The following is a list of electives that are approved to be used towards the Program Elective and/or Data Science Selective requirement. Courses are not guaranteed to run every year and serve only as examples of suitable elective courses. Enrolled students will have the opportunity to petition to add courses to this list.

This page is currently undergoing revisions for the 2022 - 2023 academic year. For the most up-to-date course offerings, please search the course catalog on my.harvard.edu. 

  • Harvard Business School

    HBSMBA 1757 - Launching Technology Ventures (HBS: 3 credits, HMS: 4 credits)

    Fall Semester Course

    The course takes the perspective of founders struggling to achieve product market fit in their early-stage startups. Our cases focus on founder decision during this search and discovery phase, both in the experiments that they design and run as well as the organizations they try to form.

    LTV has a tactical, implementation bias rather than a strategic one and will largely avoid concepts covered in Entrepreneurial Finance and Founders' Journey. There is a modest overlap with Product Management 101/102 and Entrepreneurial Sales, and Entrepreneurial Marketing, but LTV is solely focused on pre-product-market fit and the perspective of the founder. In that regard, LTV is a complementary to Scaling Tech Ventures (STV), which focused more on post product-market fit startups.

    LTV helps students learn the playbook for finding product-market fit while learning to design business models for success, answering the question why some startups are valued at only 2x revenue while others are valued at 20x.

    Transforming Health Care Delivery, Course Number 2195 (HBS: 1.5 credits, HMS: 2 credits)

    Spring Semester Course 

    At the root of the transformation occurring in the health care industry-both in the United States and internationally-is the fundamental challenge of improving clinical outcomes while controlling costs. Addressing this challenge will require dramatic improvements in the processes by which care is delivered to patients. This will, in turn, involve changing the organization of delivery, developing new approaches to performance measurement, and reimagining the ways in which providers are paid. This course will equip students with the tools required to design and implement these improvements.

     

  • Harvard Faculty of Arts and Sciences

    APCOMP 209A - Data Science 1: Introduction to Data Science (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course 

    Program Elective

    Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set; (2) data management - accessing data quickly and reliably; (3) exploratory data analysis, generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication , summarizing results through visualization, stories, and interpretable summaries. Recommended: Programming knowledge at the level of CS 50 or above, and statistics knowledge at the level of Stat 100 or above (Stat 110 recommended).

    Cross-listed as COMPSCI 109A and STAT 121A

    APCOMP 209B - Data Science 2: Advanced Topics in Data Science (FAS: 4 credits, HMS: 4 credits)

    Spring Semester Course

    Program Elective

    Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning.

    Cross-listed as COMPSCI 109B

    APCOMP 215 - Advanced Practical Data Science (FAS: 4 credits, HMS: 4 credits)

    Not running 2022 - 2023

    Data Science Selective

    In this course, we explore advanced practical data science practices. The course will be divided into three major topics:
    1) How to scale a model from a prototype (often in jupyter notebooks) to the cloud. In this module, we cover virtual environments, containers, and virtual machines before learning about microservices and Kubernetes. Along the way, students will be exposed to Dask.
    2) How to use existing models for transfer learning. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. This can be very important, given the vast compute and time resources required to develop neural network models on these problems and given the huge jumps in skill that these models can provide to related problems. In this part of the course we will examine various pre-existing models and techniques in transfer learning.
    3) In the third part we will be introducing a number of intuitive visualization tools for investigating properties and diagnosing issues of models. We will be demonstrating a number of visualization tools ranging from the well established (like saliency maps) to recent ones that have appeared in https://distill.pub

    APCOMP 221 - Critical Thinking in Data Science (FAS: 4 credits, HMS 4 credits) 

    Spring Semester Course

    This course examines the wide-ranging impact data science has on the world and how to think critically about issues of fairness, privacy, ethics, and bias while building algorithms and predictive models that get deployed in the form of products, policy and scientific research. Topics will include algorithmic accountability and discriminatory algorithms, black box algorithms, data privacy and security, ethical frameworks; and experimental and product design. We will work through case studies in a variety of contexts including media, tech and sharing economy platforms; medicine and public health; data science for social good, and politics. We will look at the underlying machine learning algorithms, statistical models, code and data. Threads of history, philosophy, business models and strategy; and regulatory and policy issues will be woven throughout the course. 

    Recommended Prep: CS 109A, Introduction to Data Science or equivalent by instructor approval. 

    APCOMP 295 - Deep Learning for NLP (FAS: 4 credits, HMS: 4 credits)

    Not offered 2022 - 2023

    Data Science Selective, Program Elective

    How can computers understand and leverage text data and human language? Natural language processing (NLP) addresses this question, and in this course students study the current, best approaches to it. No prior NLP experience is needed, but it is welcomed. This course provides students with a foundation of advanced concepts and requires students to conduct significant research on an NLP topic of their choosing. The aim is to produce a short paper worthy of submitting to an NLP conference. Assessment also includes pop quizzes, homework assignments, and an exam. The course starts with language representations and modeling, followed by machine translation (converting text from one language to another). Next, students learn about transformers (e.g., BERT and GPT-2), which are incredibly powerful deep learning models that currently yield state-of-the-art results in nearly every NLP task. We end the semester by covering tasks concerning bias and fairness, adversarial approaches, coreference resolution, and commonsense reasoning. 

    Also listed as COMPSCI 287R 

    APMTH 216 - Inverse Problems in Science and Engineering FAS: 4, HMS: 4 credits) 

    Not running 2022 - 2023

    Spring Semester Course 

    Program Elective

    Many problems in science and engineering are inverse problems.  Any experiment that requires an explanation can be couched thus -  given the data, what is the theory/model that provides it - this is an inverse problem. In engineering, a given function (in a product/software …. ) requires a design - again an inverse problem.  This course will introduce a wide array of features of inverse problems from science and engineering - from oil prospecting  and seismology to cognitive science, from particle physics to engineering design. We will then introduce deterministic and probabilistic algorithms for solving these problems. Much of the class will be spent studying how the recent revolution in deep neural networks can (and cannot) be used to solve such inverse problems. The class will have a substantial computational component -- part of every class session will contain instruction and computer implementation of the algorithms in question. Students will carry out final projects in their own area of interest.   Programming will be taught and carried out in Python and Tensorflow.

    Recommended Prep: Linear algebra, Differential equations, Basic probability. 

    BIOPHYS 170 - Evolutionary and Quantitative Genomics (FAS: 4, HMS: 4 credits)

    Fall Semester Course

    Data Science Selective

    In-depth study of genomics: models of evolution and population genetics; comparative genomics: analysis and comparison; structural genomics: protein structure, evolution and interactions; functional genomics, gene expression, structure and dynamics of regulatory networks.

    Cross-listed as HST.508. Harvard students should enroll through Biophysics 170. 

    BIOSTAT 234 - Introduction to Data Structures and Algorithms (FAS: 4, HMS: 4 credits)

    Spring Semester Course

    Program Elective

    Introduction to the data structures and computer algorithms that are relevant to statistical computing. The implementation of data structures and algorithms for data management and numerical computations are discussed.

    Cross-listed as BST 234. HMS students should enroll through BIOSTAT 234. 

    BMIF 201 - Concepts in genome analysis (FAS: 4, HMS: 4 credits)

    Fall Semester Course

    Data Science Selective; Program Elective

    This course focuses on quantitative aspects of genetics and genomics, including computational and statistical methods of genomic analysis. We will introduce basic concepts and discuss recent progress in population and evolutionary genetics and cover principles of statistical genetics of Mendelian and complex traits. We will then introduce current genomic technologies and key algorithms in computational biology and bioinformatics. We will discuss applications of these algorithms to genome annotation and analysis of epigenomics, cancer genomics and metagenomics data. Proficiency in programming and basic knowledge of genetics and statistics will be assumed. 

    BST 234 - Introduction to Data Structures and Algorithms (FAS: 4 credits, HMS: 4 credits)

    Spring Semester Course 

    Program Elective

    Introduction to the data structures and computer algorithms that are relevant to statistical computing. The implementation of data structures and algorithms for data management and numerical computations are discussed. 

    CELLBIO 312QC - Deep Learning for Biomedical Image Analysis (FAS: 2 credits, HMS: 2 credits)

    Spring Semester Course

    Program Elective

    Biomedical image analysis is undergoing a paradigm shift due to artificial intelligence and deep learning. This course will cover basic concepts of deep learning and convolutional neural networks for biomedical image analysis as well as current challenges and opportunities. The lectures will include fundamentals of classification, characterization, detection, segmentation and enhancement in biomedical images. Using a variety of different microscopy and pathology datasets the course will follow a ‘learning-by-doing’ model where each lecture will be accompanied by hand on training in using these methods in practice. The course assumes no prior knowledge of deep learning or image analysis. Basic knowledge of python is recommended but not required. 

    COMPSCI 107 - Systems Development for Computational Science (FAS: 4, HMS: 4 credits)

    Fall Semester Course

    Data Science Selective; Program Elective

    This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing and data sciences. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to adapt basic tools and approaches to solve computational problems in academic or industrial environments.

    COMPSCI 179 – Design of Usable Interactive Systems (FAS: 4, HMS: 4 credits)

    Spring Semester Course

    Program Elective

    Usability and design as keys to successful technology. Covers user observation techniques, needs assessment, low and high fidelity prototyping, usability testing methods, as well as theory of human perception and performance, and design best practices. Focuses on understanding and applying the lessons of human interaction to the design of usable systems; will also look at lessons to be learned from less usable systems. The course includes several small and one large project. 

    COMPSCI 181 - Machine Learning (FAS: 4, HMA: 4 credits)

    Spring Semester Course 

    Program Elective

    Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python.

    COMPSCI 182 - Artificial Intelligence (FAS: 4, HMS: 4 credits)

    Fall Semester Course

    Program Elective

    Artificial Intelligence (AI) is an exciting field that has enabled a wide range of cutting-edge technology, from driverless cars to grandmaster-beating Go programs. The goal of this course is to introduce the ideas and techniques underlying the design of intelligent computer systems. Topics covered in this course are broadly be divided into 1) planning and search algorithms, 2) probabilistic reasoning and representations, and 3) machine learning (although, as you will see, it is impossible to separate these ideas so neatly). Within each area, the course will also present practical AI algorithms being used in the wild and, in some cases, explore the relationship to state-of-the-art techniques. The class will include lectures connecting the models and algorithms we discuss to applications in robotics, computer vision, and speech processing. Recommended prep: CS 51; Stat 110 (may be taken concurrently).

    COMPSCI 187 - Introduction to Computational Linguistics and Natural-Language Processing

    Not offered in 2022 - 2023

    Data Science Selective; Program Elective

    Natural-language-processing applications are ubiquitous: Alexa can set a reminder if you ask; Google Translate can make emails readable across languages; Watson outplays world Jeopardy champions; Grover can generate fake news, and recognize it as well. How do such systems work? This course provides an introduction to the field of computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems such as these. You will work with ideas from linguistics, statistical modeling, and machine learning, with emphasis on their application, limitations, and implications. The course is lab- and project-based, primarily in small teams, and culminates in the building and testing of a question-answering system.

    Recommended Prep: Programming ability and computer science knowledge at the level of CS51; knowledge of discrete mathematics, including basic probability, statistics, and logic at the level of CS20; some familiarity with Python programming.

    COMPSCI 197 - AI Research Experiences (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Learn the practical skills required for applied deep learning work, including hands on experience with method development, model training at scale, and error analysis. You will learn the technical writing skills required for applied AI research, including experience composing different elements of a full research paper. Through structured assignments, you will tackle a scoped-out research project in a small team from conception to co-authoring a manuscript.

    Recommended Prerequisites: A background in machine learning, at the level of CS181 or CS109b or equivalent. A background in software engineering, at the level of being able to write non-trivial Python programs.

    COMPSCI 205 - Computing Foundations for Computational Science (FAS: 4 credits, HMS: 4 credits)

    Spring Semester Course

    Computational science has become a third partner, together with theory and experimentation, in advancing scientific knowledge and practice, and an essential tool for product and process development and manufacturing in industry. Big data science adds the ‘fourth pillar’ to scientific advancements, providing the methods and algorithms to extract knowledge or insights from data. The course is a journey into the foundations of Parallel Computing at the intersection of large-scale computational science and big data analytics. Many science communities are combining high performance computing and high-end data analysis platforms and methods in workflows that orchestrate large-scale simulations or incorporate them into the stages of large-scale analysis pipelines for data generated by simulations, experiments, or observations. This is an applications course highlighting the use of modern computing platforms in solving computational and data science problems, enabling simulation, modeling and real-time analysis of complex natural and social phenomena at unprecedented scales. The class emphasizes on making effective use of the diverse landscape of programming models, platforms, open-source tools, computing architectures and cloud services for high performance computing and high-end data analytics. 

    COMPSCI 242 - Computing at Scale (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Scaling computation over parallel and distributed computing systems is a rapidly advancing area of research receiving high levels of interest from both academia and industry. The objective can be for high-­‐performance computing and energy-­‐efficient computing (“green” data center servers as well as small embedded devices). In this course, students will learn principled methods of mapping prototypical computations used in machine learning, the Internet of Things, and scientific computing onto parallel and distributed compute nodes of various forms. These techniques will lay the foundation for future computational libraries and packages for both high-­‐performance computing and energy-­‐efficient devices. To master the subject, students will need to appreciate the close interactions between computational algorithms, software abstractions, and computer organizations. After having successfully taken this course, students will acquire an integrated understanding of these issues. The class will be organized into the following modules: Big picture: use of parallel and distributed computing to achieve high performance and energy efficiency; End-­‐to-­‐end example 1: mapping nearest neighbor computation onto parallel computing units in the forms of CPU, GPU, ASIC and FPGA; Communication and I/O: latency hiding with prediction, computational intensity, lower bounds; Computer architectures and implications to computing: multi-­‐cores, CPU, GPU, clusters, accelerators, and virtualization; End-­‐to-­‐end example 2: mapping convolutional neural networks onto parallel computing units in the forms of CPU, GPU, ASIC, FPGA and clusters; Great inner loops and parallelization for feature extraction, data clustering and dimension reduction: PCA, random projection, clustering (K-­‐means, GMM-­‐EM), sparse coding (K-­‐SVD), compressive sensing, FFT, etc.; Software abstractions and programming models: MapReduce (PageRank, etc.), GraphX/Apache Spark, OpenCL and TensorFlow; Advanced topics: autotuning and neuromorphic spike-­‐based computing.  Students will learn the subject through lectures/quizzes, programming assignments, labs, research paper presentations, and a final project.  Students will have latitude in choosing a final project they are passionate about. They will formulate their projects early in the course, so there will be sufficient time for discussion and iterations with the teaching staff, as well as for system design and implementation. Industry partners will support the course by giving guest lectures and providing resources.  The course will use server clusters at Harvard as well as external resources in the cloud. In addition, labs will have access to state-­‐of-­‐the-­‐art IoT devices and 3D cameras for data acquisition. Students will use open source tools and libraries and apply them to data analysis, modeling, and visualization problems.

    COMPSCI 282R - Topics in Machine Learning (FAS: 4 credits, HMS: 4 credits)

    Not offered 2022 - 2023

    Data Science Selective

    Special topics course. Focus of course changes year to year. 

    COMPSCI 289 - Biologically Inspired Multi-agent Systems (FAS: 4 credits, HMS: 4 credits)

    Not offered 2022 - 2023

    Data Science Selective

    Surveys biologically-inspired approaches to designing distributed systems. Focus is on biological models, algorithms, and programming paradigms for self-organization. Topics vary year to year, and usually include: (1) swarm intelligence: social insects and animal groups, with applications to networking and robotics, (2) cellular computing: including cellular automata/amorphous computing, and applications like self-assembling robots and programmable materials, (3) evolutionary computation and its application to optimization and design. Recommended Prep: Students should have a familiarity/experience with computer systems (e.g. software, networking) and algorithms/analysis through classes and/or internship experiences. Background in biology not required.

    Genetics 228 - Genetics in Medicine: From Bench to Bedside (FAS: 4 credits, HMS: 4 credits)

    Spring Semester Course

    Focus on translational medicine: the application of basic genetic discoveries to human disease. Each three-hour class will focus on a specific genetic disorder and the approaches currently used to speed the transfer of knowledge from the laboratory to the clinic. Each class will include a clinical discussion, a patient presentation if appropriate, followed by lectures, a detailed discussion of recent laboratory findings and a student led journal club. Lecturers will highlight current molecular, technological, bioinformatic and statistical approaches that are being used to advance the study of human disease. There is no exam. Students will present one paper per session in a journal club style. Attendance and active participation for the duration of all class meetings is required. If you are unable to attend class, or cannot be present for the entire session you are expected to contact the course instructor. Two incomplete or missed sessions will result in a failing grade. 

    MATH 156 - Mathematical Foundations of Statistical Software (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Presents the probability theory and statistical principles which underly the tools that are built into the open-source programming language R. Each class presents the theory behind a statistical tool, then shows how the implementation of that tool in R can be used to analyze real-world data. The emphasis is on modern bootstrapping and resampling techniques, which rely on computational power. Topics include discrete and continuous probability distributions, permutation tests, the central limit theorem, chi-square and Student t tests, linear regression, and Bayesian methods.

    MATH 242 - Mathematical Biology - Evolutionary Dynamics (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Data Science Selective Course

    This course introduces basic concepts of mathematical biology and evolutionary dynamics: reproduction, selection, mutation,  genetic drift, quasi-species, finite and infinite population dynamics, game dynamics, evolution of cooperation, language, spatial models, evolutionary graph theory, infection dynamics, virus dynamics, somatic evolution of cancer.

    MCB 145 - Neurobiology of Perception and Decision-Making (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    One of the current goals of neuroscience is to understand neuronal circuits underlying perception and behavior. Recent advances in neuroscience have allowed us to glimpse neuronal processes that link perception and decision making. How is sensory information processed in the brain? How does an animal choose its action? How does an animal learn from ever-changing environments and adjust their behavior? The course will examine neurophysiological studies in perception and decision-making.

    MCB 169 - Molecular and Cellular Immunology (FAS: 4 credits, HMS: 4 credits) 

    Fall Semester Course

    The immune system is the frontier at which molecular biology, cell biology, and genetics intersect with the pathogenesis of disease. This year the entire course will be taught through the lens of COVID19, examining the underlying scientific bases of pathogenesis, protection, treatment and prevention. The course examines in depth the cellular and molecular mechanisms involved in the development and function of the immune system and also analyzes the immunological basis of human diseases in general. Apart from COVID19, we will discuss AIDS, autoimmunity, allergic disorders, primary immunodeficiency syndromes, transplantation, and cancer

    MICROBI 302QC - Introduction to Infectious Disease Research: Infectious Diseases Consortium Bootcamp (FAS: 2 credits, HMS: 2 credits)

    January Term Course 

    Not offered J-term 2022

    This January boot camp course provides a fun, informative introduction to the breadth of infectious disease research carried out at Harvard and beyond. Students will have the chance to meet faculty, students, and fellows in infectious disease roles across the university. The course will focus on several aspects of infectious diseases:  
      
    1. Underlying biology of infectious diseases and the diverse pathogens that cause them 
    2. Modern approaches to studying infectious diseases, including experimental biology, epidemiology, bioinformatics, and clinical microbiology 
    3. Modern approaches to developing new interventions, including drugs, vaccines, diagnostics, and public health measures 

    PSY 1451 - Debugging the Brain: Computational Approaches to Mental Dysfunction (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    This course examines recent work applying computational models to mental disorders. These models formalize psychopathology in terms of breakdown in fundamental neurocognitive processes, linking normal and abnormal brain function within a common framework. Computational modeling has already begun to yield insights, and even possible treatments, for a wide range of disorders, including schizophrenia, autism, Parkinson’s, depression, obsessive-compulsive disorder, and attention-deficit hyperactivity disorder. The course will consist of weekly readings from the primary literature, with one student leading the discussion of each paper

    STAT 110 - Introduction to Probability (FAS: 4 credits, HMS: 4 credits) 

    Fall Semester Course 

    A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes' Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.  

    STAT 111 - Introduction to Statistical Inference (FAS: 4 credits, HMS: 4 credits) 

    Spring Semester Course 

    The course is designed for undergraduates as their first introduction to rigorous statistical inference.  Understanding the foundations will allow you to see more deeply into individual methods and applications, placing them in context and able to learn new ones (and invent new ones!) much faster having understood broad principles of inference.

    STAT 139 - Linear Models (FAS: 4 credits, HMS: 4 credits) 

    Fall Semester Course 

    An in-depth introduction to statistical methods with linear models and related methods. Topics include group comparisons (t-based methods, non-parametric methods, bootstrapping, analysis of variance), linear regression models and their extensions (ordinary least squares, ridge, LASSO, weighted least squares, multi-level models), model checking and refinement, model selection, cross-validation. The probabilistic basis of all methods will be emphasized.

    STAT 195 - Statistical Machine Learning (FAS: 4 credits, HMS: 4 credits)

    Spring Semester Course

    This course is designed to follow CS 181 and go into further depth on the statistical aspects of supervised learning: given what we know about our data and where it came from, how can we choose the machine learning method that will predict best on future data? Topics include the ``no free lunch" theorems, linear methods for regression and classification, shrinkage and sparsity, splines and kernel smoothing, model selection and cross-validation, additive models and trees, boosting, bagging and random forests. Recommended prep: Statistics 111 and Computer Science 181 

  • Harvard Medical School

    BETH 704 - Neuroethics (HMS: 2 credits)

    January Term Course

    Program Elective

    BMI 741 - Health Information Technology: From Ideation to Implementation (HMS: 4 credits)

    Spring Semester Course 

    BMI 720 & 721 Clinical Informatics I: Lecture and Lab (HMS: 4 credits) 

    Fall Semester Course 

    Data Science Selective; Program Elective

    This course provides a detailed overview of clinical informatics for professionals who will work at the interface of clinical care, information technology, and the healthcare system. Students will learn how to analyze, design, implement, and evaluate information and communication technologies found in hospitals, physician offices, and other healthcare settings including the home. Emphasis will be placed on the evolution of the electronic health record and its use to promote patient care that is safe, efficient, effective, timely, patient-centered and equitable. Students will also study implementation failures and unintended consequences of systems. The course will cover the fundamental concepts in clinical informatics such as evidence-based care and clinical workflow analysis. Students will not only study health information systems but have assignments to evaluate some real-life systems at local hospitals. Through case-based analysis, students analyze the life-cycle management of complex clinical computing systems. This course is geared towards physicians seeking postgraduate training. Course director permission required for cross-registration. 

    The practical lab component course that accompanies BMI 720, Clinical Informatics, and should be taken concurrently with that course. The labs will demonstrate concepts introduced in BMI 720 so that learners develop a more nuanced understanding of the different competing priorities in Clinical Informatics. The course will focus both on applied clinical informatics, specifically around clinical decision support, as well as applied machine learning for EHR data. Labs will be assigned on a weekly basis and will be group-based. 

    CSO 704 - Creating a Learning Organization in Healthcare Settings (HMS: 2 credits) 

    Spring Semester Course 

    The education mission is a key element of all healthcare organizations for patients, providers, and staff. Given the rapid pace of change in healthcare, not only must individuals be continuous learners, but also organizations must continually "learn" to adapt, change, and grow. As our healthcare organizations become vehicles for population health management across regional networks, this type of learning is necessary now more than ever before. Additionally, with the advent of system-wide EMRs, digital and tele-health, access to big data, and personalized medicine approaches, the way providers achieve their work and approach patient care is evolving. There are some new risks associated with rapid change and organizations need to learn how to preserve the fundamentals of patient care. Furthermore, patient and family expectations for the providers and teams are shifting and even some care is shifting into the home or virtual setting. This course will explore the facets of creating a learning organization. The course will also explore the teaching mission in the healthcare setting, including the operational integration of students and post-graduate trainees and financing of graduate medical education.

    CSO 708 - Integrating New Technology into Healthcare Delivery (HMS: 2 credits) 

    Spring Semester Course 

    Technological advances in healthcare are exploding at a rapid pace. The clinical operations leader needs a firm grasp of how to evaluate and implement new technologies into workflow.  Telemedicine and digital health are two major disruptive technologies that are resulting in entirely new processes and workflows. Additionally, the role of artificial intelligence and big data are a major trend that will likely be part of basic operations over the next 10 years. Clinical operations leaders need to understand how patients and providers interface with these new technologies. Another major category of innovation is the rise of genomic medicine and personalized healthcare. These advances can create major patient benefits but also operational hurdles as care becomes tailored for the individual patient. Students will gain a lens into the complexities of reimbursement and the benefits and risks of early adoption of technology. The course will also explore how the innovation curve is dynamic and how healthcare organizations translate innovation in different settings.

    IN 601 - Medicine and Management (HMS: 2 credits) 

    Fall Semester Course 

    As practicing professionals, physicians continually face complex challenges in relating to the organizations in which they work. This class, designed for students in the HMS/HBS combined MD/MBA program and the Clinical Informatics Fellowship Program provides an overview of the structure and function of health care institutions and illustrates the role of these organization in producing successful clinical outcomes. Class sessions, assigned readings, and written assignments offer a multidisciplinary perspective combining component academic disciplines of management and medicine. 

  • Harvard T.H. Chan School of Public Health

    BST 201 - Introduction to Statistical Methods

    Fall Semester Course

    Covers basic statistical techniques that are important for analyzing data arising from epidemiology, environmental health and biomedical and other public health-related research. Major topics include descriptive statistics, elements of probability, introduction to estimation and hypothesis testing, nonparametric methods, techniques for categorical data, regression analysis, analysis of variance, and elements of study design. Applications are stressed. Designed as an alternate to BIO200, for students desiring more emphasis on theoretical developments. Background in algebra and calculus strongly recommended.

    BST 210 - Applied Regression Analysis (HSPH: 5 credits, HMS: 4 credits)

    Fall and Spring Semester Course

    Topics include model interpretaion, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature. Prerequisites: ID 201 or BST201 or (BST202 and BST203) or (BST206 and (BST207 or BST208)) or permission of instructor. 

    BST 212 – Survey Research Methods in Community Health (HSPH: 2.5 credits, HMS: 2 credits)

    Spring Semester Course

    Covers research design, sample selection, questionnaire construction, interviewing techniques, the reduction and interpretation of data, and related facets of population survey investigations. Focuses primarily on the application of survey methods to problems of health program planning and evaluation. Treatment of methodology is sufficiently broad to be suitable for students who are concerned with epidemiological, nutritional, or other types of survey research. Formerly BIO212

    BST 213 - Applied Regression for Clinical Research (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    This course will introduce students involved with clinical research to the practical application of multiple regression analysis. Linear regression, logistic regression and proportional hazards survival models will be covered, as well as general concepts in model selection, goodness-of-fit, and testing procedures. Each lecture will be accompanied by a data analysis using SAS and a classroom discussion of the results. The course will introduce, but will not attempt to develop the underlying likelihood theory. Background in SAS programming ability required.

    BST 214 - Principles of Clinical Trials (HSPH: 2.5 credits, HMS: 2 credits)

    Spring Semester Course

    Designed for individuals interested in the scientific, policy, and management aspects of clinical trials. Topics include types of clinical research, study design, treatment allocation, randomization and stratification, quality control, sample size requirements, patient consent, and interpretation of results. Students design a clinical investigation in their own field of interest, write a proposal for it, and critique recently published medical literature. Course Prerequisites: BIO201 or ID200 or ID201 or ID207 or BIO202&203 or BIO206&207 or BIO206&208 or BIO206&209. Formerly BIO214 

    BST 215 – Linear and Longitudinal Regression (HSPH: 2.5 credits, HMS: 2 credits)

    Spring Semester Course

    This course is intended for students who are already very comfortable with fundamental techniques in statistics. The course will cover methods for building and interpreting linear regression models, including statistical assumptions and diagnostics, estimation and testing, and model building techniques. These models will be extended to handle data arising from longitudinal studies employing repeated measurement of subjects over time. Summer/Residential Course Note (Section 1): Lectures will be accompanied by computing exercises using the SAS statistical package. Online Course Note (Section 2): Lectures will be accompanied by computing exercises using the Stata statistical package. Course Prerequisites: EPI522 or BST201 or ID200 or ID201 or ID207 or BST202&203 or BST206&207 or BST206&208. Formerly BIO501 

    BST 217 - Statistical and Quantitative Methods for Pharmaceutical Regulatory Science (HSPH: 2.5 credits, HMS: 2 credits)

    Spring Semester Course

    The goal of this course is to enable scientists and public health professionals who already have an introductory background in biostatistics and clinical trials to acquire the competencies in quantitative skills and systems thinking required to understand and participate in drug development and regulatory review processes. The course illustrates how statistical and quantitative methods are used to transform information into evidence demonstrating the safety, efficacy and effectiveness of drugs and devices over the course the product’s life cycle from a regulatory perspective. Content is delivered using a blended-learning approach involving lectures, web-based media and selected case study examples derived from actual FDA decision-making and regulatory assessments to highlight and describe each phase of the regulatory drug approval process. Case studies will illustrate regulatory science in action and practice and will include content publicly available from the FDA’s website that can be used in conjunction with FDA science-based guidance and decision precedents. Course Prerequisites: ID538 or [(BIO200 or ID200 or BIO201 or BIO202&203 or BIO206&207/8/9) and (EPI200 or EPI201 or EPI208 or EPI505).] Formerly BIO523 

    BST 219 - Core Principles of Data Science 

    Fall Semester Course

    Modern technology has led to the generation of unprecedented amounts of data, prompting the need to train researchers to leverage data for decision-making in public health and medicine. This course assumes no prior R or programming knowledge and serves as a gentle, practical introduction to wrangling, visualizing, and modeling data using the R statistical programming language. We also emphasize the importance of reproducible research and effective data science communication.

    BST 222- Basics of Statistical Inference (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    This course will provide a basic, yet thorough introduction to the probability theory and mathematical statistics that underlie many of the commonly used techniques in public health research. Topics to be covered include probability distributions (normal, binomial, Poisson), means, variances and expected values, finite sampling distributions, parameter estimation (method of moments, maximum likelihood), confidence intervals, hypothesis testing (likelihood ratio, Wald and score tests). All theoretical material will be motivated with problems from epidemiology, biostatistics, environmental health and other public health areas. This course is aimed towards second year doctoral students in fields other than Biostatistics. Background in algebra and calculus required. Course Prerequisites: BST210 or BST213. Formerly BIO222

    BST 223 - Applied Survival Analysis (HSPH: 5 credits, HMS: 4 credits)

    Spring Semester Course

    Topics will include types of censoring, hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and the accelerated failure time model, adjustment for time-varying covariates, and the use of parametric distributions (exponential, Weibull) in survival analysis. Methods for recurrent survival outcomes and competing risks will also be discussed, as well as design of studies with survival outcomes. Class material will include presentation of statistical methods for estimation and testing along with current software (SAS, Stata) for implementing analyses of survival data. Applications to real data will be emphasized. Course Prerequisite(s): BST210 or BST213 or BST 230, or permission of instructor required. BST 213 may be taken concurrently. Formerly BIO223 

    BST 226 - Applied Longitudinal Analysis (HSPH: 5 credits, HMS: 4 credits)

    Spring Semester Course

    This course covers modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including the unbalanced and incomplete data sets characteristic of biomedical research. Topics include an introduction to the analysis of correlated data, analysis of response profiles, fitting parametric curves, covariance pattern models, random effects and growth curve models, and generalized linear models for longitudinal data, including generalized estimating equations (GEE) and generalized linear mixed effects models (GLMMs).Course Activities: Homework assignments will focus on data analysis in SAS using PROC GLM, PROC MIXED, PROC GENMOD, and PROC GLIMMIX. Course Note: Lab or section times will be announced at first meeting. Course Prerequisite(s): BIO210 or BIO211 or BIO213 or BIO232. Formerly BIO226 

    BST 227 - Introduction to Statistical Genetics (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    This course introduces students to the diverse statistical methods used throughout the process of statistical genetics, from familial aggregation and segregation studies to linkage scans and association studies. Topics covered include basic principles from population genetics, multipoint and model-free linkage analysis, family-based and population-based association testing, and Genome Wide Association analysis. Instructors use ongoing research into the genetics of respiratory disease, psychiatric disorders and cancer to illustrate basic principles. Weekly homework supplements reading, course lectures, discussion and section. Relevant concepts in genetics and molecular genetics will be reviewed in lectures and labs. The emphasis of the course is fundamental principles and concepts. Course Prerequisites: BST210 (concurrent enrollment allowed)Course Note: There will be a weekly lab section; the time will be scheduled at first meeting. Formerly BIO227

    BST 228 - Applied Bayesian Analysis (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    This course is a practical introduction to the Bayesian analysis of biomedical data. It is an intermediate Master’s level course in the philosophy, analytic strategies, implementation, and interpretation of Bayesian data analysis. Specific topics that will be covered include: the Bayesian paradigm; Bayesian analysis of basic models; Bayesian computing: Markov Chain Monte Carlo; STAN R software package for Bayesian data analysis; linear regression; hierarchical regression models; generalized linear models; meta-analysis; models for missing data. Programming and case studies will be used throughout the course to provide hands-on training in these concepts. Prerequisites: BST210 and BST222, or permission of the instructor. 

    BST 230 - Probability I (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    Axiomatic foundations of probability, independence, conditional probability, joint distributions, transformations, moment generating functions, characteristic functions, moment inequalities, sampling distributions, modes of convergence and their interrelationships, laws of large numbers, central limit theorem, and stochastic processes.

    BST 231 - Statistical Inference I (HSPH: 5 credits, HMS: 4 credits)

    Spring Semester Course

    A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests. Course Note: Lab or section time to be announced at first meeting; cross-listed: HSPH student must register for HSPH course. Course Prerequisite(s): BIO230 (concurrent enrollment allowed). Formerly BIO231 

    BST 234 - Introduction to Data Structures and Algorithms (HSPH: 5 credits, HMS: 4 credits)

    Spring Semester Course

    Introduction to the data structures and computer algorithms that are relevant to statistical computing. The implementation of data structures and algorithms for data management and numerical computations are discussed. Course Prerequisite(s): Instructor’s Permission. Formerly BIO514 

    BST 235 - Advanced Regression and Statistical Learning (HSPH: 5 credits, HMS: 4 credits) 

    Fall Semester Course

    Satisfies Data Science Selective Requirement

    An advanced course in linear models, including both classical theory and methods for high dimensional data. Topics include theory of estimation and hypothesis testing, multiple testing problems and false discovery rates, cross validation and model selection, regularization and the LASSO, principal components and dimensional reduction, and classification methods. Background in matrix algebra and linear regression required. Prerequisite: BST 231 and BST 233, or permission of instructor required. Formerly BIO235

    BST 240 - Probability II (HSPH: 5 credits, HMS: 4 credits) 

    Fall Semester Course

    A foundational course in measure theoretic probability. Topics include measure theory, Lebesgue integration, product measure and Fubini’s Theorem, Radon-Nikodym derivatives, conditional probability, conditional expectation, limit theorems on sequences of random stochastic processes, and weak convergence. Course Prerequisites: BST231 or permission from the instructor required. Formerly BIO250

    BST 241 - Statistical Inference II (HSPH: 5 credits, HMS: 4 credits) 

    Spring Semester Course

    Sequel to BIO 231. Considers several advanced topics in statistical inference. Topics include limit theorems, multivariate delta method, properties of maximum likelihood estimators, saddle point approximations, asymptotic relative efficiency, robust and rank-based procedures, resampling methods, and nonparametric curve estimation. Course Note: Cross-listed, HSPH must register for HSPH course. Course Prerequisites: BIO231 and BIO250, or permission of instructor required. Formerly BIO251 

    BST 245 - Analysis of Multivariate and Longitudinal Data (HSPH: 5 credits, HMS: 4 credits) 

    Fall Semester Course

    Presents classical and modern approaches to the analysis of multivariate observations, repeated measures, and longitudinal data. Topics include the multivariate normal distribution, Hotelling’s T2, MANOVA, the multivariate linear model, random effects and growth curve models, generalized estimating equations, statistical analysis of multivariate categorical outcomes, and estimation with missing data. Discusses computational issues for both traditional and new methodologies. Course Note: Cross-listed, HSPH student must register for HSPH course. Course Prerequisite: BIO231 and BIO235, or permission of the instructor are required. Formerly BIO245.

    BST 249 - Bayesian Methodology in Biostats (HSPH: 5 credits, HMS: 4 credits) 

    Spring Semester Course 

    General principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials, survival analysis. 

    BST 261 – Data Science II (HSPH: 2.5 credits, HMS: 2 credits) 

    Spring Semester Course

    This course is the second course in the foundational sequence of the School’s newly approved Master’s Degree in Health Data Science. The course will build upon our existing course, BST260 Introduction to Data Science, in presenting a set of tools for modeling and understanding complex datasets. Specifically, the course will provide practical regression and tree-based techniques for big data. Specific topics that will be covered include: linear model selection and regularization: LASSO and regularization; principal component regression and partial least squares; tree-based methods: decision trees; bagging, random forests, and boosting; unsupervised learning: principal components analysis, cluster analysis. Programming (Python and R) and case studies will be used throughout the course to provide hands-on training in these concepts. Prerequisites: BST260 or permission of instructor. 

    BST 262 - Computing for Big Data (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course

    Big data is everywhere, from Omics and Health Policy to Environmental Health. Every single aspect of the Health Sciences is being transformed. However, it is hard to navigate and critically assess tools and techniques in such a fast-moving big data panorama. In this course, we are going to give a critical presentation of theoretical approaches and software implementations of tools to collect, store and process data at scale. The goal is not just to learn recipes to manipulate big data but learn how to reason in terms of big data, from software design and tool selection to implementation, optimization and maintenance. 

    BST 267 - Introduction to Social and Biological Networks (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course

    Many systems of scientific and societal interest consist of a large number of interacting components. The structure of these systems can be represented as networks where network nodes represent the components and network edges the interactions between the components. Network analysis can be used to study how pathogens, behaviors and information spread in social networks, having important implications for our understanding of epidemics and the planning of effective interventions. In a biological context, at a molecular level, network analysis can be applied to gene regulation networks, signal transduction networks, protein interaction networks, and more. This introductory course covers some basic network measures, models, and processes that unfold on networks. The covered material applies to a wide range of networks, but we will focus on social and biological networks. To analyze and model networks, we will learn the basics of the Python programming language and its Network X module. The course contains a number of hands-on computer lab sessions. There are five homework assignments and four reading assignments that will be discussed in class. In addition, each student will complete a final project that applies network analysis techniques to study a public health problem. Course Prerequisites: BST201 or ID200 or ID201 or ID207 or BST202&203 or BST206&207 or BST206&208. Formerly BIO521

    BST 272 - Computing Environments for Biology (HSPH: 1.25 credits, HMS 1 credit)

    January Term Course

    This course provides a high-level introduction to general computing environments appropriate for biological data analysis, as preparation for more advanced computational biology and bioinformatics courses. It is intended for biologists, clinician-researchers, other bench or translational scientists, or mathematicians with little to no computational or applied quantitative experience. It provides a compressed, highly interactive, hands-on introduction to basic command line, Python, and R environments for biological data analysis and visualization. It covers basic quantitative methods that can be carried out for 'omics data analysis in these environments and ensures that students have access to local and online (i.e. grid, cloud) resources for using these tools in the future. Finally, it thoroughly introduces freely available documentation and strategies for self-learning when using computational methods for biology research. 

    BST 283 - Cancer Genome Analysis (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    This course is an introduction to modern statistical computing techniques used to characterize and interpret cancer genome sequencing datasets. This Master’s level course will begin with a basic introduction to DNA, genes, and genomes for students with no biology background. It will then introduce cancer as an evolutionary process and review landmarks in the history of cancer genetics, and discuss the basics of sequencing technology and modern Next Generation Sequencing. The course will cover the main steps involved in turning billions of short sequencing reads into a representation of the somatic genetic alterations characterizing an individual patient’s cancer, and will build on this foundation to study topics related to identifying mutations under positive selection from multiple tumors sampled in a population. By the end of the course, students will be able to apply state-of-the art analysis to cancer genome datasets and to critically evaluate papers employing cancer genome data. 

    EH 298 - Environmental Epigenetics (HSPH: 2.5 credits, HMS: 2 credits) 

    Spring Semester Course

    Epigenetics is a fast growing field, with increasing applicability in environmental and epidemiology studies, focusing on the alterations in chromatin structure that can stably and heritably influence gene expression. Epigenetic changes can be as profound as those exerted by mutation, but, unlike mutations, are reversible and responsive to environmental influences. The course will focus on epigenetic mechanisms and laboratory methods for DNA methylamine, his tone modifications, small non-coding RNAs, and epigenomics. Ongoing experimental, and epidemiology studies (cohort, case-control, cross-sectional and repeated measurement studies) will be presented to introduce the students to the epigenetic effects in prenatal/early and adult life of environmental factors, including air pollution, metals, pesticides, benzene, PCBs, persistent organic pollutants, and diet. The course will enable them to understand and apply epigenetic methods in multiple areas, including cardiovascular and respiratory disease, aging, reproductive health, inflammation/immunity, and cancer. 

    EPI 201 - Introduction to Epidemiology Methods I (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course

    EPI201 introduces the principles and methods used in epidemiologic research. The course discusses the conceptual and practical issues encountered in the design and analysis of epidemiologic studies for description and causal inference. EPI201 is the first course in the series of methods courses designed for students majoring in Epidemiology, Biostatistics and related fields, and those interested in a detailed introduction to the design and conduct of epidemiologic studies. Students who take EPI201 are expected to take EPI202 (Methods II). Course Note: Thursday or Friday lab required. 

    EPI 202 - Epidemiologic Methods 2: Elements of Epidemiologic Research (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course 

    EPI202 is a seamless continuation of EPI201. This course builds on the material from EPI201 and extends to concepts of statistical inference, data analysis methods and causal inference in epidemiologic research. Principles and methods are illustrated with examples, and reviewed through homework and in-class exercises. This course is designed primarily for doctoral students majoring in Epidemiology and related fields, and those interested in a research career requiring a rigorous foundation in the design, conduct and analysis of epidemiologic studies.

    EPI 288 – Data Mining and Prediction (HSPH: 2.5 credits, HMS: 2 credits) 

    Spring Semester Course

    This course will present an introduction to the methods of data mining and predictive modeling, with applications to both genetic and clinical data. Basic concepts and philosophy of supervised and unsupervised data mining as well as appropriate applications will be discussed. Topics covered will include multiple comparisons adjustment, cluster analysis, principal component analysis, and predictive model building through logistic regression, classification and regression trees (CART), multivariate adaptive splines (MARS), neural networks, random forests, and bagging and boosting. Course Activities: Computer labs. Course Note: Students should be familiar with logistic regression. 

    EPI 519 - Evolutionary Epidemiology of Infectious Disease (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course

    Like all living things, pathogens have evolved by natural selection. The application of evolutionary principles to infectious disease epidemiology is crucial to such diverse subjects as outbreak analysis, the understanding of how different genomic combinations of virulence and drug resistance determinants emerge, and how selection acts to produce successful pathogens that balance the costs and benefits of virulence and transmission. The goal of this course is to introduce basic evolutionary concepts, highlighting the importance of transmission to the fitness as illustrated by comparisons of the adaptive process among different sorts of pathogenic microorganisms. Students will also learn the basics of phylogenetic sequence analysis for the study of outbreaks and transmission, and the construction of simple mathematical models that probe the adaptive process. Students outside of HSPH must request instructor permission to enroll in this course.

    GHP 220 - Intro to Demographic Methods (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course 

    This is an introductory level class on the analysis of mortality, fertility and population change. It is required for all master’s and doctoral students in the department of Global Health and Population. Students are introduced to the core literature in this field through lectures, and assigned readings selected from peer-reviewed journals and textbooks. Together, these provide a graduate-level introduction to the principle sources and characteristics of population data and to the essential methods used for the analysis of population problems. The emphasis throughout is on understanding the key processes, models and assumptions used primarily for the analysis of demographic components. Practical training will be given through a required weekly laboratory session, assignments, and a final examination. Examples presented in class and used in assignments are drawn from several countries, combining both developed and developing in assignments are drawn from several countries, combining both developed and developing world realities.

    HPM 206 - Economic Analysis (HSPH: 5 credits, HMS: 4 credits) 

    Fall Semester Course

    Designed to bring students to an intermediate-level understanding of microeconomic theory. Emphasizes the uses and limitations of the economic approach, with applications to public health.

    HPM 261 - Health Care Information Technology Management (HSPH: 2.5 credits, HMS: 2 credits) 

    Spring Semester Course

    This course introduces students to the concepts and knowledge involved in the strategic use of information technology in health care. The course will be a blend between general IT concepts and practical problems facing health care organizations related to the acquisition, use, and management of information technology to assure the safe delivery of quality care in an affordable manner. At the completion of this course, students should be able to:

    -Describe how an IT strategy is developed to align with the overall business strategy
    -Analyze an IT project and determine the technical and human components that contributed to the success or failure.
    -Develop a plan for a system selection, acquisition and implementation.
    -Describe emerging technology trends and the likely impact the trends will have on health care delivery 

    HPM 282 - Innovative Problem Solving & Design Thinking in Healthcare (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Innovative problem solving is a critical skill for healthcare leaders confronting disruptive change and operating in increasingly complex and fast-paced environments. The capacity to innovate is essential to effectively serving patients, improving outcomes and developing sustainable organizations. Design thinking is a disciplined approach to innovation that focuses intensely on the intersection of human needs and values, technical feasibility and strategic viability to create end-user value and pursue market opportunity. Healthcare organizations such as the Mayo Clinic, Kaiser Permanente and the UK's National Health Service (NHS) as well as Ministries of Health in many countries are turning to design thinking to improve patient care and citizen health.

    In contrast to a traditional approach to problem solving that focuses on deciding among known solutions, an innovative approach seeks the best solution possible given available resources, time, and team competencies. Innovative problem solving maximizes learning to reduce uncertainty by focusing on generation of new alternatives, experimentation, and exploration of multiple solutions.

    Learning will occur through a mix of individual and group exercises in class as well as a series of graded and ungraded assignments that enable you to effectively use innovation tools, acquire skills, and adopt mindsets that complement the analytical approaches you have developed in other courses.

    RDS 280 - Decision Analysis For Health/Medical Practice (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course

    This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in developed and developing countries.

    SBS 288 - Qualitative Research Methods in Public Health (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course 

     What students can expect from this course: Qualitative research can be used alone or in combination with quantitative research to investigate public health questions. This introductory course will provide students with an overview of the range of important conceptual and practical issues associated with qualitative research, including providing general familiarity with the design of qualitative studies and conduct of commonly-used qualitative methods. The course begins by examining the variety of potential uses of qualitative methods in public health research and diverse qualitative research approaches.  The course then explores specific topics, including: developing research questions; ethics in qualitative research; “entering” the community to conduct qualitative research; role of theory; ensuring study rigor; selecting and implementing qualitative data collection methods (participant observation, different types of semi-structured interviews and focus groups); writing open-ended questions; sampling; data management and analysis; publishing results; writing research proposals; and considerations for choosing qualitative methods for mixed-methods qualitative or mixed-methods qualitative/quantitative studies. 

    Students should come to class prepared to apply concepts from readings and lectures through participation in class discussions and small group activities that will occur during every class period.  In addition, students will demonstrate application of concepts through completion of written assignments. 

    What this course is not: As this is an introductory course on qualitative research that provides an overview of all pertinent topics to foster familiarity with this research approach as a whole, the course cannot dwell deeply on any one topic.  Students who are looking for in-depth training on a particular step in qualitative research, such as how to analyze their own qualitative dataset, or how to use qualitative coding software, are advised to select a different course.  

    SBS 509 - Health Communication in the 21st Century (HSPH: 2.5 credits, HMS: 2 credits) 

    Spring Semester Course

    This course is designed to provide students in public health and social science with an overview of the theory and research on the role of communication in health in the 21st century. The role of communication in public health will be examined both as a product of everyday interaction with communication platforms including mass media and messages, and its planned use to accomplish particular public health goals. Research examined here looks both at planned and unplanned effects of communication in a variety of health situations representing a range of public health topical concerns. 

  • Massachusetts Institute of Technology

    6.435 - Bayesian Modeling and Inference 

    Spring Semester Course 

    Covers Bayesian modeling and inference at an advanced graduate level. Topics include de Finetti's theorem, decision theory, approximate inference (modern approaches and analysis of Monte Carlo, variational inference, etc.), hierarchical modeling, (continuous and discrete) nonparametric Bayesian approaches, sensitivity and robustness, and evaluation. 

    Prereq: 6.436 and 6.867

    6 .439 - Statistics, Computation, and Applications

    Not Offered 2022-2023

    Data Science Selective

    6. 4102 - Artificial Intelligence

    Fall Semester Course

    Introduces representations, methods, and architectures used to build applications and to account for human intelligence from a computational point of view. Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals. Students taking graduate version complete additional assignments. 

    6 .7900 - Machine Learning

    Fall Semester Course

    Data Science Selective 

    Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.036 or other previous experience in machine learning. Enrollment may be limited.

    6.862 - Applied Machine Learning

    Not offered 2022 - 2023

    Data Science Selective

    Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; classification, regression, reinforcement learning; and methods such as linear classifiers, feed-forward, convolutional, and recurrent networks. Students taking graduate version complete different assignments. Meets with 6.036 when offered concurrently. Recommended prerequisites: 18.06 and 6.006. 

    6.864 - Advanced Natural Language Processing

    Not offered 2022-2023

    Fall Semester Course

    Introduces the study of human language from a computational perspective, including syntactic, semantic and discourse processing models. Emphasizes machine learning methods and algorithms. Uses these methods and models in applications such as syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. Students taking graduate version complete additional assignments.

    6.8610 - Quantitative Methods for Natural Language Processing

    Fall Semester Course

    Data Science Selective Course

    Introduces the study of human language from a computational perspective, including syntactic, semantic and discourse processing models. Emphasizes machine learning methods and algorithms. Uses these methods and models in applications such as syntactic parsing, information extraction, statistical machine translation, dialogue systems. Students taking graduate version complete additional assignments.

    HST 507 - Advanced Computational Biology: Genomes, Networks, Evolution

    Fall Semester Course

    Data Science Selective   

    7.573 - Modern Biostatistics 

    Spring Semester 

    Provides an introduction to probability and statistics used in modern biology. Discrete and continuous probability distributions, statistical modeling, hypothesis testing, Bayesian statistics, independence, conditional probability, Markov chains, methods for data visualization, clustering, principal components analysis, nonparametric methods, Monte Carlo simulations, false discovery rate. Applications to DNA, RNA, and protein sequence analysis; genetics; genomics. Homework involves the R programming language, but prior programming experience is not required. Students registered for the graduate version complete an additional project, applying biostatistical methods to data from their research.

    7.75 - Human Genetics and Genomics 

    Spring Semester 

    Data Science Selective 

    Upper level seminar offering in-depth analysis and engaged discussion of primary literature on the dimensions and phenotypic consequences of variation in human genes, chromosomes, and genomes. Topics include the human genome project; pedigree analysis; mutation and selection; linkage and association studies; medical genetics and disease; sex chromosomes and sex differences; the biology of the germ line; epigenetics, imprinting, and transgenerational inheritance; human origins; and evolutionary and population genetics. Students taking graduate version complete additional assignments. 

    MIT 9.660 - Computational Cognitive Science

    Fall Semester Course

    Introduction to computational theories of human cognition. Focuses on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks include Bayesian and hierarchical Bayesian models, probabilistic graphical models, nonparametric statistical models and the Bayesian Occam's razor, sampling algorithms for approximate learning and inference, and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project.

    14.38 - Inference on Causal and Structural Parameters Using ML and AI 

    Spring Semester 

    Data Science Selective 

    Provides an applied treatment of modern causal inference with high-dimensional data, focusing on empirical economic problems encountered in academic research and the tech industry. Formulates problems in the languages of structural equation modeling and potential outcomes. Presents state-of-the-art approaches for inference on causal and structural parameters, including de-biased machine learning, synthetic control methods, and reinforcement learning. Introduces tools from machine learning and deep learning developed for prediction purposes, and discusses how to adapt them to learn causal parameters. Emphasizes the applied and practical perspectives. Requires knowledge of mathematical statistics and regression analysis and programming experience in R or Python.

    15 .085 - Fundamentals of Probability 

    Fall Semester Course 

    Introduction to probability theory. Probability spaces and measures. Discrete and continuous random variables. Conditioning and independence. Multivariate normal distribution. Abstract integration, expectation, and related convergence results. Moment generating and characteristic functions. Bernoulli and Poisson process. Finite-state Markov chains. Convergence notions and their relations. Limit theorems. Familiarity with elementary probability and real analysis is desirable.

    15.128 - Revolutionary Ventures: How to Invent and Deploy Transformative Technologies

    Fall Semester Course

    Seminar on envisioning and building ideas and organizations to accelerate engineering revolutions. Focuses on emerging technology domains, such as neurotechnology, imaging, cryotechnology, gerontechnology, and bio-and-nano fabrication. Draws on historical examples as well as live case studies of existing or emerging organizations, including labs, institutes, startups, and companies. Goals range from accelerating basic science to developing transformative products or therapeutics. Each class is devoted to a specific area, often with invited speakers, exploring issues from the deeply technical through the strategic. Individually or in small groups, students prototype new ventures aimed at inventing and deploying revolutionary technologies.

    15.363 Strategic Decision Making in the Life Sciences 

    Spring Semester Course 

    Surveys key strategic decisions faced by managers, investors and scientists at each stage in the value chain of the life science industry. Aims to develop students' ability to understand and effectively assess these strategic challenges. Focuses on the biotech sector, with additional examples from the digital health and precision medicine industries. Includes case studies, analytical models, and detailed quantitative analysis. Intended for students interested in building a life science company or working in the sector as a manager, consultant, analyst, or investor. Provides analytical background to the industry for biological and biomedical scientists, engineers and physicians with an interest in understanding the commercial dynamics of the life sciences or the commercial potential of their research. 

    15.780 - Stochastic Models in Business Analytics

    Fall Semester Course

    Data Science Selective Course

    Introduces core concepts in data-driven stochastic modeling that inform and optimize business decisions under uncertainty. Covers stochastic models and frameworks, such as queuing theory, time series forecasting, network models, dynamic programming, and stochastic optimization. Draws on real-world applications, with several examples from retail, healthcare, logistics, supply chain, social and online networks, and sports analytics.

    16.332 - Formal Methods for Safe Autonomous Systems 

    Spring Semester 

    Covers formal methods for designing and analyzing autonomous systems. Focuses on both classical and state-of-the-art rigorous methods for specifying, modeling, verifying, and synthesizing various behaviors for systems where embedded computing units monitor and control physical processes. Additionally, covers advanced material on combining formal methods with control theory and machine learning theory for modern safety critical autonomous systems powered by AI techniques such as robots, self-driving cars, and drones. Strong emphasis on the use of various mathematical and software tools to provide safety, soundness, and completeness guarantees for system models with different levels of fidelity.

    CSB .100 - Topics in Computational and Systems Biology

    Fall Semester Course

    Seminar based on research literature. Papers covered are selected to illustrate important problems and varied approaches in the field of computational and systems biology, and to provide students a framework from which to evaluate new developments. 

    HST 482 - Biomedical Signal and Image Processing 

    Spring Semester Course

    Fundamentals of digital signal processing with emphasis on problems in biomedical research and clinical medicine. Basic principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. Lab projects, performed in MATLAB, provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. Students taking graduate version complete additional assignments.

    HST 504 - Topics in Computational Molecular Biology

    Also listed as MIT 18 .418

    Fall and Spring Semester Course

    Covers current research topics in computational molecular biology. Recent research papers presented from leading conferences such as the International Conference on Computational Molecular Biology (RECOMB) and the Conference on Intelligent Systems for Molecular Biology (ISMB). Topics include original research (both theoretical and experimental) in comparative genomics, sequence and structure analysis, molecular evolution, proteomics, gene expression, transcriptional regulation, biological networks, drug discovery, and privacy. Recent research by course participants also covered. Participants will be expected to present individual projects to the class.

    HST .506 - Computational Systems Biology: Deep Learning in the Life Sciences

    Also listed as MIT  6 .874 and 20 .390

    Spring Semester Course

    Presents innovative approaches to computational problems in the life sciences, focusing on deep learning-based approaches with comparisons to conventional methods. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design (the choice and interpretation of interventions). Focuses on machine learning model selection, robustness, and interpretation. Teams complete a multidisciplinary final research project using TensorFlow or other framework. Provides a comprehensive introduction to each life sciences problem, but relies upon students understanding probabilistic problem formulations. Students taking graduate version complete additional assignments. 

    HST 525 - Tumor Microenvironment and Immuno-Oncology: A Systems Biology Approach 

    Fall Semester Course

    Provides theoretical background to analyze and synthesize the most up-to-date findings from both laboratory and clinical investigations into solid tumor pathophysiology. Covers different topics centered on the critical role that the tumor microenvironment plays in the growth, invasion, metastasis and treatment of solid tumors. Develops a systems-level, quantitative understanding of angiogenesis, extracellular matrix, metastatic process, delivery of drugs and immune cells, and response to conventional and novel therapies, including immunotherapies. Discussions provide critical comments on the challenges and the future opportunities in research on cancer and in establishment of novel therapeutic approaches and biomarkers to guide treatment.

    HST . 582 - Biomedical Signal and Image Processing 

    Spring Semester Course 

    Data Science Selective 

    Fundamentals of digital signal processing with emphasis on problems in biomedical research and clinical medicine. Basic principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. Lab projects, performed in MATLAB, provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. Students taking graduate version complete additional assignments.

    HST .937 - Global Health Informatics to Improve Quality of Care 

    Spring Semester Course 

    Addresses issues related to how health information systems can improve the quality of care in resource poor settings. Discusses key challenges and real problems; design paradigms and approaches; and system evaluation and the challenges of measuring impact. Weekly lectures led by internationally recognized experts in the field. Students taking HST.936, HST.937 and HST.938 attend common lectures; assignments and laboratory time differ. HST.936 has no laboratory.

    HST .953 - Clinical Data Learning, Visualization, and Deployments

    Fall Semester Course

    Data Science Selective

    Examines the practical considerations for operationalizing machine learning in healthcare settings, with a focus on robust, private, and fair modeling using real retrospective healthcare data. Explores the pre-modeling creation of dataset pipeline to the post-modeling "implementation science," which addresses how models are incorporated at the point of care. Students complete three homework assignments (one each in machine learning, visualization, and implementation), followed by a project proposal and presentation. Students gain experience in dataset creation and curation, machine learning training, visualization, and deployment considerations that target utility and clinical value. Students partner with computer scientists, engineers, social scientists, and clinicians to better appreciate the multidisciplinary nature of data science.

    HST .956 - Machine Learning for Healthcare

    Spring Semester Course

    Introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. Topics include causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning and transfer learning. Guest lectures by clinicians from the Boston area and course projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.

    HST .971 - Strategic Decision Making in the Life Sciences

    Spring Semester Course

    Surveys key strategic decisions faced by managers, investors and scientists at each stage in the value chain of the life science industry. Aims to develop students' ability to understand and effectively assess these strategic challenges. Focuses on the biotech sector, with additional examples from the pharmaceutical and medical device sectors. Includes case studies, analytical models, and detailed quantitative analysis. Intended for students interested in building a life science company or working in the sector as a manager, consultant, analyst, or investor. Provides analytical background to the industry for biological and biomedical scientists, engineers and physicians with an interest in understanding the commercial dynamics of the life sciences or the commercial potential of their research.

    HST  .978 - Healthcare Ventures

    Also listed as MIT 15 .367

    Spring Semester Course

    Addresses healthcare entrepreneurship with an emphasis on startups bridging care re-design, digital health, medical devices, and high-tech. Includes prominent speakers and experts from key domains across medicine, pharma, med devices, regulatory, insurance, software, design thinking, entrepreneurship, and investing. Provides practical experiences in venture validation/creation through team-based work around themes. Illustrates best practices in identifying and validating health venture opportunities amid challenges of navigating healthcare complexity, team dynamics, and venture capital raising process. Intended for students from engineering, medicine, public health, and MBA programs. Video conference facilities provided to facilitate remote participation by Executive MBA and traveling students.   

    MAS .772 - AI for Mental Health 

    Spring Semester Course 

    Provides instruction about behaviors and technologies that promote good mental health and foster resilience to stress and anxiety. Covers AI and smart technologies used in diagnosing, monitoring, and treating mental disorders. Students develop a project of their choosing on the topic, which may include novel technology design and evaluation, human subjects studies, machine learning and data analysis, or other investigations that propose and evaluate new ways to use AI for improving mental health.

  • Harvard Kennedy School

    API 222 - Machine Learning and Big Data Analytics 

    Spring Semester Course

    In the last couple of decades, the amount of data available to organizations has significantly increased. Individuals who can use this data together with appropriate analytical techniques can discover new facts and provide new solutions to various existing problems. This course provides an introduction to the theory and applications of some of the most popular machine learning techniques. It is designed for students interested in using machine learning and related analytical techniques to make better decisions in order to solve policy and societal level problems.

    We will cover various recent techniques and their applications from supervised, unsupervised, and reinforcement learning. In addition, students will get the chance to work with some data sets using software and apply their knowledge to a variety of examples from a broad array of industries and policy domains. Some of the intended course topics (time permitting) include: K-Nearest Neighbors, Naive Bayes, Logistic Regression, Linear and Quadratic Discriminant Analysis, Model Selection (Cross Validation, Bootstrapping), Support Vector Machines, Smoothing Splines, Generalized Additive Models, Shrinkage Methods (Lasso, Ridge), Dimension Reduction Methods (Principal Component Regression, Partial Least Squares), Decision Trees, Bagging, Boosting, Random Forest, K-Means Clustering, Hierarchical Clustering, Neural Networks, Deep Learning, and Reinforcement Learning.

    DPI 617 - Law, Order, and Algorithms (HKS: 4 credits; HMS: 4 credits)

    Spring Semester Course

    Data Science Selective

    Data and algorithms are rapidly transforming law enforcement and the criminal legal system, including how police officers are deployed, how discrimination is detected, and how sentencing, probation, and parole terms are set. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. In this course, we examine the often subtle relationship between law, public policy, and technology, drawing on recent court decisions, and applying methods from machine learning and game theory. We survey the legal and ethical principles for assessing the equity of algorithms, describe computational techniques for designing fairer systems, and consider how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises, though prior programming experience is not necessary.

    MLD 636 - Managing Transformations in Healthcare (HKS: 4 credits; HMS: 4 credits)

    Fall Semester Course

    This course will focus on how to successfully manage transformations in the U.S. healthcare system. Transformations in healthcare include changing reimbursement models, initiatives to improve quality, and projects to redesign the care delivery system. The course will work across sectors – non-profit, private, and public sectors, including federal, state and local levels.  The course will use case studies as a major element of each class. The course will begin with a focus on diagnosing the specific challenge and the entity's organizational culture.  Then the course will turn to management tools that can transform the healthcare delivery system. These tools include: 1) managing silos, 2) enhancing the role of clinicians, 3) goal setting and monitoring, and 4) public health campaigns.

    This course is designed for people who may serve in the healthcare delivery system – government agencies, hospitals, community health centers, or public health entities.  The healthcare delivery system is constantly evolving. The ability to manage transformations is critical to improving access, equity, and quality, as well as managing costs.  The successful transformation will balance all four elements.

    The course is taught by a practitioner. Tom Glynn served for 14 years as Chief Operating Officer of Harvard affiliated Partners Healthcare, a network of hospitals and neighborhood health centers. Previously he served as Deputy Commissioner of Public Welfare in Massachusetts, overseeing the state Medicaid Program. And he also served as Chair of the Mayor's Healthcare Commission, reviewing the role of neighborhood health centers and safety net hospitals.  Glynn has a PhD from Brandeis University's Heller School for Social Policy and Management.