In the fascinating realm of NSG 817/NUDN8147 Applied Biostatistics, discover a course designed to equip you with the skills to navigate the intricate world of biological data. This isn’t just about numbers; it’s about extracting meaningful insights from the very essence of life itself.
Why Biostatistics Matters
Biological research generates mountains of data, from gene expression levels to disease incidence rates. But raw data is like a pile of unmined gems – its true value lies in its interpretation. This is where biostatistics comes in, armed with powerful statistical tools and techniques, transforming data into knowledge. It illuminates patterns, relationships, and trends that would otherwise remain hidden. Get ready to explore the world where numbers reveal the secrets of life!
Courses
BIOS 704 Principles of Statistics in Public Health
This beginner’s course covers the basics of statistical reasoning and how statistical principles form the scientific foundation for public health research and practice. To enroll, you’ll need permission from the instructor.
BIOS 714 Fundamentals of Biostatistics I
This is the first part of a two-semester introduction to statistics. It helps you grasp how to use statistical methods correctly in scientific research, especially in public health. The focus is on basic statistical inference principles, particularly for one or two samples of continuous and categorical data. This course is essential for biostatistics and requires either calculus knowledge or permission from the instructor.
BIOS 715 Introduction to Data Management using RedCap and SAS
In this course, you’ll learn how to use Redcap and SAS for handling data. We’ll go over collecting and managing data with Redcap. Plus, you’ll discover how to clean and prepare data for analysis using SAS.
BIOS 717 Fundamentals of Biostatistics II
In this second-level statistics course, you’ll dive into more advanced statistical methods for scientific research, with a focus on applying them to public health practice, public health research, and clinical research. We’ll especially emphasize using regression methods and applying them with computer tools.
BIOS 720 Analysis of Variance
In this course, we’ll focus on methods for planned experiments. We’ll cover things like one-way analysis of variance (ANOVA), two-way ANOVA, repeated measures ANOVA, and analysis of covariance. We’ll also talk about what happens after ANOVA, including post-ANOVA tests, power, and the assumptions needed for ANOVA. Additionally, we’ll touch on finding outliers using robust estimators.
BIOS 725 Applied Nonparametric Statistics
In this course, you’ll explore nonparametric methods for various situations. You’ll cover topics like how these methods give exact p-values for tests, precise coverage probabilities for confidence intervals, accurate experiment error rates for multiple comparison procedures, and exact coverage probabilities for confidence bands. To do this, we’ll use EXCEL and SAS to carry out different procedures.
BIOS 730 Applied Linear Regression
This course covers different types of regression models, like simple linear regression, multiple regression, logistic regression, and nonlinear regression. We’ll also explore concepts such as neural networks, autocorrelation, interactions, and residual diagnostics.
BIOS 735 Categorical Data and Survival Analysis
In this intermediate-level statistics course, you’ll learn more advanced statistical methods for scientific research. We’ll focus on applying these methods to clinical research, public health practice, public health research, and epidemiology.
BIOS 740 Applied Multivariate Methods
This advanced statistics course is for students who already know the basics of biostatistics and linear regression. We’ll cover topics like Hotelling’s T-squared test, MANOVA, principal components, factor analysis, discriminant analysis, canonical analysis, and cluster analysis. If time permits, we might delve into more advanced topics like Multidimensional Scaling or Structural Equation Modeling. Throughout the course, we’ll heavily use computers, so it’s recommended that students have some familiarity with statistical software before joining.
BIOS 799 Introduction to Statistical Genomics
This course gives a broad introduction to statistical and bioinformatics methods used in studying biological systems. It covers the analysis of DNA, RNA, and DNA methylation data from both microarray and next-generation sequencing (NGS) technologies. In the final week of the summer semester, students will join a group seminar to present the results of their assigned genomics analysis projects.
BIOS 805 Professionalism, Ethics and Leadership in the Statistical Sciences
This online course focuses on professionalism, leadership, and ethics tailored for students training to be statisticians, biostatisticians, and data scientists. We’ll cover using reliable statistical methods, addressing challenges to valid inference, and communicating effectively with experts in specific fields. The course also delves into topics like transparency, reproducible research, the publishing process, conflict of interest, data security, and the qualities of effective leaders. To enroll, you’ll need department consent.
BIOS 806 Special Topics in Biostatistics
In this course, you can delve into special topics not regularly covered in the curriculum. To join, you’ll need permission from the instructor.
BIOS 810 Clinical Trials
This course covers the planning, execution, analysis, and evaluation of controlled clinical trials. We’ll focus on fundamental biostatistical concepts and models. We’ll also discuss current concerns in trial research. To enroll, you’ll need permission from the instructor.
BIOS 811 Scientific Rigor and Reproducibility
This course teaches the principles and methods needed to carry out thorough and repeatable research across different stages of translation. The National Institutes of Health (NIH) emphasizes rigor and reproducibility in their guidelines for grant applicants, which grant reviewers must assess. NIH also mandates formal training in scientific rigor and transparency for those backed by institutional training grants, career development awards, and fellowships. In this course, students discover best practices, such as effective study planning, accounting for all pertinent biomedical factors, proper data management, statistical considerations, and transparent reporting of research results.
BIOS 815 Introduction to Bioinformatics
This course explores bioinformatics, a field that combines biology, computer science, and statistics to deepen our understanding of various biological areas. Aimed at students in quantitative sciences, it covers foundational concepts in molecular biology, biological databases, sequence alignment, BLAST, molecular phylogenetics, genomics, transcriptomics, proteomics, and microbiomics. Throughout the semester, you’ll become familiar with key concepts, theories, and tools in bioinformatics. While there are no formal prerequisites, having some background in probability, statistics, and molecular biology at the graduate level is beneficial but not required.
BIOS 820 SAS Programming I
This is a graduate-level course designed to help students prepare for the SAS base programming certification exam. We’ll cover all the topics needed to pass the SAS base programming certification exam offered by SAS. Topics include referencing files, setting options, creating list reports, understanding data step processing, managing variables, reading and combining SAS data sets, using do loops, and arrays, and reading raw data from files. By the end of the course, students should be able to create SAS programs to read data from external files, manipulate the data, and generate basic reports. Permission from the instructor is required to enroll.
BIOS 821 SAS Programming II
This graduate-level course is designed to help students prepare for the SAS advanced programming certification exam. We’ll cover the topics necessary to pass the SAS advanced programming certification exam provided by SAS. The course will focus on array processing, using data step views, writing SAS programs with the data step, efficient utilization of the sort procedure, an introduction to the macro language in SAS, and accessing data using SAS PROC SQL. By the end of the course, students should be able to create SAS programs to read data from external files, manipulate the data for analysis, and generate basic reports.
BIOS 823 Introduction to Programming and Applied Statistics in R
This course offers students a chance to delve into advanced statistical programming. When we create new statistical or computational methods, we often need programming codes to make them work. Currently, much of this development happens in the R (or S-Plus) language. Especially in statistical genetics, recent advancements heavily rely on the R programming language and environment. This course introduces programming in R and its applications to practical statistical problems. To enroll, you should have some prior exposure to computer programming and a basic understanding of statistics at the Applied Regression or Applied Design level.
BIOS 825 Nonparametric Methods
In this course, you’ll dive into nonparametric statistical methods designed for data that doesn’t follow the typical normal or other distribution patterns. We’ll explore popular nonparametric methods for various situations, including single samples, two independent or related samples, three or more independent or related samples, goodness-of-fit tests, and measures of association. You’ll also touch on power and sample size considerations. The course covers the theoretical foundation of these methods at an intermediate mathematical level and demonstrates their real-world applications using statistical software with actual data. Prerequisite: Permission of instructor.
BIOS 830 Experimental Design
This course focuses on teaching the fundamentals of experimental design and how to correctly use and understand statistical analysis of variance techniques.
BIOS 833 Measurement for Statisticians
In this course, you’ll explore measurement and psychometrics for students in the statistical sciences. The aim is for you to grasp concepts like measurement theory, classical/modern test theory, reliability and validity, factor analysis, structural equation modeling, item response theory, and differential item functioning.
BIOS 835 Categorical Data Analysis
This course helps you grasp the mathematical theory and practical applications for analyzing data with response measures that are ordinal or nominal categorical variables. You’ll cover univariate analysis, contingency tables, and generalized linear models for these types of response measures. You’ll also learn regression techniques like logistic regression and Poisson regression, which involve both categorical and/or continuous explanatory variables, with and without interaction effects.
BIOS 840 Linear Regression
In this course, you’ll get started with building models using regression techniques. We’ll explore various essential topics in Linear Regression, such as simple linear regression, multiple regression, model selection, and validation, as well as diagnostics and remedial measures.
BIOS 845 Survival Analysis
This course helps you grasp the mathematical theory and practical applications for analyzing time-to-event data with censoring. We’ll cover univariate analysis, group comparisons, and regression techniques for survival analysis. You’ll learn about parametric and semi-parametric regression techniques, which include both categorical and/or continuous explanatory variables, with and without interaction effects.
BIOS 850 Multivariate Statistics
In this course, we’ll explore the theory and methods of applied multivariate analysis, with a focus on techniques commonly used in biostatistics. You’ll need some understanding of basic matrix algebra, but we’ll review it as we go along. You’ll be given theoretical exercises and real-world data sets to analyze. The main emphasis will be on applying these methods in biostatistical scenarios.
BIOS 855 Statistical Methods in Genomics Research
In this overview course, we’ll give you a broad introduction to statistical and bioinformatics methods used in studying biological systems. We’ll focus on analyzing DNA, RNA, and DNA methylation data from both microarray and next-generation sequencing (NGS) technologies. Toward the end of the summer semester, you’ll join a group seminar where you’ll present the results of your assigned genomics projects.
BIOS 860 Clinical Trial Design and Analysis
This course is for students keen on the statistical side of clinical trial research. We’ll cover everything about designing and analyzing clinical trials, including first-in-human studies (focusing on dose-finding, safety, and proof of concept in Phase I), Phase II, Phase III, and Phase IV studies.
BIOS 871 Mathematical Statistics
In this course, you’ll learn the basics of probability theory, including random variables, distribution and density functions, expectations, transformations of random variables, moment-generating functions, convergence concepts, sampling distributions, and order statistics.
BIOS 872 Mathematical Statistics II
In this course, you’ll dive into the basics of statistical estimation and hypothesis testing. We’ll cover point and interval estimation, likelihood and sufficiency principles, properties of estimators, loss functions, Bayesian analysis, and asymptotic convergence.
BIOS 880 Data Mining and Analytics
In this course, students will learn the typical steps involved in data mining, including accessing and examining prepared data, discovering patterns, and using predictive modeling techniques such as decision trees, regression, and neural networks. We’ll also cover methods for assessing the effectiveness of models.
BIOS 898 Collaborative Research Experience
In this course, students gain hands-on experience in collaborative research under the guidance of an experienced researcher. Throughout a semester, students work closely with an investigator or faculty member, making independent contributions to an ongoing research project.
BIOS 899 MSCR Thesis
In this course, students will prepare a formal thesis based on their research for the MS in Clinical Research. The thesis is directed by a faculty member in the Department of Biostatistics. After completing the thesis, students will undergo an oral examination covering the research methods and content. Approval from the Department of Biostatistics is required for enrollment.
BIOS 900 Linear Models
In this course, you’ll learn about the theory and methods of linear models for data analysis. We’ll cover general linear models, including regression models, experimental design models, and variance component models. The topics include least squares estimation, the Gauss-Markov theorem, and hypotheses with less than full rank.
BIOS 902 Bayesian Statistics
In this course, you’ll be introduced to Bayesian theory and methods for data analysis. We’ll cover the basics of the Bayesian approach to statistical inference, how Bayesian procedures perform, computational issues specific to Bayesian methods, model criticism, and model selection. The course incorporates case studies from various fields, and we’ll emphasize implementing models using Markov chain Monte Carlo methods.
BIOS 905 Theory of Statistical Inference
This course delves into advanced aspects of statistical inference. It’s designed to prepare Ph.D. BIOS students for the comprehensive exam, focusing on advanced biostatistical concepts and problem-solving techniques.
BIOS 906 Advanced Special Topics in Biostatistics
In this course, you have the opportunity to explore special topics not usually covered in the Biostatistics PhD curriculum. To enroll, you’ll need to have passed the PhD Qualifying exam and obtain permission from the instructor.
BIOS 908 Advanced Clinical Trials
In this course, you’ll get an introduction to the latest innovations in clinical trial designs and analysis methods. We’ll cover concepts like controls, blinding, and randomization, along with various trial designs based on the phase of clinical development. You’ll learn about sample size calculations, interim analysis, and adaptive clinical trials. In the first half of the course, we’ll explore traditional frequentist and likelihood approaches to trial design and analysis, and in the second half, the focus will shift to the Bayesian approach, including adaptive clinical trial designs.
BIOS 910 Generalized Linear Models
This course about Generalized Linear Models (GLM) is meant for both practical and theoretical statisticians. We’ll cover the foundational theories and important applications of generalized linear models.
BIOS 911 Nonlinear Models
In this course, we’ll explore both the theory and practical applications of nonlinear models, focusing on their relevance in biological, medical, and pharmaceutical research. We’ll discuss applications to dose-response studies, bioassay studies, and clinical pharmacokinetics and pharmacodynamics studies. The course will also delve into nonlinear mixed effects models and criteria for optimal experimental designs based on nonlinear models. While covering the theoretical foundations at an intermediate mathematical level, we’ll also showcase real-world applications using statistical software with actual data.
BIOS 915 Longitudinal Data Analysis
In a longitudinal study, researchers repeatedly observe the same individuals and events over an extended period. It’s usually an observational study but might have design components. In medical contexts, these studies and related models help track the development of a disease or treatment over time, often involving follow-up for progress and potential side effects. Since the study follows the same individuals through different time points, statistical methods use random effects or “mixed models” with various correlation structures. Generalized estimating equations and marginal model approaches are common, and Bayesian methods may also be applicable. By the end of this course, students will be equipped to design and analyze longitudinal studies, with the SAS computer package being employed.
BIOS 920 Latent Variable Analysis
Latent variables are random variables with values that can’t be directly observed or measured without error. In this course, we’ll explore a set of statistical models involving latent variables and their applications in biomedical and public health research. Designed as an elective for Biostatistics graduate program students, we’ll utilize statistical packages like M-plus, R, and/or SAS throughout the course.
BIOS 999 Doctoral Dissertation
This course involves creating a doctoral dissertation based on original research, partially fulfilling the Ph.D. degree requirements. Credits will be granted once the dissertation is accepted by the student’s dissertation committee. To enroll, you need to have completed the Department of Biostatistics Ph.D. Comprehensive Exam and obtain consent from your advisor.
Courses
DATA 806 Special Topics in Data Science
In this course, you have the opportunity to delve into special topics that aren’t regularly covered in the Applied Statistics & Analytics and Data Science curriculum. To enroll, you’ll need permission from the instructor.
DATA 817 Introduction to Tableau
In the Tableau Desktop-I specialization, you’ll learn about data visualization and how to effectively represent and understand information in a dataset using Tableau. This course explores the fundamental concepts of data visualization, delves into the Tableau Desktop interface, and guides you through applying various Tableau tools. By the end, you’ll be able to import and prepare data in Tableau and understand the connection between data analytics and data visualization. Whether you’re new to Tableau, need a refresher, or want to explore it further, this course is for you. No prior technical or analytical background is needed. The course takes you step-by-step to create visualization dashboards and stories, setting the foundation for Desktop-I certification. There are no formal prerequisites, but prior experience with plots, tables, graphs, etc., is beneficial.
DATA 819 Introduction to Python
This one-credit hour introductory course is designed to teach Python programming basics. Throughout the course, you’ll learn about Python syntax, types, data structures, control flow, functions, modules, and packages, as well as reading and writing files. Additionally, basic statistics in Python will be covered.
DATA 822 Introduction to SQL
This course readies students to work with various Structured Query Language (SQL) dialects. By the end of the course, you’ll be equipped to engage with major databases like PostgreSQL, MySQL, Oracle, and more. The topics include relational databases, data structure, Data Definition Language (DDL), Data Manipulation Language (DML), table joins, data summarization, and the ability to write and interpret SQL queries.
DATA 824 Data Visualization and Acquisition
Becoming a data scientist requires a well-rounded skill set covering statistics, machine learning, and computer programming. A crucial component is a strong foundation in data visualization principles to effectively convey messages through presentations. Put simply, data visualization helps individuals understand the significance of data by presenting it visually. This course introduces students to the principles of effective data visualization and the commonly used tools for implementation. Topics include techniques for visualizing different data types, the use of space and color for data encoding, interactive visualizations, obtaining and visualizing data from public repositories, and data cleaning and standardizing. The focus will be on breadth rather than depth, emphasizing the integration and synthesis of concepts for problem-solving.
DATA 881 Statistical Learning I
Statistical learning is a crucial skill for data scientists, who excel in handling vast amounts of big data. Statistical learning techniques are essential tools in their toolkit. This course hones in on applying statistical learning to address big data challenges using data mining and predictive modeling methods, which are highly sought after. Students will get acquainted with the fundamentals of statistical/machine learning, covering supervised learning (like linear models, nonlinear models, penalized methods, ensemble methods, etc.), unsupervised learning (including K-means clustering, nearest neighbors, hierarchical clustering, etc.), and addressing missing data in machine learning. Throughout the course, the focus is on becoming “informed doers” – individuals who not only apply methods but also understand how these methods work. This understanding is crucial for achieving reliable results with big data, ensuring a proper grasp of the limitations of specific methods.
DATA 882 Statistical Learning II
Understanding when and how to apply advanced statistical learning models to big data is a key asset for a data scientist in a research team. In Statistical Learning 2, we’ll focus on becoming “informed doers” by learning not only how many covered methods work but also when to apply them appropriately. This is particularly crucial as these methods come into play when simpler approaches are inadequate and often require significant adjustments to work effectively. Data scientists mastering these methods can tackle complex questions beyond the scope of the more general “workhorse” methods covered in the first unit, Statistical Learning 1. The course delves into various important techniques used today, including mixture models, hidden Markov models, spline regression, support vector machines, advanced discriminant analysis methods, neural networks (including deep learning), and handling complex computations, such as with Hadoop. The course concludes with a brief project that ties together all the learned skills, showcasing their application in statistical decision support—a common task for data scientists.
Courses
STAT 655 Foundations of Mathematics for Data Science
In this course, we’ll explore topics in single- and multiple-variable differential and integral calculus, along with linear algebra, with practical applications in statistics and data science. The mathematical concepts covered include limits, derivatives, integrals, sequences, series, vectors, matrices, and optimization problems, all within the context of statistical applications. The prerequisite for this course is college algebra or an equivalent background.
STAT 805 Professionalism, Ethics and Leadership in the Statistical Sciences
In this online course, we’ll delve into aspects of professionalism, leadership, and ethics tailored for students aspiring to be statisticians, biostatisticians, and data scientists. Topics cover the use of robust statistical methodology, challenges to valid inference, effective communication and collaboration with subject-area experts, maintaining transparency and independence, reproducible research practices, the publishing process (including authorship guidelines, plagiarism, peer review, intellectual property, etc.), addressing conflicts of interest, data security, and the qualities of effective leaders, among other relevant subjects. To enroll, you’ll need permission from the instructor.
STAT 806 Special Topics in Applied Statistics and Analytics
In this course, you have the opportunity to explore special topics that aren’t typically covered in the Applied Statistics & Analytics curriculum.
STAT 818 Introduction to R
In this course, students will have the chance to learn applied statistics using the R statistical programming language.
STAT 820 SAS Programming I
This is a graduate-level course designed to prepare students for the SAS base programming certification exam. We’ll cover the essential topics required for students to successfully pass the SAS base programming certification exam offered by SAS. Throughout the course, we will explore referencing files, setting options, creating list reports, understanding data step processing, managing variables, reading and combining SAS data sets, using do loops, and arrays, and reading raw data from files. By the end of the course, students should be capable of creating SAS programs to read data from external files, manipulate the data into variables for analysis, generate basic reports displaying results, and comprehend and explain results from univariate analyses using proc univariate.
STAT 821 SAS Programming II
This graduate-level course is designed to prepare students for the SAS advanced programming certification exam. We’ll cover the necessary topics for students to successfully pass the SAS advanced programming certification exam provided by SAS. Throughout the course, we will delve into array processing, using data step views, writing SAS programs using the data step, optimizing the sort procedure, introducing the macro language in SAS, and accessing data using SAS PROC SQL. By the end of the course, students should be proficient in creating SAS programs to read data from external files, manipulating data into variables for analysis, and generating basic reports displaying the results.
STAT 823 Introduction to Programming and Applied Statistics in R
In this course, students will have the chance to explore advanced statistical programming. The creation of new statistical or computational methods often involves developing programming codes to implement them. Currently, much of this development occurs in the R (or S-Plus) language. Notably, recent advancements in statistical genetics rely on the R programming language and environment. This course serves as an introduction to programming in the R language and its applications to applied statistical problems. Prerequisites include some prior exposure to computer programming, basic statistics at the Applied Regression or Applied Design level, and permission from the instructor.
STAT 825 Nonparametric Methods
This course introduces nonparametric statistical methods for data that don’t meet normality or other typical distributional assumptions. We’ll explore popular nonparametric methods for various scenarios, including a single sample, two independent or related samples, three or more independent or related samples, goodness-of-fit tests, and measures of association. Additionally, we’ll cover topics related to power and sample size. The course presents the theoretical foundations of these methods at an intermediate mathematical level and includes practical applications using real-world data and statistical software.
STAT 830 Experimental Design
This course focuses on mastering the fundamentals of experimental design and the proper application and interpretation of statistical analysis of variance techniques.
STAT 833 Measurement for Statisticians
This course aims to introduce students in the statistical sciences to the theory and applications of measurement and psychometrics. The objective is for students to grasp concepts such as measurement theory, classical and modern test theory, reliability and validity, factor analysis, structural equation modeling, item response theory, and differential item functioning.
STAT 835 Categorical Data Analysis
This course offers insight into both the mathematical theory and practical applications for analyzing data with response measures that are ordinal or nominal categorical variables. Topics covered include univariate analysis, contingency tables, and generalized linear models tailored for categorical response measures. Regression techniques for categorical response variables, like logistic regression and Poisson regression methods, will encompass both categorical and/or continuous explanatory variables, with and without interaction effects.
STAT 840 Linear Regression
This course serves as an introduction to building models using regression techniques. It will cover key topics in linear regression, including simple linear regression, multiple linear regression, model selection and validation, diagnostics, and remedial measures.
STAT 845 Survival Analysis
This course offers insight into the mathematical theory and practical applications for analyzing time-to-event data with censoring. It covers univariate analysis, group comparisons, and regression techniques specific to survival analysis. The course includes parametric and semi-parametric regression techniques, addressing both categorical and/or continuous explanatory variables, with and without interaction effects.
STAT 850 Multivariate Statistics
This course provides an introduction to the theory and methods of applied multivariate analysis. Topics covered include multivariate model formulation, multivariate normal distribution, Hotelling’s T-square, multivariate analysis of variance, repeated measures analysis of variance, growth curves, discriminant analysis, classification analysis, principal components analysis, and cluster analysis.
STAT 855 Statistical Methods in Genomics Research
This survey course offers a high-level introduction to various statistical and bioinformatics methods used in studying biological systems. Specifically, it provides an overview of the analytical aspects involved in studying DNA, RNA, and DNA methylation data measured from both microarray and next-generation sequencing (NGS) technologies. The course is structured in a block format, featuring 4 hours of lectures daily for two weeks (one week in June and one week in July). Readings and homework assignments are distributed throughout the summer semester. In the final week, students participate in a group seminar session, presenting results from their assigned genomics projects.
STAT 871 Mathematical Statistics
This course provides an introduction to the basics of probability theory, covering topics such as random variables, distribution, and density functions, expectations, transformations of random variables, moment-generating functions, convergence concepts, sampling distributions, and order statistics.
STAT 872 Mathematical Statistics II
In this course, you will delve into the basics of statistical estimation and hypothesis testing. Key topics include point and interval estimation, likelihood and sufficiency principles, properties of estimators, loss functions, Bayesian analysis, and asymptotic convergence. The aim is to provide a solid foundation in these fundamental statistical concepts.
STAT 880 Data Mining and Analytics
In this course, students will explore the essential steps in data mining, covering tasks like evaluating and analyzing prepared data, discovering patterns, and implementing predictive modeling using techniques such as decision trees, regression, and neural networks. Additionally, the course will delve into methods for assessing and evaluating the performance of models.
