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PhD in Business Analytics

The Faculty of Business and Economics of the University of Lausanne (HEC Lausanne) is one of the leading research-oriented and fully accredited business institutions in Europe. With over 80 Professors, our faculty offers a stimulating and productive research environment where PhD candidates work in close interaction with their thesis supervisor.

The doctoral program in Business Analytics aims to attract the best doctoral students and guide them to become competent independent researchers. Successful applicants must hold a MSc degree with a strong quantitative component, e.g., Business Analytics, Engineering, Mathematics, Statistics or Operational Research. While the PhD in Business Analytics opens the way for an academic career, it also offers very diverse employment opportunities in consulting and large corporations.

Undertaking doctoral studies requires a strong personal commitment for three to five years. During a first phase, our doctoral candidates acquire the necessary research skills and domain-specific knowledge through a tailor-made course program. They also perform a literature review and defend their thesis proposal. The second phase is dedicated to writing research papers, culminating in the defence of the doctoral thesis.

You can find more information about the PhD in Business Analytics on the other pages of our website.

Doctoral Program in Business Analytics University of Lausanne HEC Lausanne Anthropole 3035 Quartier Chamberonne CH-1015 Lausanne

saycam.nguyen@unil.ch +41 21 692 36 50

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AnalyticsDegrees.org

PhD in Data Analytics Programs

On This Page:

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).

Note: PhD programs are focused on original research and high-level thinking. If you want a workplace qualification, consider a Master’s in Data Analytics .

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).

Note: Interested in industry & corporate analytics applications? Check out the guide to the PhD in Business Analytics .

How Doctorates in Data Analytics Work: Curriculum & Dissertation

Degree structure.

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, 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.

Sample Curriculum

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.

Dissertation Titles

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

Degree Requirements

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.

General Requirements

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:

• Fully funded PhD programs
• Tuition-driven PhD programs

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.

All Phd in Data Analytics Programs

Arizona state university.

School of Computing and Augmented Intelligence

Tempe, Arizona

PhD in Data Science, Analytics, and Engineering

University of arizona.

Department of Biosystems Engineering

Tucson, Arizona

PhD in Biosystems Analytics & Technology

University of central florida.

College of Sciences

Orlando, Florida

University of South Florida-Main Campus

Muma College of Business

Tampa, Florida

Georgia State University

Robinson College of Business

Atlanta, Georgia

PhD in Business Administration & Digital Innovation - Data Science & Analytics

Kennesaw state university.

School of Data Science and Analytics

Kennesaw, Georgia

Doctor of Philosophy in Analytics and Data Science

University of notre dame.

Mendoza College of Business

Notre Dame, Indiana

University of Kansas

School of Business

Lawrence, Kansas

PhD in Analytics and Operations

Central michigan university.

College of Science and Engineering

Mount Pleasant, Michigan

PhD in Statistics and Analytics

North carolina, north carolina state university at raleigh.

Center for Geospatial Analytics

Raleigh, North Carolina

PhD in Geospatial Analytics

Pennsylvania, pennsylvania state university-main campus.

College of the Liberal Arts

University Park, Pennsylvania

PhD in Human Development and Family Studies and Social Data Analytics

Phd in informatics and social data analytics, phd in political science and social data analytics, phd in psychology and social data analytics, phd in social data analytics, phd in sociology and social data analytics, phd in statistics and social data analytics.

DiscoverDataScience.org

PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

• Related Programs

Phd 2023/24 in DATA SCIENCE

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See  https://phd.uniroma2.it/web/DATA-SCIENCE_nD1028.aspx

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The responsibility for the funding offers published on this website, including the funding description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.

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International Programmes 2023/2024

Data Analytics (MSc) Data Analytics (MSc)

University of hildesheim • hildesheim.

• Course details
• Costs / Funding
• Requirements / Registration

English only

Non-EU applicants: 30 June for the following winter semester EU applicants: 31 August for the following winter semester

Non-EU applicants: 15 December for the following summer semester EU applicants: 15 February for the following summer semester

The international Master's programme in Data Analytics combines both a deep and thorough introduction to cutting-edge research in machine learning, big data, and analytical technology with complementary training in selected application domains. Based on modern state-of-the-art machine learning methods, the Data Analytics programme will provide students with the knowledge and skills required for modelling and analysing complex systems in application domains from business, such as marketing and logistics, as well as from science, such as computer science and environmental science. The programme is designed and taught in close collaboration with experienced faculty and experts in machine learning and selected application domains.

The two-year Master's programme in Data Analytics comprises four semesters with a total of 120 CPs (credit points). The study programme is structured into a methodological core (65%), an application area (10%), and a Master's thesis (25%).

Programme structure for the winter intake:

First semester Compulsory modules:

• Machine Learning Lecture (6 CPs)
• Modern Optimisation Techniques Lecture (6 CPs)
• Programming Machine Learning Lab Course (6 CPs)
• Data Analytics I Seminar (4 CPs)

and one application module (6 CPs)

Second semester Compulsory modules:

• Big Data Analytics Lecture (6 CPs)
• Advanced Machine Learning Lecture (6 CPs)
• Data and Privacy Protection Lecture (3 CPs)
• Distributed Data Analytics Lab Course (6 CPs)
• Data Analytics II Seminar (4 CPs)
• Project (part I) (6 CPs)

Third semester Compulsory modules:

• Planning and Optimal Control Lecture (6 CPs)
• Project (part II) (9 CPs)
• Data Analytics III Seminar (4 CPs)

one methodological specialisation lecture (6 CPs) and one application module (6 CPs)

Fourth semester The Master's thesis is written during the last semester. (30 CPs)

Programme structure for the summer intake:

First semester

• Methodological Specialisation Lecture (6 CPs)
• Application Module I (6 CPs)

Second semester

Third semester

• Project (part I) (9 CPs)
• Application Module II (6 CPs)
• Master's thesis (part I) (6 CPs)

Fourth semester

• Project (part II) (6 CPs)
• Master's thesis (part II) (24 CPs)

A list of available modules for methodological specialisation and applications can be found here .

You will pay a contribution of approx. 400 EUR per semester. This is a contribution to student services, university administration, and the student council. You can use the local public transport in Hildesheim and Lower Saxony free of charge. You will also benefit from discounts on meals at the university cafeteria and much more.

You will need around 850 EUR a month to cover your living expenses:

• Rent approx. 350–500 EUR
• Health insurance approx. 130 EUR
• Books and stationery approx. 50 EUR
• Meals approx. 200 EUR
• Other expenses approx. 100 EUR

The University of Hildesheim offers scholarships (such as the College of Minerva and Lore-Auerbach scholarships) supporting especially capable and socially committed students. You can apply once a year in June. Additionally, the International Office offers scholarships that support international students with graduation grants, for example:

• University of Hildesheim scholarships
• International Office scholarships

The Master's programme in Data Analytics is highly relevant for students aiming to pursue careers in research in an interdisciplinary field, data analytics, or a related industry. Students with a Bachelor's degree in Computer Science, Information Technology, Mathematics, or related fields are eligible to apply. Generally, students with a strong analytical, mathematical, and statistical base and good programming skills are more suited for this programme.

Eligible admissions are prioritised according to the following criteria:

• overall mark of your Bachelor's (53%)
• amount and marks of Bachelor's courses related to Data Analytics (incl. mathematics and programming, 35%)
• prior research activities in data analytics (6%)
• prior practical activities in data analytics (6%)

English language proficiency is required to undertake the Master's programme in Data Analytics. Sufficient knowledge of English can be demonstrated by a certificate (TOEFL computer-based test score of 61 or above, IELTS band of 6 or above, or an equivalent certificate) or a German "Abitur".

https://www.ismll.uni-hildesheim.de/da/index_en.html https://www.ismll.uni-hildesheim.de/da/faq_en.html https://www.ismll.uni-hildesheim. en /apply/

There are many job opportunities for students on campus (in the different departments, the central administration, etc.) and off campus. You can find part-time jobs here:

• career service
• student and research assistant positions

International students are only permitted to work in Germany with a work permit. The student visa allows a maximum of 120 full days (or 240 half days) of work per year. If you earn more than 450 EUR a month, you will be subject to higher health insurance premiums.

Make sure your study workload and working hours remain balanced.

Accommodation is available through the Student Services Office or on the private market. Many students live in shared flats. Offers of room vacancies can be found on the notice boards in the university or online on "WG-Börsen" (shared flat marketplaces). The student services for Eastern Lower Saxony (Studentenwerk OstNiedersachsen) also has a room marketplace online.

The career service is aimed at students and recent graduates of all degree programmes at the University of Hildesheim and in all phases of the transition from study to work. Contact us — we will be happy to support you! During career week, you can take part in workshops specially designed for international students, for example, application workshops or career talks.

• Welcome event
• Buddy programme

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8 of the Best Data Analyst Courses in Germany & How You Could Take Them for Free

In engineering and manufacturing, Germany has long been an economic powerhouse. More recently, it has also emerged as a hub for data-driven innovation. The country’s booming business landscape and growing tech ecosystem have converged to create an unprecedented demand for data analysts. To illustrate, in June 2023 alone, there were almost 5,000 open positions for data analysts just in Berlin .

Fortunately, for those interested in pursuing a tech career, many data analyst courses in Germany are feeding the supply of these sought-after skills. Better yet, if you’re a German resident and are currently unemployed, free access to these data analyst courses in Germany might be possible using the “Bildungsgutschein” (or training voucher).

In this article, I’ll explore various data analyst courses in Germany and how to get started on your career change journey without paying a penny. Start today, and you could be part of Germany’s burgeoning data community far sooner than you realize.

Table of contents:

• Germany and data: A match made in heaven?
• Data analyst courses in Germany: University or bootcamp?
• How to take a bootcamp for free
• 8 of the best data analyst courses in Germany
• How to find out if data analyst courses might be for you

1. Germany and data: A match made in heaven?

During the 20th century, Germany primarily secured its economic reputation through manufacturing and precision engineering. These industrial strengths were a key driver of Germany’s growth over the last century. 

As we march into the second quarter of the 21st century, Germany continues to innovate. This time, however, less through high – performance vehicles, cutting-edge aircraft, and specialized materials, but by embracing digitalization and the data-driven economy.

Recent statistics from the European Data Market Study 2021–2023 paint a compelling picture. The study found that the value of the European Union’s data market reached a massive €72,963 million in 2022—a growth of 12.6% from 2021. And Germany is the clear leader here, accounting for the largest share at €20,351 million.

The German State hasn’t overlooked this surge in data-driven activity. Shoring up its early successes, Germany has recently adopted a National Data Strategy , aimed at improving data access, quality, trust, and usage across the public sector, economy, and civil society. This indicates Germany’s commitment to this growing industry, solidifying its position and, in turn, creating jobs for those who want to thrive in data analytics.

2. Data analyst courses in Germany: University or bootcamp?

When exploring data analyst courses in Germany, career changers have several options to choose from. Two popular ones include the formal education route (such as studying at university) and enrolling in a data analyst bootcamp. 

Let’s explore the pros and cons of each.

University programs

University data analyst courses in Germany offer comprehensive, in-depth curriculums covering the theoretical aspects of the field. They usually last two to four years and result in an academic qualification such as a bachelor’s or master’s degree. 

The German “Ausbildung” model—a well-respected apprenticeship route—is another formal approach some people take. This is more hands-on and results in a vocational qualification.

Pros of university data analytics programs

• University programs (and vocational apprenticeship routes) offer comprehensive, in-depth curriculums covering both theoretical and practical data analytics
• The university route results in a respected qualification, e.g. bachelor’s or master’s
• Academic study routes offer research opportunities and access to the latest findings and emerging techniques/technologies
• A degree provides long-term networking opportunities with professors, fellow students, and employers—potentially easing the transition into the labor market

Cons of university data analytics programs

• Formal academic and vocational training routes typically last between two and four years, which is a big time commitment
• University has much higher associated costs compared to bootcamps
• The academic route often has a more rigid curriculum with less flexibility about what you learn

Data analyst bootcamps

One fast-track alternative to the academic route is the data analyst bootcamp. Bootcamps provide intensive, hands-on training, equipping you with job-ready skills in a shorter timeframe (typically weeks or months rather than years) than a formal degree.

Pros of data analytics bootcamps

• They offer intensive, hands-on training focused on job-ready skills
• They have a shorter duration, usually weeks or months
• While they come with a price tag, it is more affordable than four years of a university program
• Most credible bootcamps offer flexible scheduling, including full-time or part-time options, so you can fit learning in around your existing responsibilities
• A high-quality bootcamp will provide strong career support and international networking opportunities
• You’ll often have access to one-to-one mentoring
• If you’re unemployed, or soon to be, you might be eligible to take a bootcamp for free using your “Bildungsgutschein” (training voucher)

Cons of data analytics bootcamps

• Because they are focused on essential skills, bootcamps may cover a narrower scope compared to university data analyst courses in Germany
• They don’t offer academic credentials (although good courses offer employer-recognized certifications)
• Intense learning objectives require a high level of self-discipline
• Not every data analyst course in Germany is created equal—you’ll have to assess numerous bootcamps to find one that meets your needs
• Course quality varies—a cheaper price tag might seem tempting up front, but could mean the course content is less rigorous

3. How to take a bootcamp for free

Let’s presume you want to opt for the bootcamp route rather than an academic one.

The good news, if you’re living in Germany, you may be eligible for the German “Bildungsgutschein”. This educational voucher, issued by the German Employment Agency (Agentur für Arbeit), permits you to enroll in training for free, so long as you meet the eligibility criteria.

What are these eligibility criteria? 

First, you are registered in Germany and can prove this with a German “Anmeldebescheinigung” (or registration certificate). 

Second, you are unemployed or at risk of unemployment (again, with evidence to prove this.) 

Third, you can dedicate time to training (the exact amount will vary depending on your preferred course, but most offer full- or part-time options).

Many data analyst courses in Germany are eligible for the Bildungsgutschein. The most sensible first step, then, is to check your chosen provider’s website for mentions of the Bildungsgutschein. You shouldn’t have to look too far—eligible providers will want to advertise this! 

Next, you’ll need to book an appointment with your local Agentur für Arbeit, which will assign you an advisor.

Finally, prepare for your appointment! You’ll have to outline to your advisor why you’d like to take this route. Hopefully, they will then approve your application to participate in your chosen data analyst course in Germany.

This covers the process in a nutshell. There’s a bit more to it, obviously, but you can get all the details by reading how to become a data analyst with your Bildungsgutschein .

3. 8 of the best data analyst courses in Germany

Now you know how to potentially take a data analyst course in Germany for free, let’s explore 8 of the best data analyst bootcamps to get you started. First up…

CareeFoundry—Data Analytics Program

CareerFoundry’s bootcamp at a glance:

• Study format: Online
• Price: €8,900
• Duration: 6 months (30-40 hours per week) or 10 months (15-20 hours per week)
• Eligible for Bildungsgutschein: Yes

CareerFoundry’s Data Analytics Program is an online bootcamp providing all the skills you need to start your data analytics career. It covers the entire data lifecycle, including data preparation, analysis, visualization, and presentation skills.

Key features include flexible study options, instruction from experienced data professionals, and personalized mentoring. The program also offers career coaching and a job guarantee—CareerFoundry will refund your tuition if you don’t find a job within six months.

If you want a structured, project-based, hands-on curriculum, CareerFoundry’s program is a great option and has an excellent score on Course Report . You’ll even complete the program with a portfolio of work to assist you in your job hunt!

Ironhack—Data Analytics Bootcamp

Ironhack’s bootcamp at a glance:

• Study format: On-campus/ remote
• Price: €8,000
• Duration: 9 weeks full-time or 24 weeks part-time

Ironhack’s Data Analytics Bootcamp is an intensive, 9-week data analytics course in Germany that covers data skills like Git, Python, and MySQL, to name a few.

You’ll not just learn the basics; you’ll future-proof your skills by learning about artificial intelligence and large language models such as ChatGPT, and their implications for the profession.

Whether you opt for the online bootcamp or prefer to study on-campus in Berlin, you’ll have to complete 60 hours of pre-work before starting to ensure that you have the requisite programming and statistics skills to thrive. But it’s worthwhile—once you’ve graduated, you’ll be capable of retrieving, cleaning, and analyzing data and effectively communicating business insights.

Le Wagon—Data Analytics Course in Germany

Le Wagon’s bootcamp at a glance:

• Study format: Online or in-person
• Price: €7,400 to €8,500 depending on study format
• Duration: 2 months (40 hours a week) to 7 months (15 hours a week)

Le Wagon’s data analytics course covers everything from data collection to visualization. And while it covers the technical requirements of the role, it also looks at the business skills you’ll need, such as teamwork and how to identify and measure key performance indicators.

You’ll learn how to collect, extract, and transform data, apply KPIs to dashboards, and about the analytics tools used by top companies.

To get the most from the bootcamp, you’ll complete 40 hours of preparation work covering SQL, Python, and Google Sheets. All of Le Wagon’s bootcamps (they offer several) are certified and recognized by the German government as an advanced training program, meaning they are eligible for the Bildungsgutschein.

Neue Fische—Data Analytics Bootcamp

Neue Fische’s bootcamp at a glance:

• Study format: Remote online or live in Hamburg, Munich, or Frankfurt
• Price: Starting from €9,500
• Duration: 12 weeks (Monday-Friday, 9 to 6)

Neue Fische’s bootcamp is a 12-week course offered in numerous locations around Germany or live online. You’ll learn about descriptive statistics, Python coding in an IDE (Integrated Development Environment), and of course, the all-important exploratory data analysis, which covers many of the essential data analytics techniques you’ll use in your future career.

You’ll also get to grips with the basics of databases, using tools like DBeaver (a database administration tool) to connect to a PostgreSQL relational database, and how to structure an SQL query. Finally, you’ll learn the essentials of sourcing data and visualizing your insights using Tableau.

The course culminates in a digital capstone project incorporating all the skills you’ve learned. The entire bootcamp includes 540 hours of instruction, practical assignments, and 1-on-1 feedback sessions.

Spiced Academy—Data Analytics Bootcamp

Spiced Academy’s bootcamp at a glance:

• Study format : On-site in Berlin or remote online
• Price: €9,800
• Duration: 12 weeks full-time

The Spiced Academy Data Analytics Bootcamp is a 12-week full-time program located in Berlin or live online. The bootcamp is aimed at beginners and covers various topics, including Python programming from scratch, data analysis using Python libraries (like Pandas and NumPy), mastering SQL, and data visualization using tools including Tableau and Dash.

Spiced Academy supports their students with prep material before the bootcamp kicks off, followed by one-on-one professional coaching and assistance with job placement once you get started. They offer flexible financing options, including the Bildungsgutschein.

WBS Coding School—Data Science Bootcamp

WBS Coding School’s bootcamp at a glance:

• Study format: Hybrid (Berlin and online) or 100% online
• Price : €9,900 (online) or €10,500 (hybrid)
• Duration: 15 weeks, full-time

WBS Coding School’s data science bootcamp is a 15-week full-time program, offering two options: 13 weeks online and 2 weeks on-campus (in Berlin) or 15 weeks online (with instructor-led training taking place live).

Like any good bootcamp, it’s designed for beginners without coding or math skills and covers the basics of Python, data analysis, machine learning, and SQL. However, this bootcamp focuses heavily on applying these in business settings. Taking a data science angle, you’ll dive deep into various Python libraries for data analysis but also machine learning topics like generative AI and how to train and fine-tune supervised and unsupervised machine learning models.

The course culminates in a two-week, real-world capstone project where you’ll work in teams. Overall, this is a good option for those hoping to pursue data science in business.

Constructor Academy—Data Science Bootcamp

Constructor Academy’s bootcamp at a glance:

• Study format: On-site in Munich or Bremen, or remote online

Constructor Academy’s Data Science Bootcamp, available in Munich, Bremen, or live online, is an intensive data analyst course in Germany with a data science focus. While the course’s early modules cover the same requisite fundamentals (such as Python, statistics, data visualization, and so on), the lion’s share of the curriculum focuses on advanced data science topics.

You’ll dedicate two weeks to machine learning and three to deep learning, natural language processing, and machine learning engineering. As far as possible, the course is extremely hands-on, meaning you’ll learn by doing, applying tools such as Keras (a neural network interface) and Docker (a platform used to develop and run ML apps) and learning techniques such as LIME and SHAP (which both help us understand how machine learning models make their predictions).

Be aware, though, that due to the complexity of the topics, you’ll have to pass a short technical interview—although you’ll receive all the materials you need to pass and have several days to prepare.

Code Academy Berlin—Advanced Data Analyst and Machine Learning

Code Academy Berlin’s bootcamp at a glance:

• Study format: On-campus in Berlin
• Price: €7,600
• Duration: 3 to 5 months

Code Academy Berlin’s Advanced Data Analyst and Machine Learning bootcamp is a 5-month data analyst course in Germany that expands on their 3-month data analyst and 4-month advanced data analyst bootcamp.

After learning the basics of Pandas and NumPy, you’ll master data visualization using various tools, including Tableau, Matplotlib, and Seaborn. The course follows a learning methodology specifically tailored to the fast-paced IT industry. If you take the 5-month course, you can choose between a data science or data engineering path.

Based on your decision, you’ll learn the foundations of deep learning and natural language processing (data analytics) or web development and data pipelines (data engineering), making this one of the more customizable courses on our list.

4. How to find out if data analyst courses might be for you

Before diving into a data analytics course in Germany, it’s essential to assess whether data analytics is right for you. Ask yourself the following:

Are you naturally curious about data and do you enjoy working with information?

Successful data analysts are curious about the world and how to quantify it. If you naturally spot quirks or patterns that others overlook, data analytics might be for you. Since data analysts have to back up their insights with numbers, you’ll probably have an innate interest in math, too, but if you’re not Einstein, it’s not a dealbreaker. You can learn to love numbers!

Are you comfortable with technology and interested in learning programming languages such as Python?

It’s inescapable that data analysis relies on specialized software, tools, and programming languages. If you’re a complete technophobe, it might not be for you. But you don’t necessarily have to be a tech whizz, either. At first, many tools used in data analytics might seem mystifying, but they’re a lot easier to grasp than you might expect. So long as you’re enthusiastic about learning and ready to adapt to new technologies, that’s usually enough to get started.

Do you have analytical and problem-solving skills?

At a high level, data analytics is about taking complex problems, breaking them down, and figuring out solutions. If you’re a logical thinker with excellent attention to detail, that’s a good sign that data analytics might be for you. In general, if you’re curious, ask questions, and like gathering evidence to inform decisions, you’ve got the right mindset to succeed.

Do you have a flair for communication?

Data analysts don’t just analyze stuff—they also have to communicate their findings. If you like the challenge of explaining complex topics clearly and compellingly, this trait will serve you well. Strong written and verbal communication skills are also beneficial, as are presentation skills—but you can learn these if they aren’t your natural strength!

Are you creative?

Often, people mistakenly think that data analytics is purely technical and logical. However, creativity is a highly valued skill in this field. The best data analysts blend analytical rigor with lateral thinking, finding new ways to solve problems. Creativity is also essential for storytelling, a much bigger part of data analytics than you might realize when communicating your findings.

You’ll notice that most of these questions don’t focus solely on technical skills. That’s because if your attitude and interests fit the bill, you’ve got the most important aspects covered. Learning the technical skills—although requiring effort and dedication—becomes more of a formality in that case.

Data analytics attracts people from a wide variety of backgrounds. This means the field is open to individuals like you, even if your previous experience doesn’t directly align with data analytics. The key is demonstrating the right mindset and passion for a new challenge.

There we have it! A detailed explanation of data analyst courses in Germany and how you can take them for free. 

Your next step should be to explore the various data analyst courses so you can find one that meets your objectives, location, budget, and career goals. For instance, are you interested in pure data analytics, or might you want to pursue a data science route, getting involved with the development and deployment of AI? 

Data analyst courses in Germany cover various career paths, so don’t rush in. If in doubt, it never hurts to start with a course focusing on the foundational data analytics skills that any data professional will need. You can always specialize later on.

Finally, if you’re resident in Germany, and are unemployed, you may be eligible for a “Bildungsgutschein” that covers the cost of a data analytics bootcamp. This is an excellent opportunity to learn the skills you need at no extra financial cost.

To get a taste of what a career in data analytics might involve, try this free, Data Analytics Short Course , or check out the following guides: