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You’re an analytics professional with a talent for research. You’re considering a PhD in Data Analytics as the next logical step in your career, but you’d like to know more about the practicals. Explore different types of analytics doctorates. Dig into details on timelines, coursework, and the dissertation process. Learn about admissions requirements and funding options, including fully-funded doctorates. Find answers to questions about online degrees and employment avenues after graduation. Or skip ahead to our listings of all the PhD in Data Analytics programs in the country.
What Are PhD in Data Analytics Programs?
A PhD in Data Analytics or a closely related field is an interdisciplinary doctorate that focuses on cutting-edge research in the realms of advanced analytics, statistical computing, big data, and data science. Doctoral students in analytics:
- Push the boundaries of analytics in order to solve complex societal & organizational problems and transform decision-making
- Train to be expert practitioners in big data technologies, newly developed statistical methods, and “out of the box” analytical thinking
- Become analytics & data science professors at universities, senior analytics consultants in industry, and government advisors
Can You Earn a PhD in Data Analytics?
Yes. Doctoral programs in data analytics are available, but they are rare. The most popular title for a degree in the realm of data is the PhD in Data Science. Data science is a highly inventive field that builds on analytical foundations, so it makes sense to consider a doctoral program that focuses on innovation & self-guided discoveries.
When you do find a PhD with the word “analytics” in the title, you’re still going to be looking at a doctorate that intersects with the field of data science. Massive data sets, complicated analytics processes, sophisticated predictive models—doctoral students in analytics are schooled in all of these areas (and more).
Types of Data Analytics Doctorate Programs
We’ve listed some common titles for doctorates in analytics, but we recommend you check the curriculum links in our listings and learn which department/s are offering the program. You should also look up the faculty’s research interests to see if they align with your own ideas for PhD projects. For example:
- If the degree is offered by the Department of Computer Science, a PhD in Data Analytics might be heavy on research into ethics, bias, AI, and building intelligent systems.
- If the degree is offered in partnership with the School of Business, a PhD in Data Analytics could be preoccupied with Machine Learning (ML), risk analysis, and econometrics.
The title of the PhD plays second fiddle to the department.
PhD in Analytics
A PhD in Analytics can often cut across multiple data-driven domains. Think of fields like Business Analytics, Data Science, Operations Research, and more. For instance, at the University of Notre Dame, doctoral students in analytics are able to access a large number of analytics research labs (e.g. gaming, human behavior, data & society, business, etc.) and collaborate with all kinds of partners.
PhD in Big Data Analytics
Doctorates in Big Data Analytics tend to focus on advanced systems & technologies that deal with processing big data (e.g. statistical computing, data mining, etc.), as well as their applications to real-world problems. Some universities, like the University of South Florida, are also interested in examining the human & social implications of analytics (e.g. ethical usage).
PhD in Analytics & Data Science
A PhD in Analytics and Data Science or a PhD in Data Science, Analytics & Engineering is a way for universities to combine data expertise from multiple departments. Yes, advanced analytics & big data processes will be addressed in the curriculum. But you’ll also find a strong emphasis on programming, algorithm creation, and systems development.
PhD in Data Science
Doctoral programs in data science may have more of a “design & develop” feel than analytics doctorates. In addition to exploring advanced analytics & big data applications, PhD in Data Science students are often interested in designing new information systems & tools (e.g. dashboards), creating their own algorithms & models, and exploring the boundaries of AI & Machine Learning (ML).
How Doctorates in Data Analytics Work: Curriculum & Dissertation
PhD programs in data analytics contain 6 key elements that take 4-5 years to complete on a full-time schedule. You will have to tackle each stage (e.g. core coursework) before you can proceed to the next one (e.g. qualifying exam).
- Core Coursework
- Qualifying/Comprehensive Exam
- Dissertation Proposal
- Dissertation Defense
- Year 1: Core coursework and first-year research papers. Assignment of a faculty mentor.
- Year 2: Core coursework, electives, second-year research papers, and the qualifying exam.
- Year 3: Any remaining coursework. Preparing research projects for publication. Dissertation proposal.
- Year 4: Dissertation work under the guidance of a dissertation advisor and advisory committee.
- Year 5: Dissertation work. Research papers & conference submissions. Dissertation defense.
A PhD in Data Analytics or a closely related field will always contain a set of courses in advanced analytics & data science subjects. These courses can come from multiple departments (e.g. Computer Science, Mathematics & Statistics, Industrial Engineering, Psychology, etc.). Examples include:
- Big Data Analytics
- Data Mining
- Theoretical Statistics
- Statistical Computing
- Machine Learning
- Database Systems
- Information Assurance & Security
These are just a few sample course titles! Use the curriculum links in our listings to get a feel for each program’s unique flavor.
Once you’ve tackled the fundamentals of core coursework, you’ll usually be able to choose high-level electives in your particular research interests. For instance, the University of Central Florida offers electives in:
- Advanced computing (e.g. Parallel & Cloud Computation)
- Sophisticated analytics applications (e.g. Interactive Data Visualization)
- Industries (e.g. Industrial Engineering Analytics for Healthcare)
With some programs, you can customize your doctorate to a remarkable extent.
A qualifying exam is designed to test your knowledge of core coursework. It might take the form of a traditional exam, a paper and/or a project. For example, at the University of South Florida, PhD students are required to report on the results of a real-world, big data analytics project and include codes & systems that were developed in the process.
You’ll be required to develop an original idea for a research- or project-based dissertation and present your dissertation proposal to a dissertation advisory committee—experienced faculty members and (occasionally) outside experts who are interested in your area of work.
- A research-based dissertation will explore new realms of analytics research and potential applications.
- A project-based dissertation will involve work on a real-life project—this may be created at a research center or be suggested by an industry partner.
The dissertation proposal often takes the form of a written outline and an oral defense/presentation. If the committee accepts your proposal, you can get to work on your dissertation.
A PhD dissertation is a piece of original research that makes a significant contribution to the theory & practice of a field. In the world of data analytics & data science, dissertations can be research-based or project-based.
Examples of real-life PhD in Data Analytics & Data Science dissertation titles include:
- A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data
- Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance
- Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data
- The Intelligent Management of Crowd-Powered Machine Learning
- Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models
- Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
While you are writing up your dissertation, many universities will also expect you to be submitting related research papers to peer-reviewed journals & industry conferences.
The final step in the PhD process is the dissertation defense. You’ll be required to present your dissertation findings to your dissertation advisory committee and defend your research ideas in an oral & visual presentation. This will be followed by questions and a discussion.
It’s not as intimidating as it sounds. By this stage in your education, you will know your research inside-out and will have brainstormed many of the potential questions with your dissertation advisor. You can prepare for a defense by observing other student defenses, practicing with mock presentations, and reading up on the work of committee members.
PhD in Data Analytics: Admissions
Doctorate in Data Analytics: What It Takes to Get In
Every PhD program in data analytics is going to have a unique set of admissions requirements! When you’re putting together a shortlist of doctorates, use the admissions links in our listings to save yourself time & trouble. You can decide if the program suits your level of expertise and education.
Doctoral programs in tech-driven disciplines—especially ones that are fully funded—are extremely competitive. You can stand out from the crowd by:
- Examining your entire application to see if you can make up for weaknesses (e.g. lower grades) with strengths (e.g. real-world projects)
- Matching your research interests to the university, department & research labs offering the program
- Collaborating with experienced analytics practitioners to co-author papers & publications
- Attending industry events and making connections that will help in your research
- Earning professional certificates to fill in any skills gaps
Your degree should be in a discipline that’s relevant to your area of research interest in the PhD. For a data analytics doctorate, that might mean a degree in statistics, data analytics, computer science, economics, or similar. The standard GPA requirement is 3.0 GPA or higher.
- Bachelor’s Degree Entry: Some doctoral programs in data analytics & data science are willing to consider applicants with a bachelor’s degree.
- Master’s Degree Entry: Some doctoral programs are only looking for candidates with a master’s degree.
If you’re an undergraduate and you like the look of a PhD that only accepts master’s candidates, ask the program coordinator if you can earn an MS through the same university. Most doctoral programs have a “Master’s Along the Way” option.
Skills & Proficiencies
PhD candidates in analytics must be ready to tackle advanced coursework and high-level research. So universities will usually want to see evidence of proficiency/course credits in:
- Statistics, calculus & linear algebra
- Common analytical programming languages (e.g. R, Python, SAS, etc.)
- Analytics fundamentals (e.g. database management systems)
If you don’t have an undergraduate or master’s degree in analytics or a closely related field, universities will be poring over your transcripts & résumé to make sure you can handle any technical coursework.
In addition to your degree transcripts, almost all PhD programs in data analytics & data science fields will want to see:
- GRE or GMAT scores
- Letters of recommendation
- Statement of purpose
- TOEFL scores for non-English speaking international applicants
PhD in Data Analytics: Tuition & Funding
How to Fund the PhD
Doctoral programs in data analytics & data science fall into 2 broad categories:
As you might expect, fully funded doctorate programs at strong universities are hard to get into!
Fully Funded PhD Programs
A number of STEM doctorates at research universities are fully funded. The university will waive all tuition costs and provide you with a living stipend as compensation for teaching & research activities. Many PhD students work as Teaching Assistants (TAs) and Research Assistant (RAs) during their doctoral studies.
Talk to the PhD program coordinator and check the fine print when you’re considering these programs.
- You may (or may not) qualify for on-campus housing and university health insurance.
- You may (or may not) qualify for conference stipends, overseas internships, and other perks.
- You may (or may not) be expected to pay for miscellaneous university fees.
- You may receive funding for Years 1-4 of your degree, but Year 5 support could be conditional on strong academic performance.
Tuition-Driven PhD Programs
You’ll also find doctoral programs in analytics & data science that do not offer any funding. They’ll expect you to pay for the degree out of your own pocket. At a private university, a PhD could cost upwards of $60,000-$80,000 in tuition alone.
So tread carefully! If you don’t qualify for fully funded PhD programs and you believe that a doctorate is essential for your career goals, consider applying to a PhD program at a public university in your state—UCF’s in-state tuition for a PhD in Big Data Analytics is very reasonable.
You will also need to look into postgraduate loans, private scholarships & fellowships, employer reimbursement, and teaching & research job opportunities to offset your costs.
Online PhD in Data Analytics Programs
Can You Earn an Online PhD in Data Analytics?
Yes—but we would caution against them. There are a few universities that offer online doctorates in data analytics, but they tend to be for-profit (e.g. Colorado Tech) or focused on executive-level training instead of research (e.g. DBA in Data Analytics from the University of the Southwest).
You’ll have a little more luck in finding online doctorates in data science, but they still won’t be offered by top-tier universities.
Why Are Online PhD Programs in Analytics Hard to Find?
Prestigious research universities & high-ranking schools are very cautious about maintaining their reputation for quality. They want doctoral students in data analytics & data science to:
- Attend classes in advanced topics, ask questions, and follow-up with faculty
- Have unfettered access to the university’s research centers, labs, and technical facilities
- Be able to teach undergraduates and conduct research in-person
- Meet with their dissertation advisor on a regular basis
- Network with visiting experts and fellow students
We agree with them. At this level, we highly recommend you choose an on-campus doctoral degree.
Career Prospects for PhD in Data Analytics Graduates
A PhD in Data Analytics or a closely related field is a super-specialized degree. You don’t need a doctorate to pursue a career in analytics & data science. Many senior-level practitioners simply have a degree like a Master’s in Data Analytics (or a similar title) and a lot of on-the-job experience.
However, a doctorate in analytics is an excellent choice for aspiring:
- University Professors: If you wish to teach analytics & data science at a college or university, you will probably need a research-focused doctorate. At the University of Notre Dame, 80% of its PhD in Analytics graduates go into academia.
- High-Level Researchers: PhD graduates work in think tanks, industry research labs, and university research centers where exciting discoveries are taking place.
- Data Science & Analytics Consultants: You may wish to act in an advisory capacity for Wall Street, Silicon Valley, and other major centers of industry.
- Senior Research Positions: Some jobs in major tech companies, data-intensive businesses & financial companies (e.g. Senior Statistician) will require top-level research skills.
PhD Data Analytics FAQs
What Should I Look for in a Data Analytics Doctoral Program?
When you’re starting to put together a shortlist of doctoral programs, consider the following aspects:
- Funding Options: The best choice is going to be a fully funded PhD from a highly ranked & highly regarded university that includes teaching & research assistantships.
- Departmental Reputation: Which schools & departments are offering the degree? What kinds of unique benefits do they offer students? How much research funding do they receive?
- Faculty Expertise: Faculty profiles will be posted on the PhD program website. Read their bios, meet them for a virtual coffee, and learn more about their research & industry work. These people will become your advisors & mentors.
- Access to Resources: Will you have access to top-of-the-line analytics tools, commercial resources, and large-scale infrastructures? Can you work on projects within a major analytics research lab or center?
- Career Preparation: A strong PhD program will prepare you for the job market after graduation. Does the curriculum include opportunities for you to submit research papers to peer-reviewed journals? Does it offer stipends for conference travel? Does it bring in visiting experts for seminars?
What is a STEM Doctorate?
STEM stands for Science, Technology, Engineering & Mathematics. A STEM doctorate is any PhD—including the PhD in Data Analytics and the PhD in Data Science—that contains at least 50% of coursework in these fields.
- Are you an international student? Ask if the doctoral program has a “STEM designation” from the U.S. Department of Homeland Security (DHS). Students on an F-1 Visa can apply for Optional Practical Training (OPT)/temporary employment after graduation. Having a STEM-designated degree extends the OPT period from 12 months to 36 months.
- STEM programs often receive a fair amount of funding from the government and private industries. That means universities may be able to offer fully funded PhD programs to multiple students.
Is a PhD in Data Analytics Worth It?
Only if you have a specific career goal in mind. A PhD in Data Analytics or a closely related field is going to be time-consuming, challenging, and heavy on research. At least 4-5 years of your life will be devoted to earning it, so you and your family need to be prepared for the journey.
Unsure about your decision? Talk to analytics professionals who have already gone through the PhD gauntlet. You’ll find doctoral graduates on LinkedIn, at industry conferences, and within faculty directories on university websites. Be prepared to talk to them about your research interests and your goals.