DTSC 720: Cloud Foundations:This course will introduce students to the advantages and vocabulary of cloud computing. Students will gain exposure and experience with cloud-based core resources for compute, storage, database, and networking tasks. Students will explore best practices for cloud architecture, including operational excellence, security, shared responsibility, cost optimization, reliability, and scalability. Students in DTSC 720 will complete all of DTSC 620 as well as additional PhD-level work, as described in the syllabus.

DTSC 775: Mathematics for Data Science: This course provides a comprehensive introduction to the mathematical foundations of data science. Students will explore topics in linear algebra and multivariate calculus, focusing on their applications in data science. The course aims to build the mathematical framework necessary for understanding various machine learning models and algorithms. Python programming will be used throughout the course to reinforce learning concepts. Previous experience in calculus 1 is necessary to be successful in this course. 

DTSC 780: Applied Machine Learning: This course will explore modern machine learning applications such as deep learning methods. Special attention will be given to image classification and object detection. Students will also focus on different dimensionality reduction techniques with emphasis on using principal component analysis. Additionally, students will learn to operationalize machine learning models using Flask. Students must have prior machine learning knowledge. Before enrolling in this course, students should confirm their knowledge of materials covered in DTSC 670 by emailing dsadvising@eastern.edu and datascience@eastern.edu and filling out their evaluation form. Students in DTSC 780 will complete all of DTSC 680 as well as additional PhD-level work, as described in the syllabus.

DTSC 810: Academic Writing: This course focuses on developing clear, precise, and compelling research communication skills for PhD-level academic inquiry, tailored for scientific publications, grant proposals, and dissertations.  Students will learn the principles of structuring research papers, writing with clarity and coherence, integrating data-driven narratives, and adhering to style guidelines.  Through lectures, discussion, and literature review, students will refine their ability to present complex concepts effectively to both specialized and interdisciplinary audiences. 

DTSC 830: Research Methods I: This course introduces the core principles of research design, methodology, and ethical considerations in applied data science. Topics include formulating research questions, experimental vs. observational studies, causal inference, statistical and computational approaches to data collection, and reproducibility in research. Students will also learn about data governance, bias in algorithms, and ethical AI. The course emphasizes critical evaluation of research literature and prepares students for designing their own studies.

DTSC 831: Research Methods II: Building upon DTSC 830: Research Methods I, this course provides advanced methodological training in applied data science research, with a focus on experimental design, machine learning interpretability, and mixed-methods approaches. Students will explore cutting-edge techniques in causal inference, Bayesian modeling, and digital ethnography while critically examining the ethical implications of AI-driven research. Through hands-on exercises and case studies, students will develop the skills necessary to design rigorous and reproducible research studies that integrate quantitative and qualitative methodologies.

DTSC 850: Seminar in Quantitative Methods: This seminar provides an advanced, discussion-driven exploration of quantitative methods in applied data science, grounded in the critical analysis of academic literature. Students will engage with a diverse selection of research articles covering statistical modeling, experimental design, Bayesian inference, multivariate analysis, and high-dimensional data techniques. Readings will provide a broad foundation while allowing students the flexibility to explore specialized topics of interest through independent research. Weekly discussions will encourage students to synthesize knowledge from prior coursework, evaluate methodological approaches, and consider the implications of quantitative techniques in real-world applications. The course culminates in an individual research project where students deepen their expertise in a chosen area of quantitative methods and contribute to the broader academic discourse in data science.

DTSC 860: Seminar in Qualitative Methods: This seminar provides an in-depth exploration of qualitative methodologies in applied data science, focusing on their role in understanding complex social, organizational, and human-centered problems. Students will critically analyze qualitative research approaches such as ethnography, grounded theory, focus groups, and case study research. The course will incorporate discussion-based learning, review of key academic literature, and hands-on experience with qualitative data collection techniques, including interviews, focus groups, and digital ethnography. Through structured weekly discussions, students will examine how qualitative insights complement quantitative methods in data science. The course culminates in an independent research project where students apply qualitative methods to a data science-related research question.

DTSC 870: Seminar in ethics in data science and analytics: In this culminating seminar, students critically examine the ethical, social, and philosophical aspects of data science, analytics, and artificial intelligence.  Through readings, discussion, and case studies, this course explores pressing contemporary issues such as algorithmic decision making and bias, global perspectives on data-driven technologies, impacts of automation on labor and the workforce, and religious, cultural, and moral interpretations of artificial intelligence. The course emphasizes the development and application of ethical reasoning to data-related practices in the design, deployment, and evaluation of data technologies. By the end of this seminar, students will be able to articulate ethical strengths and limitations of data use related to their dissertation research and professional domain.