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Data science is an area of study within the Harvard John A. Paulson School of Engineering and Applied Sciences. Prospective students apply through the Harvard Kenneth C. Griffin Graduate of School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select “Engineering and Applied Sciences” as your program choice and select “SM Data Science” in the area of study menu.

Data is being generated at an ever-increasing speed across all aspects of modern life. The data science master’s program combines computer science and statistics to train students how to analyze, contextualize, and draw insights from that data. The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.

The program focuses on hands-on research projects. In many of the program’s courses you will demonstrate your mastery of the material covered in the course by applying those methods in a final project. In addition, you will have a deeper research experience by completing a master’s thesis on a computational project under faculty supervision or through the Capstone Project course—in which teams of students work on real-world projects sourced from industry partners, such as working with Spotify on recommender systems and with the Massachusetts Bay Transportation Authority on optimum bus scheduling.

Graduates of the program have taken key positions at large technology companies, major financial institutions, and emerging startups. Others have gone on to doctoral studies in computer science and statistics.

Standardized Tests

GRE General:  Not Accepted

APPLICATION DEADLINE

Questions about the program.

From PhD to Data Scientist: 5 Tips for Making the Transition

Originally posted by Douglas Mason with Valerie Bisharat

Douglas Mason, Harvard Physics PhD, Insight Fellow, and Data Scientist at Twitter, outlines his advice on transitioning from academia to data science.

About a year ago, I began my unexpected but rewarding transition to industry after completing my physics PhD. My dream for years before that had been to work as a physicist in the National Laboratories, but when the time came to do so, that fate just didn’t feel right.

Before Insight, I had virtually zero knowledge of how to score a job at a tech company like Twitter, Facebook or Google. Instead, what I carried with me before, during, and after the program was a relentless enthusiasm. I’m certain that was key to my success, leading to my current role as a data scientist at Twitter.

Here are my top tips on transitioning from academia to the tech industry:

1. Show that you want it. I’m now part of the interview process to select new hires at Twitter. You wouldn’t believe how many people come through here saying, “Well, the academic thing just isn’t working out for me. I guess I’ll do this now.” We can tell on your resume and in the interview if you didn’t put in any effort to look impressive. If that’s the case, what’s going to happen when we hire you? We want people who actively want to be here.

2. Emphasize the parallels between your thesis work and potential professional projects. My data science career is hugely impacted by having written a thesis. Remember that as a PhD you’ve essentially conducted a five-year project that you were totally responsible for. You’re also constantly giving talks, presentations and summaries of your research, which is exactly what my job involves as a data scientist. Work projects are essentially like an entire thesis compressed into one or two quarters. Only now, you have a bunch of colleagues who will help you figure the problems out. In your resume and the interviews, find ways to draw parallels between your work experience from your PhD and the responsibilities outlined in the job description.

3. Take time to practice your hard skills. They aren’t easy.

• Study your algorithms and data structures from a lot of different sources.
• Give yourself time to learn recursive programming — it’s a different way of thinking, so you can’t do it in a night.
• Review your statistics . Know your different regression types , as well as p values and t-tests .
• Know how to calculate expectation values in combinatorics problems.
• Learn and work with SQL .
• If you’re using Matlab, Fortran, etc. it’s time to make the transition to Python or R . Build fun side projects to practice your skills.

4. Interact with the tech community as much as possible. Learn the language, the lingo, how people talk, how people think. Learn what people value. If you go in having done none of this, you will sound like an alien. You have to show, by doing , that you’re willing to learn the vernacular.

5. Choose a company with an employee size that fits your needs. I love that Twitter is a medium sized company — it’s big enough that I can learn a lot from the experts around me, but small enough that I can have a big impact. For me, that’s an ideal balance. Consider your desired mix, and get lots of advice from veterans along the way.

Remember, this process will throw lots of unknowns at you. In fact, the unknown is the only constant on this path. Get comfortable with that, stay focused, be positive and work hard. Going out on a limb is usually worth it.

Interested in transitioning to a career in data engineering? Find out more about the Insight Data Engineering Fellows Program in New York and Silicon Valley, apply today, or sign up for program updates.

Already a data scientist or engineer? Find out more about our Advanced Workshops for Data Professionals. Register for two-day workshops in Apache Spark and Data Visualization , or sign up for workshop updates.

Written by Insight

Insight Fellows Program - Your bridge to a thriving career

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Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

Information Request Form

Ph.d. specialization committee.

• View All People
• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• The Fu Foundation School of Engineering and Applied Science Vice Dean of Research
• Tang Family Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• Data Science Institute Interim Director
• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Academic Affairs

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

The application deadline for Fall 202 4 Admissions was Tuesday, December 5, 2023, 5pm ET. Applications for Fall 2025 Admissions will open in late September 2024.

Our Fall 2024 PhD Admissions Information Session took place Thursday, October 26 at 1pm.

The Committee welcomes applications from candidates with relevant undergraduate/master’s degrees and candidates with work or research experience in data science. Relevant degrees include mathematics, statistics, computer science, engineering, and other scientific disciplines that develop skills in drawing inferences or making predictions using data. Coursework or equivalent experience in calculus, probability, statistics and programming are required.

Please visit the  GSAS Application Resource Center  for more information on the application process. Please see our FAQs page for additional information.

For a list of all of our faculty research areas,  please review our Areas and Faculty page.

PhD in Data Science

First Year Requirements

The standard first-year program requires students to complete nine courses: four required courses (1-4 below); one elective either in mathematical foundations or scalability and computing (pick from either 5 or 6); and finally four other electives that can come from proposed courses in data science or existing graduate courses in Computer Science or Statistics. Some students, after consulting with the committee graduate advisor, might decide to take the nine courses over the first two years.

Required courses:

• Foundations of Machine Learning and AI Part 1
• Responsible Use of Data and Algorithms
• Data Interaction
• Systems for Data and Computers/Data Design
• Foundations of Machine Learning and AI Part 2 
• Data Engineering and Scalable Computing

Synthesis project

Students will take courses during the first two years after which they focus primarily on their research. A milestone in this transition is completion of a synthesis project before the end of the second year in the program. Thesis projects can be done in partnership with any of DSI affiliates, and aims to meaningfully connect PhD students to their chosen focus areas.

Thesis Advisor and Dissertation Committee

Students typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student, after consultation with their advisor, shall establish a thesis committee of at least three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science.

Proposal Presentation and Admission to Candidacy

By the end of the third year, students should have scheduled and completed a proposal presentation to their committee, in order to be advanced to candidacy. The proposal presentation is typically an hourlong meeting that begins with a 30-minute presentation by the student, followed by a question and discussion period with the committee.

Dissertation Defense

The PhD degree will be awarded following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

DiscoverDataScience.org

Do You Need a PhD to Become a Data Scientist?

Created by aasif.faizal

However, despite the exceptional job statistics for data scientists, many are intimidated by the seemingly extensive schooling needed to work as a data scientist earning at that salary level. It’s true that the more advanced a degree one has, the more in-demand they will be for employers, especially for positions at the highest levels of data management and consulting. Some people assume that without a PHD in data science, there will not be the same number of excellent opportunities that are so widely publicized.

So, do you need a PhD to become a data scientist, taking advantage of this booming industry? In short, no!

Those who hold master’s degrees in data science are also in competitive positions for top-paying jobs in the field. The truth is, data science is a field in which what matters most is what you actually know how to do – in other words, employers will be concerned with academic credentials insofar as they demonstrate that you have acquired the requisite knowledge to serve as an expert on their team.

(Technically, one doesn’t need a degree at all to become a data scientist, although it is by no means easier to receive the necessary education independently, and it is much harder to prove your abilities without a degree.)  Since a PhD asserts the highest level of training and education, those who hold them are in the very highest demand for top data science jobs . However, you can certainly become a data scientist without a PhD, and a master’s degree should also take you quite far, asserting a very high level of competence and ability.

Included in this article:

• Degrees, Bootcamps, and Certifications

Bachelor’s in Data Science

Master’s in data science, associate degree in data science, data science certificate programs, data science bootcamps, degrees, bootcamps, and certifications for aspiring data scientists.

This article will discuss different degree and certification opportunities for aspiring data scientists to help you find the path that will meet your existing level of education and areas of interest. To learn more about the educational options that are out there, read on.

If you are interested in pursuing a data science PhD, there are numerous excellent programs that will help you gain the top-level expertise you are seeking. Take a look at our guide for more about data science PhDs , with information about available programs, and academic requirements.

If you do not currently hold a bachelor’s degree and feel drawn to the field of data science, getting your bachelor’s in data science can be an excellent way to get your grounding in key concepts in the field. You’ll receive your introduction to the interdisciplinary approach of data science, learn coding languages that are foundational to any data science profession, and explore key issues in the ever-evolving world of big data.

To be clear, a bachelor’s in data science is not a prerequisite to attend a master’s program. (Those who are considering a master’s in data science should take note.) However, starting your education at the bachelor’s level will allow you to start building your competence and insight into the field early, opening up new dimensions of the subject and making you an especially qualified and capable data scientist in the future. You may even be able to find internships or entry-level employment opportunities that will give you your first work experience in the field before you’ve even received your degree. If you’re impatient to break into the arena of big data, finding a bachelor’s program with a major or concentration in data science is the perfect way to hit the ground running.

Top Jobs for Those Who Hold a Bachelor’s in Data Science

While a master’s degree in data science will open up top-ranking job opportunities in the field, there are numerous jobs that can give those who hold bachelor’s degrees in data science a leg up in their career. These include the following:

• Database administrator
• Junior data analyst
• Junior data engineer
• Market research analyst

These entry-level roles can offer an on-the-ground education for aspiring data scientists, helping them figure out an area of specialty and build the skills that will allow them to thrive in a master’s program.

To learn more about Bachelor’s in Data Science programs, with information about academic requirements, tuition, community college options, and more, take a look at our guide here.

When people discuss the many lucrative opportunities that are proliferating in the field of data science, they are most often describing the professional landscape for those who hold master’s degrees. Indeed, a master’s in data science is likely to give you the skills and experience employers are looking for to carry out large-scale operations related to data. If you’re looking to truly take advantage of the tremendous opportunities currently available to data scientists, aiming to eventually receive your master’s degree in data science is the most reliable path to a high-earning career.

Requirements for a Data Science Master’s Degree

One does not need to hold a bachelor’s degree in data science in order to apply for a master’s program. Those who pursue advanced degrees in data science typically have majored in mathematics, statistics, or computer science in undergraduate college, though it is possible to enter a master’s program with a prior degree in a totally unrelated field, such as history or political science. No matter what type of bachelor’s degree you hold, there’s no reason to think it won’t be possible to become a data scientis t.

What’s most important for prospective data science master’s students to know is that you will be expected to enter with a prior understanding of the fundamentals of:

• Programming (including knowing at least one programming language, such as Python or Java)
• Data analysis
• Single-variable calculus

There are many ways to build up the knowledge to get yourself up to speed before starting a master’s program, including summer courses and bootcamps (more information on those later).

Some master’s programs even include preliminary course work for those who are not yet acclimated to the baseline competencies expected of data science students. These can be a great resource for students whose academic backgrounds did not emphasize data science, statistics, or computer science. However, be ready for a great deal of work, as there will be a point in your master’s program where you will be expected to perform the same operations as classmates who arrived already versed in the materials.

Specializations

Since the field of data science is so vast, many decide to focus their master’s degree work on a particular area of specialty, which is likely to help you define a particular career path and enter at a high level. It can be helpful to have this area of expertise in mind before applying to a program, as some schools offer more targeted course work than others.

Some of the most popular specializations within the field of data science include the following:

• Business intelligence (B.I.)
• Cloud computing
• Data analytics
• Data engineering
• Data visualization
• Machine learning (M.L.)

These are just a few of the top focus areas within the field of data science. For more complete information on each topic, visit our guide to data science focus areas .

Top Jobs for Those Who Hold a Master’s in Data Science

Some of the roles available to master’s degree holders are advisory positions who can have a great deal of influence in company decisions, translating key data findings to team leaders that will inform major actions. In short, these are jobs that will require a great deal of expertise, as they will ask you to take a great deal of responsibility.

Some of the top positions for those who hold master’s degrees in data science include the following:

• Business intelligence (BI) analyst
• Data visualization analyst
• Database architect
• Financial manager
• Machine learning (ML) engineer
• Senior data engineer

These are just a few of the high-ranking job titles in the world of big data. Many of these correspond with one’s chosen area of specialty in their degree program. It is also worth noting that some who hold these positions work their way up from lower-ranking roles on their team, building their expertise and proving their competence until they become the trusted authority on staff for matters related to data, analytics, and programming.

Find more information about master’s in data science programs , including standard course offerings, part-time options, and details on specific programs in your area.

If you do not hold any higher education degree and would like to take your first steps in the field right away, it is possible to get your associate’s degree in data science, which will help you build the foundation from which you can pursue a career in big data. It is important to note that these programs alone are not likely to make you a candidate for the high-level data science jobs that draw many to the field, but they will give you a baseline knowledge in the discipline as a whole and typically will give you transferable course credits if you choose to pursue a bachelor’s degree.

Topics in associate’s degree programs tend to include introduction to programming, database statistics, software design, calculus, business fundamentals, and more. These units will be a great opportunity to help you get a grasp on the variety of job opportunities in the world of big data and could lead you to determine your area of focus when pursuing a bachelor’s or master’s degree.

Take a look at our complete guide on associate degree programs in data science .

If you are looking to build proficiency in some of the fundamental skills needed to become a data scientist but don’t have the time to take on a full-blown degree program, there are many certification programs that can provide a basic orientation for you in the field of big data. These programs are also excellent options for those who do hold advanced degrees but would like to advance a particular area of focus that they may not have studied while in school.

Typically, certificate programs are far less intensive than degree programs, making them advantageous for those who have too many other responsibilities to enroll in graduate school. They also tend to be more affordable than more rigorous degree programs.

Certificate programs in data science can be a great option for dedicated individuals who have a very focused agenda for pursuing further knowledge of the field in the first place. However, it is important to keep in mind that simply holding a certification in data science is unlikely to give you the qualifications needed for the top-paying jobs in big data, which tend to call for the level of expertise imparted by a master’s or PhD program.

Use our comprehensive guide to learn more about data science certificate programs including career building opportunities, admission requirements, and more .

If there is a particular skill set you are hoping to develop or an entry-level, project based position you are hoping to find in the world of big data, then a bootcamp could be a great option for you. There are bootcamp offerings in a wide variety of subjects and some are designed to set you up with your first work experience in a data science related position. As with certificate programs, it’s important to know that these bootcamps will not alone give you the expertise needed to launch a career working in big data at the managerial or executive level, but they can be a path to your first work opportunities in the field, helping you cultivate a particular ability in coding or data engineering in a minimum of time.

Boot camps tend to be time-intensive but short, held in courses that typically last from eight to 15 weeks. Many of these programs have turned to online options so that students can attend from all over, but it’s important to set aside the proper amount of time to do the work they require – despite their convenience, boot camps aren’t easy, and in order to get the most out of their course offerings, you’ll be expected to put in the time. For this reason, some people find that in-person bootcamps are a more productive use of your time, and given that there are still so many courses out there, you are more than likely to find an in-person course in your area.

Visit our complete guide to learn more about data science bootcamp options , survey the course offerings that are out there, and find out if this path is the right one for you.

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PhD Program

Requirements for doctor of philosophy (ph.d.) in data science.

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Course Requirements

https://datascience.ucsd.edu/graduate/phd-program/phd-course-requirements/ 

Research Rotation Program

https://datascience.ucsd.edu/graduate/phd-program/research-rotation/

Preliminary Assessment Examination

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

The preliminary assessment is an oral presentation that must be completed before the end of Spring quarter of the second academic year. Students must have a GPA of 3.0 or above to qualify for the assessment and have completed three of four core required courses . The student will choose a committee consisting of three members, one of which will be the HDSI academic advisor of the student. The other two committee members must be HDSI faculty members with  0% or more appointments; we encourage the student to select the second faculty member based on compatibility of research interests and topic of the presentation. The student is responsible for scheduling the meeting and making a room reservation. 

The student may choose to be evaluated based on (A) a scientific literature survey and data analysis or (B) based on a previous rotation project. The student will propose the topic of the presentation. 

• If the student chooses the survey theme, they should select a broad area that is well represented among HDSI faculty members, such as causal inference, responsible AI, optimization, etc. The student should survey at least 10 peer-reviewed conference or journal papers representative of the last (at least) 5 years of the field. The student should present a novel and rigorous original analysis using publicly available data from the surveyed literature: this analysis may aim to answer a related or new research question.
•  If the student chooses the rotation project theme, they should prepare to discuss the motivation for the project, the analysis undertaken, and the outcome of the rotation. 

For both themes, the student will describe their topic to the committee by writing a 1-2 page proposal that must be then approved by the committee. We emphasize that this is not a research proposal. The student will have 50 minutes to give an oral presentation which should include a comprehensive overview of previous work, motivation for the presented work or state-of-the-art studies, a critical assessment of previous work and of their own work, and a future outlook including logical next steps or unanswered questions. The presentation will then be followed by a Q&A session by the committee members; the entire exam is expected to finish within two hours. 

The committee will assess both the oral presentation as well as the student’s academic performance so far (especially in the required core courses). The committee will evaluate preparedness, technical skills, comprehension, critical thinking, and research readiness. Students who do not receive a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program . If the lack of preparation is course-based, the committee can require that additional course(s) be taken to pass the exam. If the lack of preparation is research-based, the committee can require an evaluation after another quarter of research with an HDSI faculty member; the faculty member will provide this evaluation. The preliminary assessment must be successfully completed no later than completion of two years (or sixth quarter enrollment) in the Ph.D. program. 

The oral presentation must be completed in-person. We recommend the following timeline so that students can plan their preliminary assessments:

• Middle of winter quarter of second year: Student selects committee and proposes preliminary exam topic.  
• Beginning of spring quarter of second year: Scheduling of exam is completed. 
• End of spring quarter of second year: Exam. 

Research Qualifying Examination and Advancing to Candidacy

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

A petition to the Graduate Committee is required for students who take UQE after the required 12 quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS in Data Science track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense oral presentation and examination to the Dissertation Committee consisting of five or more members approved by the graduate division as per senate regulation 715(D).  One senate faculty member in the Dissertation Committee must have a primary appointment in a department outside of HDSI. Partially appointed faculty in HDSI (at 25% or less) are acceptable in meeting this outside-department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of Data Science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715(D) requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

The dissertation topic will be selected by the student, under the advice and guidance of Thesis Adviser and the Dissertation Committee. The dissertation must contain an original contribution of quality that would be acceptable for publication in the academic literature that either extends the theory or methodology of data science, or uses data science methods to solve a scientific problem in applied disciplines.

The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Data Science. The final examination will be conducted in two parts. The first part consists of a presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members.

Special Requirements: Generalization, Reproducibility and Responsibility A candidate for doctoral degree in data science is expected to demonstrate evidence of generalization skills as well as evidence of reproducibility in research results. Evidence of generalization skills may be in the form of — but not limited to — generalization of results arrived at across domains, or across applications within a domain, generalization of applicability of method(s) proposed, or generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirement may be satisfied by additional supplementary material consisting of code and data repository. The dissertation will also be reviewed for responsible use of data.

Special Requirements: Professional Training and Communications

All graduate students in the doctoral program are required to complete at least one quarter of experience in the classroom as teaching assistants regardless of their eventual career goals. Effective communications and ability to explain deep technical subjects is considered a key measure of a well-rounded doctoral education. Thus, Ph.D. students are also required to take a 1-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.

Obtaining an MS in Data Science

PhD students may obtain an MS Degree in Data Science along the way or a terminal MS degree, provided they complete the requirements for the MS degree.

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How to Write a Good Results Section

Effective results sections need to be much more than a list of data points given without context. .

Nathan Ni holds a PhD from Queens University. He is a science editor for The Scientist’s Creative Services Team who strives to better understand and communicate the relationships between health and disease.

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The results section details the findings of a given study. The primary difference between the results section and the discussion section is that the results section does not delve into hypothetical interpretation. However, people are often taught in school that a results section should only present data and include nothing else. This goes too far—a results section that is only a list of numbers and facts is confusing, boring, and difficult to read. When presenting their results, authors need to exercise discretion and nuance. Most importantly, they need to provide context for their numbers and comparative reference points for their data.

Why Did the Authors Want This Data?

Before jumping right into the dataset, authors should explain the rationale behind why they chose to generate the dataset. While there is no need to overly rehash the introduction, the reader still benefits from a brief primer on what the authors sought to examine through this particular experimentation and the resulting data.

Here are some examples of what this means in practice. Look at the following passage:

“In order to test the plausibility of this model, we implement a Brownian dynamics simulation based on prior modeling of meiotic chromosome movement and pairing.” 1  

The authors use the first clause—“In order to test the plausibility of this model”—to explain why the second clause—“we implement a Brownian dynamics simulation”—took place. 

Similarly, consider another example : 

“MRGPRX4 engages intracellular G q to induce calcium flux. Using calcium imaging as a readout, we screened 3808 drugs for activity against human embryonic kidney (HEK) 293 cells expressing MRGPRX4 (the Ser83, rs2445179 variant).” 2  

Here, the first sentence clearly sets up why the authors employed calcium imaging to study drug activity against HEK293 cells.

Why Did the Authors Choose These Parameters?

In addition to why they chose to perform a certain experiment, it is also important for scientists to tell their audience why they examined selected specific parameters or variables in their experiments. Too often, authors will highlight or emphasize numbers in a sentence without contextualizing them. Based on the syntax, the reader recognizes that these numbers are significant, but does not immediately understand why. 

Biologist Gary T. ZeRuth from Murray State University, in a recent article in Islets , provides an example of how to contextualize experimental parameters and results:

“Given that INS1 cells are normally maintained in 11.1 mM glucose, expression of  Ins2, MafA , and  Glis3  was measured in INS1 cells cultured in 3 mM glucose (low glucose), 11.1 mM glucose, and 25 mM glucose (high glucose). Graded levels of expression were observed with expression at 11.1 mM glucose being more similar to low glucose conditions than chronically elevated glucose for all three genes.” 3  

Here, ZeRuth and his colleagues annotate the three parameters—3mM, 11.1mM, and 25mM glucose—as low, normal, and high concentrations. The authors then present their results within this framework: Gene expression at 11.1mM was more similar to that found at low glucose concentrations than high ones. In this way, they show the effect of high glucose versus low glucose and examine the validity of 11.1mM as a baseline. 

What Is the Right Level of Detail for the Data? 

It is important that data is not just dumped en masse onto the reader, but presented in a curated and meaningful way. To do this, researchers have to decide on an appropriate level of detail that provides sufficient evidence and is not overwhelming. In the prior example, ZeRuth and his colleagues did not provide gene expression as an empirical value, but rather as a relative one. In this circumstance, it was more important to emphasize gene expression changes in difficult glucose experiments than to say that gene A expression was 2.3 in high glucose and 1.2 in low glucose. 3  

One good way of determining the right level of detail is to keep the figures in mind when writing the results section. Many times, authors will use the text only as a vehicle to introduce the figures. However, the proper way is actually the opposite, where the figures provide additional depth and detail for the text. It is important that the text is able to stand alone from a narrative and argumentation perspective, while the figures present information that does not translate well to text format, such as high volumes of numbers, multi-parameter comparisons, and more complex statistical analyses.

As an example, consider the following passage:

“Several phosphomonoester compounds including fospropofol {EC 50 : 3.78 nM [95% confidence interval (CI): 1.82 to 6.78]}, fosphenytoin [an antiepileptic drug, EC 50 : 77.01 nM (95% CI: 52.63 to 115.10)], and dexamethasone phosphate [steroid-derived phosphate, EC 50 : 14.68 nM (95% CI: 5.44 to 22.10)] showed high agonist potencies for MRGPRX4 (Fig. 1, C and D, and table S1).” 2

The core statement in this sentence is: “Several phosphomonoester compounds including fospropofol, fosphenytoin, and dexamethasone phosphate showed high agonist potencies for MRGPRX4.” The specific EC 50 values are provided as immediate direct evidence for this claim, as well as for reference, while the figure is referenced only at the end, almost as a “if more information is needed, look here” prompt.

Applying Principles Throughout the Whole Results Section

These considerations should be applied on both a micro level, when presenting the results of each discrete experiment, and on a macro level, across the results section as a whole. Each paragraph should offer a transition to the next. Each presented piece of data should likewise offer some insights as to why the researchers sought the next piece of data. Finally, all of the data together must form a cohesive body that serves as evidence for the interpretations that readers will find in the discussion.

In their work, ZeRuth and his colleagues conclude most paragraphs in the results section with a summary statement that begins with “these data suggest/indicate”. 3 Readers who collate these statements together are rewarded with a de facto abstract for the results section, giving them an accessible and digestible primer on what the authors believe their data shows. 

Looking for more information on scientific writing? Check out  The Scientist’ s   TS SciComm  section. Looking for some help putting together a manuscript, a figure, a poster, or anything else?  The Scientist ’s   Scientific Services  may have the professional help that you need.

• Marshall WF, Fung JC. Modeling homologous chromosome recognition via nonspecific interactions . PNAS . 2024;121(20):e2317373121.
• Chien DC, et al. MRGPRX4 mediates phospho-drug-associated pruritus in a humanized mouse model . Sci Transl Med . 2024;16(746):eadk8198. 
• Grieve LM, et al. Downregulation of Glis3 in INS1 cells exposed to chronically elevated glucose contributes to glucotoxicity-associated β cell dysfunction . Islets . 2024;16(1):2344622.

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Home News Biomedical Informatics Faculty Members Named 2024 Translational Research Scholars back to News

Biomedical Informatics Faculty Members Named 2024 Translational Research Scholars

Assistant professors joanne cole, phd, and janani ravi, phd, have each been awarded $300,000 over four years to continue their impactful research in medicine..

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Two junior faculty members in the Department of Biomedical Informatics (DBMI) have been named 2024 Translational Research Scholars through the Program to Advance Physician Scientists and Translational Research (TRSP), allowing the bioinformaticists to continue and expand investigations into their specialties.

Joanne Cole , PhD, and Janani Ravi , PhD, both assistant professors in the DBMI, will each receive $300,000 over the next four years to put toward their research. They’ll also have access to career development resources and mentorship through the award. Cole will continue her work on the genetics of food preference and nutrition, while Janani will expand her lab’s work by developing computational methods to better understand host responses to infectious diseases.

In all this year, the program named five scholars across the University of Colorado School of Medicine who will find new and innovative ways to apply their data-driven research in the medical field.

Put to the taste

Four years after identifying 814 regions in the genome that are associated with dietary intake , Cole is ready to dive even deeper into the connections between genetics and food preference and intake.

With the TRSP award, she plans to develop a food preference survey with the Colorado Center for Personalized Medicine Biobank and test for associations with sensory genetic loci. With that, she’ll lead a human taste study connecting genotype to sensory perception and whether a person prefers a specific food.  

“This is an exciting area of research,” Cole says. “If we can use flavor and the reward pathways connected to flavor as a mechanism to help people alter their eating behaviors, maybe we can establish better long term personalized nutrition guidelines.”

Eating behavior is influenced by several different factors, but often consumers say it’s a like or dislike of a flavor that drives their food choice. Cole hypothesizes that targeting flavor biology could help to improve eating behavior and nutrition at the individual level.

This genome-centered personalized nutrition could improve health across a range of diseases and conditions, she says.

For Cole, the award is a chance to apply her research in new ways.

“I’m a statistical geneticist on a computer all day, so I find it exciting that this award couples funding with mentoring and guidance in the transitional space,” Cole says. “I will be able to take findings born from big data and apply it into a personalized nutrition therapy and actually impact human health.”

Hosting new methods in fighting infection

Antimicrobial resistance is a growing problem around the world, but instead of continuing to develop new drugs to kill infection, Janani, a computational biologist, and the members of her lab are looking at ways to better equip patients to fight infection on their own.

“We want to understand how the host responds to infectious disease and whether they can be given a drug that can help them fight the infection better,” Janani says.  

The lab will leverage publicly available omics signatures from drugs and noncommunicable diseases to then discover targeted host-directed therapeutics. Janani’s work will initially focus on the potential of repurposing FDA-approved drugs to treat tuberculosis (e.g., heart disease-treating statins), but the framework is broadly applicable to understand host response to infectious diseases of all kinds, Janani says.

DBMI is a thriving and innovative environment for this research to take place, and the TRSP award continues to make it possible.

“We have the Colorado Center for Personalized Medicine right upstairs, the biobank, and the strength of the Human Medical Genetics and Genomics and Computational Bioscience graduate programs to enable this translational work,” Janani says. “Collaborating with these resources allow us to take the genomic layer and the transcriptomic layer and understand how different subpopulations — whether it’s sex, ancestry, age, geographical region, or something else — and their underlying genetic variants increase the predisposition to the disease and how they differentially respond to it. That helps inform how we can tailor targeted host-directed therapeutics, which is the translational piece to this award.”

Editor's note:   Janani Ravi, PhD, assistant professor of biomedical informatics at the CU School of Medicine, has requested to be identified by her first name in this article.

Topics: Research , Awards

Joanne Cole, PhD

Janani Ravi, PhD

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Title: performance evaluation of reddit comments using machine learning and natural language processing methods in sentiment analysis.

Abstract: Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is hindered by the lack of expansive and fine-grained emotion datasets. To address this gap, our study leverages the GoEmotions dataset, comprising a diverse range of emotions, to evaluate sentiment analysis methods across a substantial corpus of 58,000 comments. Distinguished from prior studies by the Google team, which limited their analysis to only two models, our research expands the scope by evaluating a diverse array of models. We investigate the performance of traditional classifiers such as Naive Bayes and Support Vector Machines (SVM), as well as state-of-the-art transformer-based models including BERT, RoBERTa, and GPT. Furthermore, our evaluation criteria extend beyond accuracy to encompass nuanced assessments, including hierarchical classification based on varying levels of granularity in emotion categorization. Additionally, considerations such as computational efficiency are incorporated to provide a comprehensive evaluation framework. Our findings reveal that the RoBERTa model consistently outperforms the baseline models, demonstrating superior accuracy in fine-grained sentiment classification tasks. This underscores the substantial potential and significance of the RoBERTa model in advancing sentiment analysis capabilities.

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