20510 or MATH 20800). Prerequisite(s): PBHS 32400, STAT 22400 or STAT 24500 or equivalent or consent of instructor. Students that did not take one of these courses but believe they have a strong background in statistics can still bid for the course given the explicit written permission of the instructor. The course will comprise three broad streams: the design and analysis of social experiments and quasi-experiments; the design and analysis of sample surveys; and how the interrelationships between the two approaches can strengthen causal claims from social data. There are two major challenges in providing evidence [generalizing findings] from social research: (i) determining causation and (ii) generalizing results from a sample of observed cases to the rest of the (unobserved) population. With MasterTrack™ Certificates, portions of Master’s programs have been split into online modules, so you can earn a high quality university-issued career credential at a breakthrough price in a flexible, interactive format. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22200 or STAT 23400, or by consent. BUSN 41201   Big Data 3 2 Descriptive Statistics Descriptive statistics are often used to describe variables. Part III examines the consequences of data that is “poorly behaved” and how to cope with the problem. There will be a strong emphasis on stochastic processes and inference in complex hierarchical statistical models. STAT 37601/CMSC 25025 Machine Learning and Large-Scale Data Analysis. This course is about regression, a powerful and widely used data analysis technique wherein we seek to understand how different random quantities relate to one another. The class builds heavily on material developed in 41902, and it is strongly recommended that students have taken 41902 or equivalent before enrolling in this course. The emphasis in this course will be on the extraction of information about political and social phenomena, not upon properties of estimators. STAT 26300/35490 Introduction to Statistical Genetics (Spring). Under certain assumptions, these models allow us to partially pool information across groups in order to efficiently model the group structure even when the number of observations within each group is small. To what extent do individuals from different socioeconomic backgrounds hold different types of political attitudes and engage in different types of political behavior? STAT 34700   Generalized Linear Models 100 Units. Epidemiology is a quantitative field and draws on biostatistical methods. It considers analytical issues caused by violations of the Gauss-Markov assumptions, including linearity (functional form), heteroscedasticity, and panel data. This course is meant to give future consultants and entrepreneurs important tools and ways of thinking that are relevant for dealing with insightful consulting and are useful in the practice of marketing consulting and beyond. Students will become familiar with various research designs, measurement, and advanced analytic strategies broadly applicable to theory-driven and data-informed quantitative research in many disciplines. This course will provide an introduction to the linear model, the dominant form of statistical inference in the social sciences. PBHS 30910/STAT 22810/PPHA 36410/ENST 27400/BIOS 27810 Epidemiology and Population Health (Fall). Students will review the course content and learn to use the Stata software in the lab under the TA’s guidance. Such data are increasingly common in many areas of empirical social science research. The course covers univariate and bivariate descriptive statistics, an introduction to statistical inference, t test, two-way contingency table, analysis of variance, simple linear regression, and multiple regression. The course is set in Hawaii at a fictional resort hotel. We will consider the complementary strengths of surveys and experiments in assessing evidence for generalization in policy areas; randomized clinical trials in medicine, field experiments in economics and psychology, and the use of scientific evidence in policy formulation will be among the examples. Topics covered include: the PAC framework, Bayesian learning, graphical models, clustering, dimensionality reduction, kernel methods including SVMs, matrix completion, neural networks, and an introduction to statistical learning theory. Prerequisite(s): ((MATH 16300 or MATH 16310 or MATH 20500 or MATH 20510 or MATH 20900), with no grade requirement), or ((MATH 19520 or MATH 20000) with (either a minimum grade of B-, or STAT major, or currently enrolled in prerequisite course)). Students will learn how to use deep learning to analyze a variety of complex real world problems. Prerequisite(s): BIOS 20151 or BIOS 20152 or consent of the instructor. Recent advances in computing have made the estimation of multilevel models much more practical. For course description contact Psychology. PQ: PSYC 37300 (No substitutions) or permission of instructor. We will discuss applications and examples from the fields of education, demography, health, crime, job training, and others. BUSN 41301 Statistical Insight into Marketing, Consulting, and Entrepreneurship (Fall) Course Outline. Previous exposure to linear algebra is helpful. The course is designed to offer an overview of and present the common logic underlying a wide range of methods developed for rigorous quantitative inquiry in the social and behavioral sciences. The goal of this course is to introduce students to program evaluation and provide an overview of current issues and methods in impact evaluation. On successfully completing the course, students will have acquired enough knowledge of the underlying machinery to intuit and implement solutions to non-trivial data science problems arising in biology and medicine. Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200, and the equivalent of two quarters of calculus (MATH 13200 or higher). ; (9) state-space models and Kalman filter; and (10) models for high frequency data. Graduate course covering recent research on the field of econometrics. Prerequisite(s): (MATH 19520 or MATH 20000 with a grade of B or better), or MATH 16300 or 16310 or 20250 or 20300 or 20310 or 20700 or STAT 24300 or PHYS 22100. Topics for the course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods including matching, stratification, inverse-probability-of-treatment-weighting (IPTW), marginal mean weighting through stratification (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RDD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-section and panel data, and fixed effects model.Intermediate Statistics or equivalent such as STAT 224/PBHS 324, PP 31301, BUS 41100, or SOC 30005 is a prerequisite. MAPS 31702    Data Science, SOSC 36006   Foundations of Statistical Theory Unlike marketing research, marketing consulting is a problem-solving endeavor that requires a great deal of specificity and is fueled by experience. Prerequisite(s): PBHS 32100 or STAT 22000 or other introductory statistics highly desirable. Mathematics employed is to the level of single-variable differential and integral calculus and sequences and series. This course is a graduate-level methods class that aims to train you to solve real-world statistical problems. The goal of the course is for students to be able to choose an appropriate statistical method to solve a given problem of data analysis and communicate your results clearly and succinctly. Take courses from the world's best instructors and universities. (8) Multivariate series: cross correlation matrices, simple vector AR models, co-integration and threshold co-integration, pairs trading, factor models and multivariate volatility models. Examples are drawn from the social, physical, and biological sciences. PLSC 43401   Mathematical Foundations of Political Methodology, PPHA 31102    Statistical Data Analysis I ECON 35003    Human Capital, Markets, and the Family The age of ubiquitous data is rapidly transforming scientific research, and advanced analytics powered by sophisticated learning algorithms is uncovering new insights in complex open problems in biology and biomedicine. This course will cover principles of data structure and algorithms, with emphasis on algorithms that have broad applications in computational biology. An important aspect of the course is to learn and apply open source software tools, including R and GeoDa.Â. In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. (Fall) Among other examples, we will consider consumer database mining, internet and social media tracking, network analysis, and text mining. Students develop an appreciation for statistics, become statistically literate, learn to use statistical techniques properly, gain confidence using statistical software, and acquire the skills necessary to look at statistical analyses critically. Prerequisite(s): MATH 13100 or 15100 or 15200 or 15300 or 16100 or 16110 or 15910 or 19520 or 19620 or 20250 or 20300 or 20310. This course is the first quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. Although an overview of relevant statistical theory will be presented, emphasis is on the development of statistical solutions to interesting applied problems. Quantitative Analysis I. The course will cover statistical applications in medicine, mental health, environmental science, analytical chemistry, and public policy. The acquisition of hands-on skills will be emphasized over machine learning theory. STAT 30810. Note(s): Students may count either STAT 24500 or STAT 24510, but not both, toward the forty-two credits required for graduation. Prereq: Bus 41000 (OR 41100) is mandatory: strict. This class is part of the course bundle: Behavioral and Experimental Immersion. Some familiarity with linear algebra is strongly recommended. BUSN 41201 Big Data (Spring) Program elective. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Knowledge of probability and statistical estimation techniques (e.g. PBHS 32901/STAT 35201     Introduction to Clinical Trials (Winter) Grouped data, such as students within schools or workers within firms, are ubiquitous in public policy. Students complete an independent project on a topic of their interest.  We will examine the impact of questions on data quality and will review past and recent methodological research on questionnaire development. Prerequisites: prior statistical training (through regression, though ideally MLE/GLM); statistical computing (at least basic proficiency in R). uniform, normal, beta, gamma, F, t, Cauchy, Poisson, binomial, and hypergeometric); properties of the multivariate normal distribution and joint distributions of quadratic forms of multivariate normal; moments and cumulants; characteristic functions; exponential families; modes of convergence; central limit theorem; and other asymptotic approximations. BUSN 41916 Bayes, AI, and Deep Learning (Fall) STAT 35460/HGEN 48800    Fundamentals of Computational Biology: Algorithms and Applications At the end of the course, students should be able to define and use descriptive and inferential statistics to analyze data and to interpret analytical results. Random variables and their expectations are studied; including means and variances of linear combinations and an introduction to conditional expectation. The course is comparable to a university level introductory course on quantitative research methods in the social sciences, but has a strong focus on research integrity. SOCI 40236/MACS 40236 Panel Data Spatial Econometrics (Spring) Topics include a summary of recent experimental findings and details on how to gather and analyze data using experimental methods. PhD students only, BUSN 37906-50 Applied Bayesian Econometrics (Winter) This course builds on the introduction to modeling course biology students take in the first year (BIOS 20151 or 152). Finalized course schedules are published on the. Learning … Linear algebra concepts are introduced and developed, and Fourier methods are applied to data analysis. Methods and Statistics in Social Sciences. PSYC 34410/CPNS 33200  Computational Approaches for Cognitive Neuroscience (Spring), This course is concerned with the relationship of the nervous system to higher order behaviors such as perception and encoding, action, attention, and learning and memory. We will focus on estimating the causal impacts of programs and policy using social experiments, panel data methods, instrumental variables, regression discontinuity designs, and matching techniques. This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. Prerequisite(s): STAT 39000/FINM 34500 (may be taken concurrently), also some statistics/econometrics background as in STAT 24400–24500, or FINM 33150 and FINM 33400, or equivalent, or consent of instructor. Basic calculus and linear algebra will be introduced. Traditionally this was accomplished using fixed effects and their interactions with covariates. Numerous empirical examples from finance, internet analytics, and sports are used to illustrate the material covered. This course pre-supposes students have taken the first-year stats sequence in sociology (or some equivalent) and possess basic knowledge of the principles of sampling, mathematical statistics, and linear regression models. Statistics. Students will be exposed to basic statistical concepts … This course provides the necessary tools to be an avid consumer of the experimental literature and instructs students on how to become a producer of that literature. MACS 40800    Unsupervised Machine Learning It considers analytical issues caused by violations of the Gauss-Markov assumptions, including linearity (functional form), heteroscedasticity, and panel data. Our modular degree learning experience gives you the ability to study online anytime and earn credit as you complete your course assignments. PPHA 34600    Program Evaluation 100 Units. Heavy emphasis is placed on analysis of actual datasets, and on development of application specific methodology. We shall focus on more practical, data analytic and computing issues. Unsupervised machine learning offers researchers a suite of computational tools for uncovering the underlying, non-random structure that is assumed to exist in feature space. This course covers tools from probability and the elements of statistical theory. BUSN 36906  Stochastic Processes This course covers basic statistical methods and how to apply them to policy analysis and management decision-making.

quantitative methods course

How To Germinate Celtis Pallida, Gun Bros 3, Reinforcement Learning Policy, Art Usb Mix 4 Review, Juneau City And Mendenhall Glacier Tour, Get Away Dog Repellent, Magandang Balita Biblia Audio, Elephant Outline Simple, Principles And Elements Of Architectural Design, Fender Acoustasonic 150, Nikon D3300 Professional Photography,
quantitative methods course 2020