Doctor of Philosophy
In Data Science
100% ONLINE PhD-DS
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National and Northcentral have merged, and this program is now offered by NU. Learn more.
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.
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- 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.
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.
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.
Establishing insights concerning population estimates, while understanding and communicating knowledge about variance in likely outcomes, is a fundamental skill of a data scientist. At the doctoral level, you will apply this understanding to the delivery of documentation for an audience of stakeholders who hinge key business decision-making on understanding the likelihood of an event’s occurrence. Within the academic setting, this understanding drives the development of foundational knowledge for research in the resolution of problem settings. In this course you will learn how to understand probability functions to apply your knowledge as a decision-maker or educator.
Data preparation is the process in which data from one or more sources is cleaned and transformed to improve its quality before its use in data analysis. This process requires the majority of the time required to complete the data science lifecycle. During this course, you will learn the tools and techniques used during data preparation and the role they play in delivering quality data for making informed decisions. You will end the course by gathering and preparing data sets for future analysis.
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 includes methods, means, and processes involved in transforming raw data into useable form for a multitude of analytics. Data curation is a set of processes that transforms, manages, stores, and democratizes data for use for analysts and data scientists through the lifecycle of data. The curation of data enables an organization or researcher to maximize the value of the data and effectively use the transformed data to produce and deliver insights. This course considers data that have been already acquired and integrated into useable repository and focuses on teaching techniques to make those data usable for next steps in developing analytics models.
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.
This course examines current techniques and methods utilized in manipulating data in quantitative analysis. You will analyze processes within data science that help organize large data sets. You will explore the differences in statistical reasoning based on Frequentists and Bayesian philosophy and will analyze output based on Artificial Neuron Network analysis.
The ability to generate insights from data is a critical data science competency. As part of this course, you will expand upon your understanding of ethics regarding data reporting. As such, you will be required to develop standards needed to improve integrity and validity of data. This course also covers textual and tabular reporting concepts and formats used in data science. During this course, you will evaluate methods for communicating data outputs and outcomes. You will end the course by creating a data presentation report and executive memo that adheres to industry standards.
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.
The results of technical research are frequently used to support informed management decisions. This course provides technology leaders and professionals with the skills needed to design and conduct quantitative research studies to support specific types of data. During this advanced course in research, you will explore and apply different types of quantitative research methods and statistical techniques. You will also explore instrumentation, data collection, and data analysis tools and techniques to create aligned, ethical, and substantive research designs.
A quantitative research design includes objective analysis using experimental, quasi-experimental, and related techniques. 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. During this course, you will work through the scientific research process and apply your knowledge of quantitative research design to develop a technical research proposal in which 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.
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
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
- Finance and Insurance
- Colleges and Universities
- Information Services
SOURCE: Emsi Labor Analyst- Report. Emsi research company homepage at https://www.economicmodeling.com/company/ (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
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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.
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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.