Applied Data Science & Analytics (M.S.)

Applied Data Science & Analytics (M.S.) | Graduate

Now more than ever, organizations across a range of industries turn to data scientists to help make better organizational decisions and drive business solutions. Howard's fully online M.S. in Applied Data Science and Analytics program trains the next generation of data-driven leaders ready to address real-world problems backed by data intelligence.

Howard Graduate School's fully online interdisciplinary M.S. in Applied Data Science and Analytics program prepares data-driven leaders to generate, analyze, interpret, and present complex data in compelling ways that drive solutions to real-world problems. Whether you are interested in using data science for social impact, predicting the spread of a disease outbreak, or understanding the impacts of racial bias implicit in facial recognition software, predictive policing algorithms, or AI-based financial services, our M.S. in Applied Data Science and Analytics is designed to equip you with the data science skills you need to advance your career and make a meaningful impact in your work. Foundational coursework in data mining and machine learning, predictive modeling and analytics, SQL and relational databases, statistical programming in R and Python, and data visualization provides the skills you need to enhance your decision-making in academic, government, professional, applied research, social impact, and policy settings. With a unique emphasis on the growing importance of data analysis across academic disciplines, our program provides traditional data science opportunities while also offering specializations in minority health & health disparities, social justice, environmental justice, and economic empowerment. You'll tailor your studies to align with your career interests and analyze data to better understand a challenge within an applied field, culminating in a final capstone experience. Situated in the nation’s capital, at the nexus of policy and practice, our program is ripe with opportunities for you to gain insight into the generation, analysis, interpretation, and application of data to real-world problems. Our data science faculty bring their industry expertise in fields ranging from information technology to sociology, to help connect coursework to real-world situations.

Program Snapshot

      ❱  30 credit hours 
      ❱  Full-time / Part-time
      ❱  Online format 

Application Deadlines

    ❱  Fall entry (deadline for priority admission): January 15, 2023
    ❱  Fall entry (final deadline for admission): April 15, 2023

Hard deadline with rolling admissions decisions

Transfer credits accepted (reviewed by committee)


Dr. Amy Yeboah Quarkume

Director of Graduate Studies

Program Details

  • Degree Classification: Graduate
  • Related Degrees: M.S.

Admission Requirements

Application for Admission

  • Online GradCAS application
  • Statement of purpose/ Statement of academic interest (500-1,000 words)
  • GRE scores not required
  • Official transcripts sent to GradCAS
  • 3 letters of recommendation
  • Bachelor's degree from an accredited college or university
  • Resume or Curriculum Vitae
  • Autobiographical statement (500-750 words)

GRE Required?

  • No

GRE Preferred Minimums

  • GRE Verbal Reasoning: N/A
  • GRE Quantitative Reasoning: N/A
  • GRE Analytical Writing: N/A

GPA Required Minimums

  • Overall GPA minimum: 3.0
  • Undergrad GPA minimum: 3.0

Prerequisite Courses

We do not require applicants to come into the program knowing a specific programming language, but it should be noted that Python and R are the preferred languages used in our classes. Taking courses such as Introduction to Programming, Statistics, and Intro to Data Science, will certainly strengthen an application, but it is not required.

Reference Requirements

Evaluator type accepted:

  • Professor (Required)
  • Supervisor/Manager
  • Coworker
  • Other

Evaluator type not accepted:

  • Friend
  • Family Member
  • Clergy