phd in data science harvard

Data Science

Students enrolled in the Master of Liberal Arts program in Data Science will develop the skills necessary to analyze, discover, and innovate in a data-rich world. Students gain hands-on experience conducting interdisciplinary data science research.

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The Data Science secondary field is available to any student enrolled in a PhD program in the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences upon approval of a plan of study by the Data Science Program Committee and the director of graduate studies in the student’s home department.

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains. This secondary field offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. Students completing the Data Science secondary field will be exposed to topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

The Data Science secondary field is overseen by the joint leadership of the Computer Science and Statistics faculties and administered by the Institute for Applied Computational Science (IACS). All questions should be directed to Daniel Weinstock , associate director of graduate studies (ADGS) in Applied Computation.

Interested students should consult with their director of graduate studies no later than the first semester of the third year of study and reach out to the ADGS to express interest in applying. The ADGS will provide information about the application, which should include a proposed plan of study.

Applications, which must be approved by the home department DGS, may be submitted twice a year, in the spring semester (deadline: March 1) and fall semester (deadline: October 1) for the following academic term. The ADGS will respond to all applications within one month.

Requirements

Each student’s plan of study for the secondary field will include:

1. Core Courses

At least 3 of the Data Science core courses:

• AC 209a*        Data Science 1: Introduction to Data Science
• AC 209b*         Data Science 2: Advanced Topics in Data Science
• AM 207           Advanced Scientific Computing: Stochastic Methods for    Data Analysis, Inference, and Optimization
• CS 207            Systems Development for Computational Science
• AC 221            Critical Thinking in Data Science

*Students can, with the permission of the program committee, count CS 109a/b in place of AC 209a/b.

2. Electives

Two electives in Computer Science or Statistics. Students may choose from a offered by the Computer Science and Statistics faculties.

Alternatively, students may choose to satisfy the elective requirement by taking additional core courses. Students may also choose, as a substitute for one elective, either AC 297r, the IACS Capstone Project course, or AC298r, the interdisciplinary seminar in Computational and Data Science.

3. Oral Examination

As a final requirement, an oral examination by a faculty committee on a data science research topic. Typically students will present on a part of their dissertation thesis work. Students will be evaluated on their ability to explain their work to the interdisciplinary IACS audience and their command of the Data Science methods they have used. The oral presentation should explain how the courses taken to satisfy the Data Science secondary field impact their research.

Advising and Other Activities

Daniel Weinstock, ADGS in Applied Computation, will be responsible for frontline advising of students in the Data Science secondary field. Students interested in the secondary field are encouraged to reach out to Dr. Weinstock before submitting an application. Students enrolled in the secondary field will be able to participate in the activities of the IACS community, including technical and interdisciplinary colloquia, skill-building workshops, and tech-treks to local companies working to apply computation and data science in many different domains.

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Data Science

The Harvard Faculty of Arts and Sciences is pleased to offer the Master of Science (SM) degree in Data Science. The degree, under the joint academic leadership of the  Computer Science  and  Statistics  faculties and administered through the  Institute for Applied Computational Science (IACS) at the John A. Paulson School of Engineering and Applied Sciences (SEAS), trains students in the rapidly growing field of data science.

The Statistics Department is heavily involved in the  Harvard Data Science Initiative  (DSI). DSI is a cross-campus effort to develop important new data science methods and to better harness the power of data science in research.

Data Science at Harvard

  Where is Data Science happening at Harvard Univeristy?

Harvard Data Science Initiative

Seas computer science, institute for applied computational science (iacs), department of statistics, national statistics,   learn more about the program.

HDSI Postdoctoral Fellowship Program

Overview of the fellowship program.

The Harvard Data Science Initiative (HDSI) is seeking applications for its flagship Harvard Data Science Postdoctoral Fellows Program for the 2024-2025 academic year. The normal duration of the Fellowship is two years. Fellows will receive a generous salary as well as an annual allocation for research and travel expenses. 

We are looking for researchers whose interests are in data science, broadly construed, and including researchers with a primarily methodological focus as well as researchers who advance both methodology and application. Fellows will be provided with the opportunity to pursue their research agenda in an intellectually vibrant environment with ample mentorship. We are looking for independent researchers who will seek out collaborations with other fellows and with faculty across all schools of Harvard University.

We recognize that strength comes through diversity and actively seek and welcome people with diverse backgrounds, experiences, and identities.

Funding Priorities

The hdsi postdoctoral fellows program will support outstanding researchers whose interests relate to the following themes:.

• Methodological foundations including, for example, causal inference, data systems design, deep learning, experimental design, modeling of structured data, random matrix theory, non-parametric Bayesian methods, scalable inference, statistical computation, and visualization.
• Development of data science approaches tailored to analytical challenges in fields that span the full intellectual breadth of Harvard’s faculties.  To give some purely illustrative examples, these fields include health sciences (e.g., life and population sciences), earth systems (e.g. climate change research); society (e.g., data that can affect the experience of individuals or policy); and the economy (e.g., blockchain, labor economics, digital markets). This list is by no means exhaustive.
• Successful applicants will be expected to lead their own research agenda while collaborating with others, including members of Harvard’s faculty, and to contribute to building up the field of data science. The Fellows program offers numerous opportunities to engage with the broader data science community, both inside and outside Harvard, including through seminar series, informal lunches, mentoring opportunities, opportunities for fellow-led programming, and other networking events. 

Available Funding

Stipend: $85,000 is available in salary support per year for an initial two-year appointment. Appointments may be extended for a third year with partial support, budget and performance allowing.  

Travel: An additional $10,000 will be allocated for research and travel expenses each year.

Relocation expenses: Funding is available to support relocation expenses for fellows moving to the Cambridge, MA area.  Please inquire about the availability of funding with an email .  Requests for funding are evaluated separately from a candidate’s application. 

Appointment dates: While most incoming Fellows begin their appointment at the beginning of September, earlier or later start dates can be arranged to accommodate Fellows’ needs. If an earlier or later start date is of interest, please let us know at any point during the application process. 

Deadline : November 13, 2023

Application Information

Required application documents include:.

• A cover letter and up to five (and at least two) Harvard faculty members from across the university with whom the applicant would like to work. Candidates are not expected to already have formed a relationship with prospective mentors, and should not contact faculty directly prior to submission of application. The HDSI will reach out to faculty mentors for the most meritorious applications.
• A statement of research interests of up to three pages that succinctly describes the applicant’s research interests. The statement should explain the importance and potential impact of this research.  
• Up to three representative papers, which can include working papers.
• The HDSI is committed to supporting research that promotes fair and responsible data science.  We ask that you submit an impact statement (<500 words) that speaks to the ethical issues and potential societal impact of your proposed work, areas of interest, or data science more broadly.  This requirement is modeled in part on a similar requirement by NeurIPS , and applicants may find this guide helpful in thinking about how to write this statement. 
• Names and contact information for at least two and up to five references (the application is only complete when two letters of reference have been submitted, so please contact referees early in the application process). Referees will be provided with a link to the submission portal. 

All materials should be submitted as PDF documents.

Eligibility Criteria

Applicants should be intellectually curious researchers at an early stage of their scholarly career. Applicants are required to have a doctorate in a related area by the expected start date of their fellowship. Applicants should have demonstrated a capacity for independent work, and will be expected to engage with researchers and faculty and participate in activities convened by the HDSI.

Additional Information

A first assessment of applicants will be made in late November with decline notifications coming shortly thereafter. Final decisions are expected to be made in January 2024 with offer letters being extended some time at the end of January into early February. 

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.

For questions about the Harvard Data Science Initiative Postdoctoral Fellows Program, please contact us here .

Student Profile

Graduate student profile: maria emilia mazzolenis, helping seas combine data science with ethics.

Maria Emilia Mazzolenis, S.M. '24, in data science (Eliza Grinnell/SEAS)

Maria Emilia Mazzolenis knows that to change a system, you first must understand it. Want to understand the needs of your fellow graduate students? Join a graduate advisory committee. Want to know what students are actually learning in their classes? Become a teaching fellow. Want to learn about the entrepreneurial world? Join a startup. Want future students to study the ethical implications of their research? Take on a new fellowship focused on changing the curriculum.

“I am deeply committed to the causes I believe in, and devoting my time and energy to them has been immensely rewarding,” said Mazzolenis, who’s about to finish her master’s degree in data science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). “That’s allowed me to contribute to reshaping the integration of ethics into technical education, which has the potential to impact the next generation of data and machine learning scientists, and is something I’m really honored to be working towards.”

Originally from Buenos Aires, Mazzolenis arrived at SEAS after majoring in economics and psychology at the University of North Carolina at Chapel Hill. Her first exposure to data science and machine learning came as an undergraduate, and she quickly realized data science was the right field for her graduate degree. She joined the SEAS master’s program almost immediately after graduating UNC.

“The program was the perfect combination of my interdisciplinary interests, and having access to the Harvard-MIT community proved to be incredibly beneficial,” said Mazzolenis, who’s cross-registered as a grad student in computer science at MIT. “The program had rigorous technical training, but also a focus on real-world applications. I knew that research was highly emphasized at SEAS, and I’d been drawn to research since I was a child. It all seemed like a great fit in terms of my academic and professional interests.”

It didn’t take long for Mazzolenis to become a student leader. In her first semester, she became an Institute for Applied Computational Science Social Fellow and co-leader of an admissions and funding initiative for the Graduate Advisory Committee (GAC) on Diversity, Inclusion, and Leadership in Applied Computation. The following spring, she became co-chair of the GAC.

“In order to truly make an impact, you have to know who you’re working with, have contact with students, and understand the intricacies of the technical curriculum,” she said.

The summer after her first year at SEAS, Mazzolenis gained a new level of understanding of the importance of socially conscious technical training. She was selected for the Fellowships at Auschwitz for the Study of Professional Ethics, a program dedicated to examining contemporary professional ethics through an exploration of how technologists and engineers enabled and executed Nazi policies. She returned to campus with an even greater passion for the intersection of ethics and data science.

“I think everyone is aware that machine learning has immense potential to shape the world, but we can’t forget that shaping the world also comes with responsibilities,” she said. “As data scientists, if we’re not actively thinking about the impact that our actions can have, it can feel like we’re just coding behind a computer screen, disconnected from real-world implications and applications. We need to consider that our code can have an impact, both positive and unfortunately negative, in our community, the people we hope to help, and even on those who we were not originally thinking about.”

While at SEAS, Maria Emilia Mazzolenis has been an Institute for Applied Computational Science Social Fellow, co-chair of the Graduate Advisory Committee, an AI Ethics Pedagogy Fellow, and teaching fellow in both advanced data science and data science master's capstone project classes (Eliza Grinnell/SEAS)

Last fall, Mazzolenis became an AI Ethics Pedagogy Fellow at SEAS. Combined with her roles as a teaching fellow in both an advanced data science class and the data science capstone project course, she’s directly influenced how ethics are integrated into data science classes.

“I’m working on adapting the curricula we present students,” she said. “I’m starting from the ground up, and I am identifying essential ethical concepts we need to address, spanning societal, environmental, technical, and legal considerations. I’m collaborating closely with key stakeholders to ensure the class content and assignments cover both technical and ethical concepts.”

Mazzolenis’s own research initiatives have covered a range of topics. For a capstone project, her team examined the role online questionnaires and data from wearable technology can play in predicting suicidal ideation. She’s also researched the ways in which healthcare professionals may influence their patients’ decisions to continue or discontinue in vitro fertilization treatment depending on how they communicate the probabilities of treatment outcomes derived from machine learning models. She’s also collaborating with doctors at Massachusetts General Hospital to evaluate the current state of machine learning applications for studying chronic pain and headaches, and she is also acting as the principal investigator in training for a research project that uses computer vision for healthcare-related applications.

“Machine learning and data science are extremely applicable to a wide variety of disciplines, which is what drew me to them,” Mazzolenis said. “A lot of the research that I do is at the intersection of machine learning and psychology, healthcare, or economics. I’m able to integrate those areas because of the progression of my academic path.”

Wherever her professional career goes next, she knows SEAS has prepared her for success.

“The program was indeed challenging, but it helped me grow as a machine learning engineer, a researcher, and as a person,” she said. “I’ve made a lot of connections with people in the program, professors, administrators, and other professionals in the field. SEAS has not only equipped me with technical skills but also fostered a sense of leadership and innovation that will certainly guide me in my professional and personal life.”

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MS in Data Science

MSDS students choose among the many introductory graduate courses offered to students in the PhD program. These courses cover areas of computer science, optimization, linear algebra and statistics for students that have not had prior exposure to this required course work. Master’s students are fully integrated in the academic activities of the department alongside the PhD students.

Students must complete the required 5 core courses, 4 electives, and a final project to complete the program. There are also three foundational courses that students can test out. For the students who test out of foundational courses, the minimum number of courses taken in the program is 9. For the students who take all foundational courses, it is 12. These foundational courses can be taken in the summer before the program starts. Finally, students will be able to engage in a variety of opportunities across the Data Science Institute research programs and partnerships during their residency in the program.

The Curriculum

Foundational courses:.

Interested students will have the opportunity to test out of each of the 3 foundation courses below. Each of the courses will be offered in the late summer and offered online before the start of the fall quarter.

• Computational Foundations for Data Science
• Mathematical Foundations for Data Science
• Statistical Foundations for Data Science

Core Courses:

• Introduction to Data Science
• Systems for Data and Computers/Data Design
• Data Interaction
• Introduction to ML and AI  or Foundations of Machine Learning and AI Part I
• Responsible Use of Data and Algorithms

Four graduate-level electives can be selected from a wide variety of courses in Data Science, Computer Science, Statistics and across the University.

The online application portal will begin accepting applications for Fall 2024 admission in early Fall 2023. To ensure full consideration, applicants should apply by the deadline. The program may accept applications after the deadline if the cohort is not filled.