Bioinformatics and Data Science

In this workshop, learners will learn the basics of how to use R to wrangle data, create visualizations, and conduct exploratory analyses. The workshop will use the popular “tidyverse” suite of packages and will also teach learners the concept of tidy data and how it facilitates analysis.

This workshop builds upon the principles of using R for data science by introducing intermediate concepts that will help learners advance their knowledge and use R for more complex tasks. These tasks include working with APIs and packages to access data on remote servers, iterating tasks over datasets, and writing custom functions.

This workshop will explore both the design side and the coding side of creating visualizations in R. The first session will introduce best practices for designing effective visualizations, and learners will put these into practice in the next two sessions to create static and interactive visualizations. Learners will be introduced to Shiny, an R package used to build interactive web apps.

In this introductory workshop, users will learn how to create, read, transform, and visualize data for scientific analysis. Learners will also gain skills in applying basic computer logic to scientific applications.

This workshop will provide an introduction to working with text data in R and explore various approaches to analyzing text data. The first session will cover principles for wrangling text data as well as some basic text mining applications. The subsequent two sessions will delve into specific techniques to enable automated analysis of text data.

R is a free, cross-platform – Windows, Mac, and Linux – programming language, designed specifically to facilitate data management, analysis, and visualization. Boasting vibrant development and support communities, R has become an indispensable tool for bioinformaticians, statisticians, and data scientists. Created with true beginRs in mind, this training will teach participants the fundamental, transferable skills needed to unleash R’s full potential for producing publication-worthy analyses and visualizations.

This workshop will introduce the basics of next generation sequencing (NGS) data analysis. We will introduce NGS methods and platforms, bioinformatics tools and standards, and data analysis workflows, specifically for DNA-seq (variant analysis) and RNA-seq (transcriptome analysis). The lectures will introduce common software libraries and algorithms used in the bioinformatics community. During the hands-on training sessions, we will perform alignment and analysis of NGS data from scratch using standard software libraries and pipelines.

Bulk transcriptomes have provided considerable insights and fostered the discovery and characterization of GRN (Gene Regulatory Network)s. However, bulk transcriptomes provide population-based averaged measurements which blur cell heterogeneity and developmental dynamics of asynchronous cell populations. Single-cell transcriptome technologies (scRNA-seq) capture cell heterogeneity and thus are useful for the discovery of cell populations, identification of cell mutants, and quantification of subpopulations. Leveraging on the ability of generating thousands of individual measurements from scRNA-seq, lots of methods have been developed to capture spatial or temporal information from cell populations. This workshop will introduce basic steps of the scRNA-seq processing, starting from fastq coming off 10x sequencer to spatial and temporal analysis using popular R packages. Then, we will discuss case studies that can be addressed using scRNA-seq.

In the past decade, neural networks have become a valuable tool for data scientists, revolutionizing fields such as text processing, image analysis, genomic/proteomic data analysis, data clustering, and much more. However, these algorithms can be very difficult to understand, interpret, and program. This workshop will first cover the theory and proper applications of various neural networks (multilayer perceptrons, convolutional neural networks, long-short term memory models, autoencoders, etc.). From there, powerful deep learning packages, such as Pytorch and Keras, will be introduced. Proper coding techniques will be shown through examples and practiced through exercises that will be completed in the Python 3 programming language. Finally, concepts in data visualization and software engineering will be discussed, helping researchers use neural networks in an effective and reproducible way to improve the impact of projects with a computational component.

Artificial intelligence (AI) in biomedical research has grown exponentially in the past decade. AI can be used to uncover powerful new insights in data that your lab is already collecting. This workshop has two primary components. First, participants will engage in discussions that cover recent advances in artificial intelligence (AI) and how these developments can be used in biomedical research. Topics will include active learning, adversarial learning, Bayesian deep learning, reinforcement learning, semi-supervised learning, self-supervised learning, and transfer learning. These topics will be covered in an integrated manner: the discussions will explore how different facets of AI can interact with each other to generate high-quality results. Second, participants will work with the instructor to design and implement AI project(s). These projects will have direct relevance to the research being done by each participant. This workshop will have 1 day of discussion followed by 1 week of offline work where the participants communicate directly with the instructors about project development.

Metagenomics is gaining importance due to low-cost next generation sequencing technologies. This workshop introduces end-to-end solutions for analyzing metagenomic data, including data-quality analysis, alignment, community profiling, taxonomic comparison, and novel taxa discovery.

R is the industry standard for creating specific graphs and plots. This workshop walks participants through creating interactive, static, and shareable plots using popular R packages. The workshop will cover formatting data, loading data, setting parameters, creating images, and saving outputs.

Predicting the effect of a mutation on the structure and function of a protein is not just for researchers with computer facilities.  Users with basic molecular biology background can set up and run intensive computational modeling and dynamics experiments.  In this workshop, participants will use open-source tools and techniques to conduct molecular modeling and dynamics experiments.

Bioinformatics (Computational Biology) is a must skill required in every modern biomedical research lab. Installing and configuring a wide variety of computational biology tools is a cumbersome task that requires software engineering skills.

This workshop provides an introduction to basic concepts in using popular tools and techniques for sequence analysis, structure analysis, function prediction, biological database searching, “omics” data analysis, pathway analysis, data visualization, data curation and integration, and scripting basics.

In order for a drug to get approved by the FDA for market in the USA, the sponsor must ultimately demonstrate the drug has: 1) a predictable exposure profile with dose; 2) a good safety profile; and 3) is effective at safe doses. Therefore, the pharmacology of a drug is essentially being reviewed by the FDA. The ability of scientists to analyze drug exposure/response relationships is crucial to understanding what exposure amount will elicit the safest, most effective response, and ultimately what dose amount and frequency will produce the optimal exposure amount. Additionally, the ability to identify sub-populations that may produce differing exposure or response levels is key to providing as many subjects as possible a safe and effective dose. This quantitative exposure/response analyses, often referred to pharmacometrics, is key to making go/no go decisions both during clinical trials by investigators and by the FDA during the subsequent review period. Participants will learn basic pharmacology theory with introductory statistics using a popular open-source software program (R Studio) that is capable of conducting pharmacokinetic (PK) exposure and pharmacodynamic (PD) response analyses from example clinical trial data. Ultimately, the framework of analyzing exposure/response relationships will be demonstrated in order to make go/no go decisions. This workshop is designed for researchers and clinicians interested in learning how to utilize freely available software to explore, visualize, and understand drug exposure/response relationships where responses include any clinical endpoint collected on a trial, or for researchers and clinicians interested in understanding and predicting the effect of different doses on drug exposure as well as the effect of exposure on a variety of clinically relevant response endpoints (biomarkers), or for medical, pharmacy, dental, nursing, and lab-based graduate-school students interesting in obtaining a deeper understanding of pharmacokinetics, exposure/response analyses, as well as a broad understanding of clinical drug development and the impact of pharmacometrics on decisions.

Scientists generate more data than ever before. It can be daunting to determine how to extract insights from a mountain of data. Data science is a relatively new discipline that combines traditional statistics and analytics with programming to produce novel insights, intelligently automated processes, and data-driven decisions.

This workshop will equip you with everything you need to complete a basic data science project using Python from beginning to end. Participants will be exposed to a practical, real-world use case, which will be built on throughout the workshop. Students will use the data from this use case to perform exploratory analysis and build their skills up to advanced analytics.

This course will introduce participants to a comprehensive set of text mining related topics, tools and techniques. It will cover three primary components: (1) basics of Python and its related packages, (2) an overview of text mining pipeline and techniques, and (3) an introduction to machine learning and development of text mining applications using machine learning. Each component will have hands-on exercises and case studies for practice.

Members of the QIIME development group will lead this hands-on workshop on bioinformatics tools for microbial ecology. The workshop will include lectures covering basic QIIME usage and theory, and hands-on work with QIIME, to perform microbiome analysis from raw sequence data through publication-quality statistics and visualizations. The workshop will also cover related bioinformatics tools including DADA2,Emperor, scikit-bio, and an introduction to applied bioinformatics. This workshop will provide the foundation on which participants can begin using these tools to advance their own studies of microbiome analysis or microbial ecology. This is a hands-on workshop. Participants must bring their laptop. This workshop will be on QIIME2 platform only.

MATLAB® is a programming platform designed for scientists and engineers. This workshop provides a comprehensive introduction to the MATLAB® technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the workshop.

This workshop provides hands-on experience with performing image analysis. Examples and exercises demonstrate the use of appropriate MATLAB® and Image Processing Toolbox™ functionality throughout the analysis process. The workshop also provides hands-on experience with performing computer vision tasks. Examples and exercises demonstrate the use of appropriate MATLAB® and Computer Vision System Toolbox™ functionality.

MATLAB is a high-level language that enables you to quickly perform computation and visualization through easy-to-use programming constructs.

This two-day workshop focuses on data analytics and machine learning techniques in MATLAB® using functionality within Statistics and Machine Learning Toolbox™ and Deep Learning Toolbox™. The workshop demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results. Topics include:

• Organizing and preprocessing data
• Clustering data
• Creating classification and regression models
• Interpreting and evaluating models
• Simplifying data sets
• Using ensembles to improve model performance

As big data becomes the norm and experiments continue to increase in scale, proper understanding and use of statistics is becoming increasingly important for scientists in every field. While experimental researchers are expert in concepts related to their respective fields and receive extensive scientific education, statistical training is relatively lacking. As a result, experimental researchers may feel overwhelmed or uncertain about how to correctly use statistics to quantify their experimental results and how to properly interpret the results of those statistical tests. Unfortunately, this knowledge gap can result in both reduced understanding of reported results in scientific publications as well as superficial or potentially inaccurate reported statistics. This workshop serves as a practical, hands-on workshop to close the knowledge gap and help experimental researchers learn how to choose a statistical test for their data, how to perform those tests, and how to interpret the results. The workshop starts by establishing a solid foundation in basic statistical theory before advancing to practical applications of statistical tests on real data.

R is a popular platform to perform statistical analysis. This workshop introduces how to perform basic statistical analysis using the R platform. First, participants will learn to use the R and RStudio software platforms. Followed by an introduction to basic statistical concepts, participants will do hands-on exercises to perform basic statistical analysis using the R platform.

In this interactive workshop, participants will first learn the theory underlying analyses, then watch demonstrations of running analyses in R, and finally practice running analyses themselves.

This course will teach students key programming skills needed for success in biomedical research or other coding applications. The assignments will be done in Python, which is one of the most popular and widely-used programming languages (redmonk.com/sogrady/2020/02/28/language-rankings-1-20/), but the concepts learned in the course will be transferrable to many other languages, allowing students to more easily transition to another language if they so desire. The assignments will also encourage students to think about scientific applications for programming

Python is a free, open-source and powerful programming language that is easy to learn. This course is intended for non- programmers who want to learn how to write programs that expand the breadth and depth of their daily research. Most elementary concepts in modern software engineering will be covered, including basic syntax, reading from and writing texts files, debugging python programs, regular expressions, and creating reusable code modules that are distributable to peers. The course will also focus on potential applications of Python to bioinformatics, including sequence analysis, data visualization and data analysis. Students will also learn to use the Jupyter Notebook and the PyCharm integrated development environment (IDE), which are available at no cost.

INDIVIDUAL LAPTOP IS NEEDED FOR EACH CLASS.
Continuum Analytics Installer Anaconda (V3) will be utilized to install Python and the necessary packages.

The goal of this course is to introduce biomedical research scientists to R as an analysis platform rather than a programming language. Throughout the course, emphasis will be placed on example-driven learning. Topics to be covered include: installation of R and R packages; command line R; R data types; loading data in R; manipulating data; exploring data through visualization; statistical tests; correcting for multiple comparisons; building models; and, generating publication-quality graphics. No prior programming experience is required.

INDIVIDUAL LAPTOP IS NEEDED FOR EACH CLASS.

Between Electronic Medical Records and Electronic Health Records, PubMed, and collections of biomedical grant applications, there exist large quantities of medical information stored in databases waiting to be explored. Besides tables of numbers, medical records also contain a great amount of free-text paragraphs that are comprehensible to human readers but challenging to computers. Text mining is an interdisciplinary area that primarily combines advances in Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML) to help the computers understand human written language and thus extract medical and clinical information from free-text records. This class aims to introduce fundamental subjects in text mining such as tokenization, named entity recognition (NER), grammars, parsing, relation extraction, and document classification. The class is oriented towards hands-on experience with Python and Natural Language Toolkit (NLTK).

Deep learning (DL) is emerging as a major disruptive technology in biomedical and clinical research. It is also a skill with high demand in the decade to come. This course aims to teach the foundations to understand how neural network works and also introduce latest developments. You will build your own neural networks and gain skills to apply deep learning to your field. The course consists of a set of lectures over the 7 weeks. A number of course videos and assignments will be released every week to cover basis and advanced topics in deep learning. The assignments consist of multi-choice and short-answering questions, and coding problems. We will start from basics of neural networks, introduce the loss function, optimization and how to setup and manage the training session. The next section is the convolutional neural network for imaging and vision tasks. We will learn the recurrent neural network (RNN) for the sequence data. More recently, attention mechanism and transformer models (BERT, GPT family etc.) are very popular. They are introduced after RNN. We will teach generative model and in details the GAN (generative adversarial network). The technique to visualize the neural network is introduced to help understand how and why the neural network works. The course will end with a focus on how to handle "small dataset" use case, as in many practical applications, we may not be able to acquire large labelled dataset. Three techniques are introduced, transfer learning, meta learning and contrastive learning (as the more recent development of self-supervised learning).

In this course, students will learn how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. Upon completion of this course, students will be able to apply CNNs to MRI scans to conduct a variety of medical tasks. INDIVIDUAL LAPTOP IS NEEDED FOR EACH CLASS (Mac, Linux or Windows).

This course will demonstrate and practice the use of R in creating and presenting data visualizations. After a short introduction to R tools, especially the tidy verse packages, the course will cover principles for data visualization, examples of good and bad visualizations, and the use of ggplot2 to create static publication-quality graphs. Students will also have the chance to learn about modern web-based interactive graphics using the html widgets packages as well as dynamic graphics and dashboards that can be created using flex dashboard and Shiny. The course will explore ways in which bioinformatics data can be presented using static and dynamic visualizations. Finally, RMarkdown and other packages will be used to develop webpages for presenting data visualizations as self-explanatory and possibly interactive storyboards.

This course will demonstrate and practice the use of Python in creating and presenting data visualizations.

Enormously large series of complex and chaotic events have shaped the genomes of eukaryotes, prokaryotes, and viruses.This course will address cutting-edge approaches to the computational investigation of these events, with an eye toward developments in translational systems biology. The course will begin by presenting the fundamentals of evolutionary genomics, including basic properties of genomes and comparative genomics, population genetics, and sequence-structure-function relationships. Experimental design and biological project integration will be a major theme of the course. Specific lectures on statistical analysis, similarity searches, Next Generation Sequencing, epigenomics, and other specialized topics will supplement those given in the earlier part of the course.

Learning from data in order to make useful predictions or obtain insights is a cornerstone of modern science. The goal of this course is to introduce students to the basic tools and workflows for doing this, with a focus on biological- and health-related data. In this course, students will learn how to use Python-based tools, particularly Numpy, SciKit-learn, Pandas, and Matplotlib.

R is a free statistics software that is becoming increasingly popular and important for data analysis in biology. During the course, students will first learn how to handle the R programming environment. Next, students will learn how to simulate data for analysis, while the background for R programming will be provided in accompanying lectures. At the end of the course, students will become familiar with simple R programming, which they will be then able to apply for their own data analysis.

Machine learning is a computational field that consists of techniques allowing computers to learn from data and make data-driven predictions or decisions. The ability to effectively implement machine learning approaches is a crucial component of data analysis. BIOF 509 provides a comprehensive overview of machine learning concepts, project design, and implementation. The course will give a conceptual overview of the most popular machine learning algorithms with examples of how/when to apply them to datasets. Algorithms that will be covered include: support vector machines, decision trees, random forests, multiple clustering approaches, and deep learning. Best practices in designing machine learning projects will also be emphasized, and this course will introduce strategies to avoid common pitfalls and to accurately interpret results. To reinforce key concepts, this course contains 4 written homework assignments and a research project. Through the homework assignments, students will (i) study theory behind common machine learning algorithms and (ii) explore examples of successful machine learning projects in biomedical research. For the research project, students will use python machine learning packages (Scikit-Learn, Tensorflow, Pytorch) to design a multistep pipeline to analyze a dataset of their choice. Students will also be expected to use Github to demonstrate proper documentation and version control practices when completing the project.

In the past decade, big data has become increasingly prominent in many fields, including healthcare and biomedical research. These increasingly large datasets pose a unique challenge to researchers. In these cases, nuanced approaches to machine learning are often necessary to extract important information. BIOF 510, a continuation of BIOF 509, will cover advanced applications of popular machine learning algorithms, including support vector machines, random forests, and neural networks. Neural network algorithms that will be covered include multi-layer perceptrons, convolutional neural networks, recurrent neural networks, probabilistic neural networks, and autoencoders. To reinforce key concepts, this course contains 4 written homework assignments and a research project. Through the homework assignments, students will (i) study theory behind common machine learning algorithms and (ii) explore examples of successful machine learning projects in biomedical research. For the research project, students will use python machine learning packages (Scikit-Learn, Tensorflow, Pytorch) to design a multistep pipeline to analyze a dataset of their choice. Students will also be expected to use Github to demonstrate proper documentation and version control practices when completing the project.

The objective of this course is to give students an introduction into the theory and practice of a wide range of bioinformatic techniques and applications, enabling them to use these tools in their own research. This course will be divided into five modules: statistical approaches in sequence analysis; phylogenetic analysis of nucleotide and protein sequences; acquisition and analysis of sequence datasets, including EST and RNA-seq data; analysis of genomic datasets from an evolutionary perspective; and, prediction of protein secondary structure. Two or three of the five sessions in each module will be divided roughly 60 percent theoretical lecture and 40 percent learning to use relevant computational tools. The final session of each module will be split between a discussion of computational tools, a journal club, and a discussion of work on a project assigned for each module. By the end of the course, students should be able to acquire many types of sequence data, identify orthologous and paralogous genes, predict domains and motifs, identify alternative splicing, analyze genomic/protein alignments, and make a prediction of secondary protein structure from primary sequence.

The objective of this course is to give students an introduction into the theory and practice of a wide range of bioinformatic techniques and applications, enabling them to use these tools in their own research. This course will be divided into five modules: statistical approaches in sequence analysis; phylogenetic analysis of nucleotide and protein sequences; acquisition and analysis of sequence datasets, including EST and RNA-seq data; analysis of genomic datasets from an evolutionary perspective; and, prediction of protein secondary structure. Two or three of the five sessions in each module will be divided roughly 60 percent theoretical lecture and 40 percent learning to use relevant computational tools. The final session of each module will be split between a discussion of computational tools, a journal club, and a discussion of work on a project assigned for each module. By the end of the course, students should be able to acquire many types of sequence data, identify orthologous and paralogous genes, predict domains and motifs, identify alternative splicing, analyze genomic/protein alignments, and make a prediction of secondary protein structure from primary sequence.

This is the second part of a two-part course. The completion of the first part (prerequisite) is required before taking the second part. Registration is required separately for each part of the course.

In this course, students will learn to analyze data generated by a variety of sequencing techniques (such as DNAseq, RNAseq and CHIP-seq) particularly in relation to biomedical applications (such as analysis of gene expression and identification of medically relevant sequence variation). While recorded lectures and readings will provide necessary background, the course emphasizes hands-on, self-paced lessons featuring real-world data sets to give the learner experience with all major steps of sequencing analyses, from filtering of raw data to creating polished figures. As the course progresses, students will work on a term project in which they design a sequencing project based on their own research interests. To make this course accessible to all students, we will focus on the use of publicly available resources, such as the NCBI SRA and the Galaxy platform, that can be accessed from anywhere.

The gene expression programs that instantiate eukaryotic cell states are complex and dynamic, but ultimately essential to understanding development, homeostasis, real-time environmental adaptation and cellular dysregulation. This course will aim to equip you with a broad range of tools for analyzing gene expression and elucidating the regulatory influences affecting it. By the end, students will have an appreciation for the many layers of expression regulation and a familiarity with common methods for analyzing gene expression and its regulation that will enable interpretation of such results in the literature and the ability to choose the right tool for answering their own gene expression-related research questions in the future.

The human genome reference sequence took decades and vast resources to complete. The human transcriptome reflects how the genome is deployed, and is vastly more complex and elusive, but great strides in characterizing transcriptomes have been enabled by sequencing technology in the last decade. The objective of this course is to gain an understanding of the ways transcriptomes are analyzed with modern sequencing methods. In this course, students will move beyond thinking of gene expression as getting lists of up and down genes, and gain an appreciation for the multiple interacting mechanisms at play that determine protein levels from transcripts - including splicing, RNA modifications, and translational control. Starting from a biological question about gene regulation, the student will learn to design the experiment to assay that question, as well as choose the appropriate tools and perform the analysis. The student will also gain an understanding of current and cutting edge sequencing technology to know what assays are possible.

This is a first course in calculus and is aimed at students of diverse backgrounds who have previously not taken any formal course on the subject. The course includes a brief review of pre-calculus topics, including functions and algebra, and then moves on to computations using infinity and beyond: infinitesimal quantities, differentials, infinite sequences, and whether it is possible to divide by zero. Scientific applications and achievements will motivate the exploration of the essential single-variable calculus concepts of limits, derivatives, and integrals.

This is the first part of a two-part course. Registration is required separately for each part of the course.

This is a first course in calculus and is aimed at students of diverse backgrounds who have previously not taken any formal course on the subject. The course includes a brief review of pre-calculus topics, including functions and algebra, and then moves on to computations using infinity and beyond: infinitesimal quantities, differentials, infinite sequences, and whether it is possible to divide by zero. Scientific applications and achievements will motivate the exploration of the essential single-variable calculus concepts of limits, derivatives, and integrals.

This is the second part of a two-part course. The completion of the first part (MATH 127) is required before taking the second part. Registration is required separately for each part of the course.

This course is a continuation of MATH 127 and is focused on multivariable and vector calculus. It covers calculus of curves in space, vector functions, functions of more than one variable, and introduces vector calculus. Applications of this more general descriptions of calculus to scientific research will also be presented.

This is the first part of a two-part course. Registration is required separately for each part of the course.

This course is a continuation of MATH 127 and is focused on multivariable and vector calculus. It covers calculus of curves in space, vector functions, functions of more than one variable, and introduces vector calculus. Applications of this more general descriptions of calculus to scientific research will also be presented.

This is the second part of a two-part course. The completion of the first part (MATH 129) is required before taking the second part. Registration is required separately for each part of the course.

This is a first course in linear algebra, aimed at students with diverse backgrounds. It covers the content of a standard textbook: linear systems, vectors and matrices, dimensions and bases of vector spaces, eigenvalues and eigenvectors, singular value decomposition. It is also dedicated to explaining applications of these linear algebra concepts in classic analysis methods as well as state-of-the-art statistical inference and machine learning approaches. In the application portion of the course we will strive to tailor the content to the interests and research needs of the students.

This is the first part of a two-part course. Registration is required separately for each part of the course.

This is a first course in linear algebra, aimed at students with diverse backgrounds. It covers the content of a standard textbook: linear systems, vectors and matrices, dimensions and bases of vector spaces, eigenvalues and eigenvectors, and singular value decomposition. It is also dedicated to explaining applications of these linear algebra concepts in classic analysis methods as well as state-of-the-art statistical inference and machine learning approaches. In the application portion of the course, we will strive to tailor the content to the interests and research needs of the students.

This is the second part of a two-part course. The completion of the first part (MATH 215) OR an equivalent course OR the knowledge of vectors, matrix operations, orthogonality, and determinants is highly recommended before taking the second part. Registration is required separately for each part of the course.

This course introduces statistical concepts and essential techniques that are frequently used in biomedical data analysis. The emphasis will be equally divided between solid understanding of basic principles and their applications. R software is introduced and used for demonstration throughout the course. Topics covered in the second semester: test of statistical hypothesis; one- and two-sample tests; power and sample size calculation; analysis of variance (ANOVA); nonparametric tests; linear regression; analysis of categorical data; permutation and bootstrap; data analysis using R.

This is the first part of a two-part course. Registration is required separately for each part of the course.

This course introduces statistical concepts and essential techniques that are frequently used in biomedical data analysis. The emphasis will be equally divided between solid understanding of basic principles and their applications. R software is introduced and used for demonstration throughout the course. Topics covered in the second semester: test of statistical hypothesis; one- and two-sample tests; power and sample size calculation; analysis of variance (ANOVA); nonparametric tests; linear regression; analysis of categorical data; permutation and bootstrap; data analysis using R.

This is the second part of a two-part course. The completion of the first part (prerequisite) is required before taking the second part. Registration is required separately for each part of the course.

This course introduces statistical concepts and essential techniques that are frequently used in biomedical data analysis. The emphasis will be equally divided between solid understanding of basic principles and their applications. R software is introduced and used for demonstration throughout the course. Topics covered in the second semester: test of statistical hypothesis; one- and two-sample tests; power and sample size calculation; analysis of variance (ANOVA); nonparametric tests; linear regression; analysis of categorical data; permutation and bootstrap; data analysis using R.

This is the first part of a two-part course. Registration is required separately for each part of the course.

This course introduces statistical concepts and essential techniques that are frequently used in biomedical data analysis. The emphasis will be equally divided between solid understanding of basic principles and their applications. R software is introduced and used for demonstration throughout the course. Topics covered in the second semester: test of statistical hypothesis; one- and two-sample tests; power and sample size calculation; analysis of variance (ANOVA); nonparametric tests; linear regression; analysis of categorical data; permutation and bootstrap; data analysis using R.

This is the second part of a two-part course. The completion of the first part (STAT 203) is required before taking the second part. Registration is required separately for each part of the course.

The objective of this course is to learn the concepts and methodology used in the design and conduct of randomized clinical trials. Topics to be covered will include the description of main types of trial designs, principles of randomization and stratification, issues in protocol development (defining objectives and endpoints, blinding, choice of control), recruitment and retention, data collection and quality control issues, monitoring, and analyses of trials reports. Textbook material will be frequently supplemented by material from the literature. Guest lecturers will give lectures on power and sample size calculations, life table analysis, quality of life and cost evaluation. Examples from the cardiovascular, pulmonary, and cancer areas will be used when appropriate. The course is intended for biomedical researchers desiring exposure to the clinical-trial field. In order to run this course, minimum 10 students need to register.

The objective of this course is to provide a deeper understanding of epidemiologic research methodology that can be used to interpret critically the results of epidemiologic research. This understanding will result from investigating conceptual models for study designs, disease frequency, measures of association and impact, imprecision, bias, and effect modification. The course will emphasize the interpretation of research, even when the design or execution of the respective research is less than ideal.

The course will cover the fundamentals of the SAS program and its variables, creating data, importing data (from text and Excel files), exporting data (to text, pdf, and Microsoft-related formats), manipulating data, and providing descriptive statistics. Students will have the opportunity to practice in class, using sample datasets. Homework and project assignments will be provided as well.

The course will cover advanced SAS coding concepts such as the use of SAS Macro, SAS SQL, as well as a combination of both. The course will also introduce students to SAS STAT coding for common statistical tests (such as t-test, ANOVA, linear regression, and others). Students will have the opportunity to practice in class, using sample datasets. Homework and project assignments will be provided as well.

The objective of this course is to provide an overview of statistics for biomedical researchers and clinicians who are interested in the interpretation of the results of statistical analyses. This is a series of integrated lectures, readings, and exercises on analysis and interpretation of medical research data using Excel. Emphasis is on ideas and understanding rather than mechanics. Topics covered include the foundation of statistical logic, interpretation of the most commonly encountered statistical procedures in medical research, and selection of an appropriate method to analyze a particular set of data. The second semester expands on the material covered in the first semester.

This is the first part of a two-part course. Registration is required separately for each part of the course.

The objective of this course is to provide an overview of statistics for biomedical researchers and clinicians who are interested in the interpretation of the results of statistical analyses. This is a series of integrated lectures, readings, and exercises on analysis and interpretation of medical research data using Excel. Emphasis is on ideas and understanding rather than mechanics. Topics covered include the foundation of statistical logic, interpretation of the most commonly encountered statistical procedures in medical research, and selection of an appropriate method to analyze a particular set of data. The second semester expands on the material covered in the first semester.

This is the second part of a two-part course. The completion of the first part (STAT 500) is required before taking the second part. Registration is required separately for each part of the course.

The objective of this course is to provide an overview of statistics for biomedical researchers and clinicians who are interested in the interpretation of the results of statistical analyses. This is a series of integrated lectures, readings, and exercises on analysis and interpretation of medical research data using Excel. Emphasis is on ideas and understanding rather than mechanics. Topics covered include the foundation of statistical logic, interpretation of the most commonly encountered statistical procedures in medical research, and selection of an appropriate method to analyze a particular set of data. Those who will be routinely engaged in computing statistical procedures should consider STAT 200.

This is the first part of a two-part course. Registration is required separately for each part of the course.

The objective of this course is to provide an overview of statistics for biomedical researchers and clinicians who are interested in the interpretation of the results of statistical analyses. This is a series of integrated lectures, readings, and exercises on analysis and interpretation of medical research data using Excel. Emphasis is on ideas and understanding rather than mechanics. Topics covered include the foundation of statistical logic, interpretation of the most commonly encountered statistical procedures in medical research, and selection of an appropriate method to analyze a particular set of data. Those who will be routinely engaged in computing statistical procedures should consider STAT 200.

This is the second part of a two-part course. The completion of the first part (STAT 502) is required before taking the second part. Registration is required separately for each part of the course.

This seminar course provides a non-mathematical review of statistical tests commonly encountered in medical research. Readings and class discussions will focus on understanding and interpreting the results from studies, as well as critiquing observational and experimental studies. The target audiences for this course are clinicians, fellows who are participating in journal club/literature review activities, and researchers who want to strengthen skills in interpreting statistical tests.

Statistical analyses are a fundamental component of experimental design in many biomedical research fields. Particularly when working with large, messy data, proper understanding of statistics is essential to perform proper statistical analyses. This course will build on students' existing knowledge of statistics to help them expand their analysis toolkits and will cover topics including modeling, bootstrapping, simulations, imputation, and basic machine learning. Students will attend lectures to gain theoretical understanding of topics before applying concepts through practice problems and projects using the R programming language. Note that students should have basic proficiency in R and simple statistical analyses before enrolling in this course to be successful.