MS in Applied Data Science

2 Years On Campus Masters Program

Howard University

Program Overview

The Master of Science in Applied Data Science at Howard University is an interdisciplinary graduate programme designed to prepare students to use data science and analytics to address complex real-world challenges, with a particular emphasis on social justice, equity, and community impact. The programme combines statistical analysis, programming, machine learning, data visualization, data engineering, and ethical data practices, allowing students to develop practical expertise while exploring specialized areas such as Minority Health and Health Disparities, Environmental Justice, and Economic Empowerment.

Curriculum Structure

The programme is designed as a three-semester course of study and requires approximately 30 credit hours, including required courses, electives, a graduate capstone, and a practicum or internship. Students can pursue the programme full-time by taking three courses per semester or part-time by enrolling in one or two evening courses each semester. The curriculum emphasizes practical data science applications, social impact research, and responsible technology development.

Year 1

During the first year, students establish a foundation in data science, statistical analysis, and ethical computing through courses such as Introduction to Applied Data Science (DATA-200), Computational Social Justice (DATA-201), and Statistically Measuring and Modeling Social Justice (DATA-202). These subjects introduce data cleaning, statistical analysis, data storytelling, visualization, research methods, data privacy, intellectual copyright, and the ethical implications of artificial intelligence.

Students then develop practical analytical and computational skills through Data Storytelling and Visualization (DATA-301), Applied Data Science for Social Impact (DATA-302), Applied Machine Learning, Bias and Ethics (DATA-303), and Engineering and Managing Data-Driven Change (DATA-304). These courses cover Tableau-based visualization, machine learning algorithms, predictive modelling, supervised and unsupervised learning, SQL, data engineering, and the use of real-world datasets to investigate social and economic challenges.

Year 2

During the final stage of the programme, students can deepen their expertise through specialized electives such as Foundational Python for Data Science (DATA-203), Data Science and Environmental Justice (DATA-400), Introduction to Geographic Information Systems (DATA-401), Text as Data (DATA-402), and Advanced Python for AI and Data Science (DATA-404). These courses develop advanced programming, geospatial analysis, natural language processing, text analytics, and computational skills for addressing challenges involving environmental sustainability, public health, and social inequality.

Students complete the Data Science Graduate Capstone (DATA-801) and Data Science Practicum/Internship (DATA-802), applying their academic knowledge to an original research project, industry internship, government assignment, or supervised practical project. Students can also explore specialized subjects such as Genomic Data Science (DATA-405) and Independent Study (DATA-410), allowing them to investigate advanced topics related to their academic and professional interests.

Focus Areas

Applied Data Science, Data Analytics, Social Justice, Computational Social Justice, Minority Health and Health Disparities, Environmental Justice, Economic Empowerment, Statistical Analysis, Machine Learning, Artificial Intelligence, Data Visualization, Data Storytelling, Data Engineering, Python Programming, SQL, Predictive Modelling, Data Mining, Natural Language Processing, Text Analytics, Geographic Information Systems, Geospatial Analytics, Genomic Data Science, Data Ethics, Responsible AI, Social Impact Research.

Learning Outcomes

Develop practical knowledge of applied data science and analytics, collect and manage complex datasets, apply statistical analysis and inferential methods, develop machine learning and predictive models, use Python and SQL for data processing and analysis, implement data visualization and storytelling techniques, apply supervised and unsupervised learning algorithms, identify and address bias in artificial intelligence, understand data privacy and ethical computing practices, perform geospatial and text-based data analysis, apply data engineering techniques to real-world problems, evaluate social and economic inequalities using data, conduct independent research and practical projects, communicate analytical findings effectively, and develop data-driven solutions that promote social justice, environmental sustainability, and economic empowerment.

Professional Alignment (Accreditation)

The programme is offered through Howard University's Center for Applied Data Science and Analytics, which coordinates interdisciplinary data science education and research. Its curriculum emphasizes responsible artificial intelligence, data privacy, ethical computing, statistical analysis, and practical applications of data science to social challenges. Students also gain opportunities to work with community organizations, government agencies, research institutions, and corporate partners through internships and practical projects.

The official programme information does not specify a separate external professional accreditation for this master's degree.

Reputation (Employability Rankings)

Howard University emphasizes interdisciplinary education and practical data science applications through collaborations across five schools and colleges, representing more than 50 academic areas. Its Center for Applied Data Science and Analytics facilitates research and educational partnerships with organizations including the Social Science Research Council, data.org, the Distributed AI Research Institute, Esri, Accenture, Amazon, Google, Mastercard, and AstraZeneca.

The programme prepares graduates for careers in data science, analytics, research, and technology-related fields. Potential career opportunities identified by the university include Data Scientist, Data Analyst, Data Mining Specialist, Database Manager, Statistician, Program Evaluation Analyst, Decision Support Analyst, Research Analyst, Opinion Polling Statistician, and Community Intelligence Expert. Graduates can pursue opportunities in government agencies, nonprofit organizations, healthcare, financial services, research institutions, and technology companies.

The official programme information does not publish a specific graduate employment rate, average starting salary, or programme-specific QS or Guardian employability ranking.

Experiential Learning (Research, Projects, Internships etc.)

At Howard University, the MS in Applied Data Science program is designed to provide students with hands-on experience that bridges the gap between theoretical knowledge and real-world application. Students engage in practical learning through various projects, internships, and access to state-of-the-art facilities and tools. This experiential learning approach ensures that graduates are not only proficient in data science concepts but also equipped with the skills necessary to tackle complex problems in diverse settings.

Here are some key aspects of the experiential learning opportunities available in the program:

- Collaborative Projects: Students work on group projects that simulate real-world data challenges, fostering teamwork and problem-solving skills.
- Internship Opportunities: The program encourages internships with industry partners, allowing students to gain valuable experience and network within the field.
- Access to Advanced Software: Students utilize industry-standard software such as Python, R, SQL, and Tableau, ensuring they are familiar with tools commonly used in data science.
- Research Laboratories: Howard University provides dedicated research labs equipped with the latest technology for data analysis and visualization, enabling students to conduct meaningful research.
- Data Science Institute: The university hosts the Data Science Institute, which offers additional resources, workshops, and seminars to enhance students' learning experiences.
- Field Trips: Students have opportunities to visit tech companies and data-driven organizations, providing insights into the industry and potential career paths.
- Library Resources: The university's libraries offer extensive collections of data science literature and access to online databases, supporting students' research and learning needs.

These experiential learning components are integral to the MS in Applied Data Science program, ensuring that students graduate with both the knowledge and practical skills needed to excel in their careers.

Embarking on this journey at Howard University will not only enhance your academic experience but also prepare you for a successful career in the dynamic field of data science!

Progression & Future Opportunities

The MS in Applied Data Science at Howard University equips graduates with the skills and knowledge to excel in a rapidly growing field. Alumni have successfully transitioned into roles such as Data Scientist, Data Analyst, Business Intelligence Analyst, and Machine Learning Engineer. With a strong foundation in data science, graduates are well-prepared to meet the demands of various industries.

Here’s how Howard University supports your journey toward a successful career in data science:

- Career Services: The university offers dedicated career counseling, resume workshops, and interview preparation to help you stand out in the job market.
- Employment Statistics: Graduates of the program have reported a high employment rate, with many securing positions within six months of graduation. The average starting salary for data science roles is around $85,000, with potential for growth as you gain experience.
- University–Industry Partnerships: Howard has established partnerships with leading companies in technology and analytics, providing students with internship opportunities and real-world projects that enhance learning and employability.
- Accreditation Value: The program is accredited by recognized bodies, ensuring that your degree holds value in the job market and meets industry standards.
- Graduation Outcomes: Graduates have gone on to work for top organizations, contributing to impactful projects and advancing their careers in data science.

Further Academic Progression: After completing the MS in Applied Data Science, you may choose to pursue a Ph.D. in Data Science or a related field, which can open doors to advanced research opportunities and academic positions. Additionally, you could consider certifications in specialized areas such as machine learning, artificial intelligence, or big data analytics to further enhance your expertise and career prospects.

Choosing Howard University for your graduate studies in Applied Data Science is a step toward a promising future filled with opportunities.