Man sitting on bench in office building

Doctor of Philosophy In Data Science (PhD-DS)

computer monitor icon

100% Online PhD-DS

Complete your studies on your own time.

calendar icon

New start date every Monday

Start your first course when it’s convenient for you.

graduate cap icon

40 Months to your PhD-DS

Finish your PhD-DS in just 20 courses.

National and Northcentral have merged, and this program is now offered by NU. Learn more.

Home » Programs » Doctor of Philosophy in Data Science 

Doctor of Philosophy in Data Science

Make informed decisions and drive growth with the 100% online Doctor of Philosophy in Data Science (PhD-DS) degree program at National University. Get an edge in the dynamic data science field by increasing your knowledge through a PhD-DS that’s aligned with industry needs, including the CRISP structure. 

NU’s PhD-DS program is designed and taught by experienced technology professionals, so you’ll build practical, real-world knowledge. You’ll explore a broad range of relevant topics, including data mining, big data integration, databases, and business intelligence. Additionally, the curriculum covers data visualization, critical analysis, and reporting, along with the strategic management of data.

Unleash the Power of Data with NU’s PhD-DS

The PhD-DS degree program will prepare you to conduct research in data science by exploring each stage of the data science life cycle in depth from an applied perspective and a theoretical perspective. Receive unmatched personal attention through NU’s unique one-to-one learning model, which pairs you with a professor in each course, so you get the support and guidance you deserve.

WASC icon

The Western Association of Schools and Colleges (WASC) accredits public and private schools, colleges, and universities in the U.S.

Course Details

  • Credit Hours: 60
  • Courses: 20
  • Estimated Time to Complete: 40 months

The Doctor of Philosophy in Data Science (PhD-DS) program can be completed in 60 credits. Each course runs 8 weeks, and dissertation courses run 12 weeks.

Course Sequence

The PhD program may be completed in a minimum of 60 credits. Additional credit hours may be allowed as needed to complete the dissertation research. If granted, additional courses will be added to the student degree program in alignment with the SAP and Academic Maximum Time to Completion policies. Students who do not complete their program in accordance with these policies may be dismissed.

Course Name

This course provides an introduction and overview of data science in order to make informed decisions about business needs. The objective of this course is to introduce you to the nature and methods of data science at the doctoral level. While data science is a varied and nuanced field that generally combines computer science with advanced mathematics, it’s application in research and industry ranges from understanding problem statements to producing insights using validated methods. You will explore data science life cycle and determine appropriate design methods and management of data to fit the context of research and/or industry issues. 

This course includes analytics methods to understand how data is shaped in relation to how it can be analyzed. This is a foundational skill for data scientists and important to apply prior to creating confirmatory (final) models that predict and deliver end-user insights for decision making. The focal points in this course are descriptive statistics and exploratory data analysis. Specific attention is given to measures of central tendency, clustering, variability, and frequency. You will learn identification of the appropriate univariate analysis for use in applied research in a business context. You will also learn to apply clustering analysis in relation confirmatory models.

Introducing statistical techniques is essential for extracting meaningful insights from data focusing on projects and research of Data Science. Through a comprehensive eight-week journey, students will explore topics such as normal distribution, hypothesis testing, power of test, type I and type II errors, sampling distributions, bootstrapping methods, diagnostic tools, validation techniques, and more. The course emphasizes practical applications, equipping learners with the skills to make data-driven decisions and extract hidden patterns from datasets. By mastering these inferential statistics techniques, students will be well-prepared to tackle complex real-world problems and enhance their expertise in the field of Data Science.

A comprehensive exploration of advanced predictive modeling and machine learning techniques. The course equips students with the skills needed to harness the power of data for making informed decisions. The course dives into regression models, decision trees, support vector machines, and ensemble methods like random forests and gradient boosting. Students will also learn about clustering methods, time series analysis, and the application of these techniques in real-world scenarios. Through hands-on projects and assessments, participants will become proficient in building predictive models, evaluating models, and effectively leveraging machine learning algorithms. It also equips students to interpret and communicate their findings effectively.

Data and databases are the foundation of all business systems. Organizations that do not understand the importance of data management are less likely to survive in the modern economy. During this course, you will study advanced concepts of database management systems and data warehouses. You will also research processes and techniques used to improve data repositories, manipulate data, and prevent data corruption. By the end of the course, you will be able to construct, assess, and transform data to improve business intelligence to support informed business decisions.

This course focuses on modern tools and methods to develop and work with large datasets. Some course concepts include the exploration of relational databases, distributed storage software, distributed computing methods, analytics and algorithms. You will explore current topics in the area of big data and potential future problems. You will investigate appropriate architectural techniques associated with big data. You will also evaluate the constructs of ethics in data science, propose techniques for application, and design a system to produce insights.

This course addresses needs in industry, business, and academia to improve performance and advance scientific knowledge. You will learn data mining techniques that help discover patterns, trends, anomalies, and associations that are otherwise hidden or unknown. In addition, this course introduces the fundamentals, principles, implementation techniques, and applications of data mining. Learning also includes data curation techniques, focuses on exploratory data analysis, prediction, classification, association analysis, similarity assessment and clustering, outlier, and anomaly detection. Interpreting and evaluating data analysis/data mining results is explored. Additionally, data mining experience for applications in computer vision, big data, and social networks will be provided.

This course examines the use of multivariate analysis to provide statistical and applied insight to data science problems. You will apply a variety of multivariate methods by selecting the appropriate models for the research questions posed and the data type. You will engage in hypothesis testing using parameters of multivariate data. Specifically, you will develop problem solutions by analyzing multidimensional data to derive meaningful insights into problem statements. Finally, you will present your results and actionable insights in an appropriate format for your audience.

Exploring univariate data analysis, beginning with the fundamentals of clustering univariate data, students learn to group similar data points an essential skill for identifying patterns in various fields. Moving on to advanced analytical methods, students extract deeper insights and discern trends. The next focus is on predictive analytics, where students acquire the skills to forecast outcomes using univariate data implementing predictive techniques, processes, and diagnostics. The natural language processing, underlining the criticality of effectively communicating analytical results is also a subject explored. Each section is carefully crafted to provide a detailed and practical learning experience, making this course ideal for anyone seeking to master the spectrum of univariate data analysis, from basic clustering techniques to advanced predictive and communication strategies.

Artificial intelligence is becoming more and more useful in helping solve everyday problems. Intelligent agents and natural language processing have become common in the marketplace. During this course, you will evaluate the impact of artificial intelligence on performance and enterprise resources. You will also expand your ability to improve an artificial intelligence application to address varied user specifications. Finally, you will be able to produce a complete artificial intelligence project plan that will integrate with current and proposed IT solutions for process improvement.

Evaluating the accuracy and effectiveness of graphical representations of data is a critical skill required of experienced data scientists. This advanced course in data visualization will help you identify the appropriate questions required to evaluate the validity of the insights provided by others and develop the skills needed to influence other decision makers. During this course, you will synthesize research on the best practices associated with communicating through data visualization. You will also study techniques and processes you can use to dynamically communicate your interpretations of effective graphic interactive representations of data.

This course provides a survey of the different methods used to conduct technology-based research. During this course, you will learn about the research principles and methodologies that guide scientific inquiry in order to develop an understanding of the effects of research on individuals and organizations. Specifically, you will study the scientific research lifecycle, data collection methods, and research design methodology. You will finish the course by selecting a research design methodology to support your research interests through the remainder of your program.

This advanced Data Science research design course immerses you in diverse methodologies, equipping you with a multifaceted approach to data-driven investigations. From the foundations of quantitative research, which harnesses statistical analyses to draw generalizable conclusions from large datasets to the cutting-edge realm of Constructive Research focuses on models, frameworks, tools, and software used by industry to improve value creation. Throughout the course, you will delve into DSR (Design Science Research) and examine how it integrates theoretical and empirical constructs with industry practices to develop applied and testable models, enhancing the Data Science landscape. Common approaches include experimental design, where controlled experiments are conducted to test hypotheses, observational studies that involve data collection without intervention, and exploratory research to uncover patterns and relationships in data. Furthermore, cross-sectional and longitudinal designs allow for the examination of data at specific time points or over time.

Technical, quantitative research involves statistical analysis of data collected from a larger number of participants to determine an outcome that can be applied to a general population. Technical constructive research focuses on models, frameworks, tools, and software used by the industry to improve value creation. A constructive approach to research of a technical nature integrates theoretical and empirical constructs with standard practices and experience to develop an applied and testable model to improve the field of Data Science. During this course, you will work through the scientific research process and apply your knowledge of both quantitative and constructive research design to develop a technical research proposal that you can use to support your research interests through the remainder of your program.

New data science technologies and programs should be aligned to the organizational mission, vision, and values; thus, it is important for technology leaders to develop data, information, and knowledge management policies. During this advanced course in data and knowledge management, you will develop an enterprise data governance strategy that integrates industry standards and best business practices in data science. You will also design metrics to measure and analyze data integrity to ensure data validity, evaluate various influences on enterprise data and knowledge management, and recommend data management solutions.

The Pre-Candidacy Prospectus is intended to ensure students have mastered knowledge of their discipline prior to doctoral candidacy status and are able to demonstrate the ability to design empirical research as an investigator before moving on to the dissertation research coursework. During this course, you will demonstrate the ability to synthesize empirical, peer reviewed research to prepare for the dissertation sequence of courses. This course should be completed only after the completion of all foundation, specialization, and research courses.

Students in this course will be required to complete Chapter 1 of their dissertation proposal including a review of literature with substantiating evidence of the problem, the research purpose and questions, the intended methodological design and approach,  and the significance of the study. A completed, committee approved (against the minimum rubric standards) Chapter 1 is required to pass this course successfully. Students who do not receive approval of Chapter 1 to minimum standards will be able to take up to three supplementary 8-week courses to finalize and gain approval of Chapter 1.

Students in this course will be required to work on completing Chapters 1-3 of their dissertation proposal and receive committee approval for the Dissertation Proposal (DP) in order to pass the class. Chapter 2 consists of the literature review. Chapter 3 covers the research methodology method and design and to includes population, sample, measurement instruments, data collection and analysis, limitations, and ethical considerations. In this course, a completed, committee-approved Chapters 2 and 3 are required and, by the end of the course, a final approved dissertation proposal (against the minimum rubric standards). Students who do not receive approval of the dissertation proposal will be able to take up to three supplementary 8-week courses to finalize and gain approval of these requirements.

Students in this course will be required to prepare, submit, and obtain approval of their IRB application, collect data, and submit a final study closure form to the IRB. Students still in data collection at the end of the 12-week course will be able to take up to three supplementary 8-week courses to complete data collection and file an IRB study closure form.

In this dissertation course students work on completing Chapters 4 and 5 and the final Dissertation Manuscript. Specifically, students will complete their data analysis, prepare their study results, and present their findings in an Oral Defense and a completed manuscript. A completed, Committee approved (against the minimum rubric standards) Dissertation Manuscript and successful Oral Defense are required to complete the course and graduate. Students who do not receive approval for either or both their Dissertation Manuscript or defense can take up to three supplementary 8-week courses to finalize and gain approval of either or both items as needed.

Degree Requirements

The University may accept a maximum of 12 semester credit hours in transfer toward the doctoral degree for graduate coursework completed at an accredited college or university with a grade of “B” or better.

The PhD-DS degree program also has the following requirements:

  • GPA of 3.0 (letter grade of “B”) or higher
  • University approval of Dissertation Manuscript and Oral Defense completed
  • Submission of approved final dissertation manuscript to the University Registrar, including the original unbound manuscript and an electronic copy
  • Official transcripts on file for all transfer credit hours accepted by the University
  • All financial obligations must be met before the student will be issued their complimentary diploma and/or degree posted transcript

Dissertation Process

Faculty assists each NU Doctoral student to reach this high goal through a systematic process leading to a high-quality completed dissertation. A PhD dissertation is a scholarly documentation of research that makes an original contribution to the field of study. This process requires care in choosing a topic, documenting its importance, planning the methodology, and conducting the research. These activities lead smoothly into the writing and oral presentation of the dissertation.

A doctoral candidate must be continuously enrolled throughout the series of dissertation courses. Dissertation courses are automatically scheduled and accepted without a break in scheduling to ensure that students remain in continuous enrollment throughout the dissertation course sequence. If additional time is required to complete any of the dissertation courses, students must re-enroll and pay the tuition for that course. Continuous enrollment will only be permitted when students demonstrate progress toward completing dissertation requirements. The Dissertation Committee determines progress.

What Can You Do with a Doctor of Philosophy in Data Science?

The PhD-DS degree prepares you to conduct research in data science by exploring each stage of the data science life cycle from both a theoretical and applied perspective. You’ll explore a broad range of related topics, including data mining, big data integration, business intelligence, data visualization, critical analysis, and strategic data management. These skills will qualify you to pursue a range of occupations that include:

  • Data Scientist
  • Data Engineer
  • Data Science Manager or Director
  • Machine Learning Engineer or Scientist
  • Research Scientist or Analyst

According to Emsi labor market analytics and economic data1, data science careers span a variety of technology, manufacturing, and service sectors, including:

  • Professional, Scientific, and Technical Services
  • Manufacturing
  • Finance and Insurance
  • Colleges and Universities
  • Retail
  • Information Services

SOURCE: Emsi Labor Analyst- Report. Emsi research company homepage at (Report viewed: 4/19/2022).

DISCLAIMER: The data provided is for informational purposes only. Emsi data and analysis utilizes government sources to provide insights on industries, demographics, employers, in-demand skills, and more to align academic programs with labor market opportunities. Cited projections may not reflect local or short-term economic or job conditions and do not guarantee actual job growth. Current and prospective students should use this data with other available economic data to inform their educational decisions.

Program Learning Outcomes

As a graduate of National University’s Doctor of Philosophy in Data Science (PhD-DS) program, you’ll be able to:

  • Develop knowledge in data science based on a synthesis of current theories
  • Explain theories, applications, and perspectives related to data science
  • Evaluate theories of ethics and risk management in information systems
  • Formulate strategies for data and knowledge management in global organizations
  • Contribute to the body of theory and practice in data science


Enrolling in a university is a big decision. That’s why our dedicated admissions team is here to guide you through the admissions process and help you find the right program for you and your career goals.

To that end, we’ve simplified and streamlined our application process, so you can get enrolled in your program right away. Because we accept and review applications year round, you can begin class as soon as next month, depending on your program and location of choice.

Learn more about undergraduate, graduate, military, and international student admissions, plus admissions information for transfer students. You can also learn more about our tuition rates and financial aid opportunities.

To speak with our admissions team, call (855) 355-6288 or request information, and an advisor will contact you shortly. If you’re ready to apply, simply start your application today.

Man in a button-down shirt smiles at a young boy in glasses who also wears a backpack
Weekly Course Starts
Our course structure is built to make earning your degree accessible and achievable by offering a rigorous, yet flexible program that works with your schedule.

Why Choose National University

  • 190+ Degree Programs
  • Online
  • Year-Round Enrollment
  • Military Friendly

We’re proud to be a Veteran-founded, San Diego-based nonprofit. Since 1971, our mission has been to provide accessible, achievable higher education to adult learners. Today, we educate students from across the U.S. and around the globe, with over 230,000 alumni worldwide.

head shot image of man named Francisco

“National University has impacted my career. You can immediately apply what you learn in class to your business.”

-Francisco R., Class of 2016

A mother sits on a couch with a laptop and smiles at her toddler-aged son, who is looking forward.
We know your life may not happen on a 9-5 schedule, so we offer courses online.
white scholarship oppotunities icon

The Key Grant Scholarship

Do you qualify for a needs-based scholarship? Learn more about the NU Key Grant Scholarship and other scholarship opportunities to unlock the door to your dreams!

Frequently Asked Questions (FAQ)

Yes, the National University Doctor of Philosophy in Data Science (PhD-DS) degree program is available 100% online. 

According to the Bureau of Labor Statistics (BLS), the median annual wage for data scientists was $100,910 in May 2021. The employment of data scientists is projected to grow 36% in the next ten years, much faster than the average for all occupations. 

Program Disclosure

Successful completion and attainment of National University degrees do not lead to automatic or immediate licensure, employment, or certification in any state/country. The University cannot guarantee that any professional organization or business will accept a graduate’s application to sit for any certification, licensure, or related exam for the purpose of professional certification.

Program availability varies by state. Many disciplines, professions, and jobs require disclosure of an individual’s criminal history, and a variety of states require background checks to apply to, or be eligible for, certain certificates, registrations, and licenses. Existence of a criminal history may also subject an individual to denial of an initial application for a certificate, registration, or license and/or result in the revocation or suspension of an existing certificate, registration, or license. Requirements can vary by state, occupation, and/or licensing authority.

NU graduates will be subject to additional requirements on a program, certification/licensure, employment, and state-by-state basis that can include one or more of the following items: internships, practicum experience, additional coursework, exams, tests, drug testing, earning an additional degree, and/or other training/education requirements.

All prospective students are advised to review employment, certification, and/or licensure requirements in their state, and to contact the certification/licensing body of the state and/or country where they intend to obtain certification/licensure to verify that these courses/programs qualify in that state/country, prior to enrolling. Prospective students are also advised to regularly review the state’s/country’s policies and procedures relating to certification/licensure, as those policies are subject to change.

National University degrees do not guarantee employment or salary of any kind. Prospective students are strongly encouraged to review desired job positions to review degrees, education, and/or training required to apply for desired positions. Prospective students should monitor these positions as requirements, salary, and other relevant factors can change over time.