Introduction to the field of data science, including both theoretical and applied components. Students till explore the origins of the field, including links to computer science, statistics, and mathematics. Students will use Python and associated data manipulation and visualization libraries to explore and analyze varied data sets.

This course is designed to teach students best practices in data visualization, key trends in the industry, and how to become better storytellers with data. Students will learn the imporance of using actionable dashboards that enable their organizations to make data-driven decisions.

This course will introduce students to the ethical, philosophical, and interdisciplinary implications of Artificial Intelligence development through the understanding of how machine learning models are created, trained, and used. Students will learn the basics of data, statistics, and models in order to fully engage in conversations surrounding AI development.

This course will introduce statistical program R and build upon prior statistics knowledge. Students will both complete hand calculations and execute them in R.

This course considers the ways data can be organized, cleaned and managed within and between disparate data sets. More formal algorithmic techniques are emphasized with the end of prediction and analysis in mind.

In this course, students will use Python and its libraries to obtain, store, and clean data. Topics include data cleaning, data preparation, data joining and combining, and general data manipulation. This course assumes prior knowledge of Python, NumPy, and Pandas.

This course serves as a capstone for the Data Science Major. The student will apply the techniques learned to actual data sets in their chosen cognate area.

This course will introduce students to various ethical issues related to computing technology and the internet. Free/open source software, cybersecurity, privacy, monopoly power and artificial intelligence will be considered within a Christian framework.

This course serves as a capstone for the Data Science Major. The student will apply the techniques learned to actual data sets in their chosen cognate area.

This course provides an overview of data science and analytics for those with little to no background in coding, statistics, or other technical fields. Students will be introduced to data science and analytics topics, including coding in Python, probability and statistics, critical thinking and problem solving in data contexts, machine learning, and databases. Following the completion of this, students will be fully prepared for DTSC 520: Fundamentals of Data Science.

Introduction to foundational concepts, technologies, and theories of data and data science. This includes methods of data acquisition, cleaning, analysis, and visualization. Taught in Python.

This course is an optional, 0-credit, ungraded lab section meant to supplement coursework in DTSC 520, and will follow the suggested weekly schedule in the main 520 section. Instruction will take place through live lab sessions in which instructors will provide additional learning materials, such as coding problems and supplemental notebooks. The lab is recommended for students with little or no coding background. Students must be enrolled in DTSC 520 to be eligible for the lab.

A comprehensive introduction to the field of artificial intelligence (AI), tailored for graduate students with minimal prior background knowledge in AI or machine learning. The course focuses on foundational theories of AI, ethical and societal implications of AI technologies, and practical skills in using modern AI tools. This is an interdisciplinary course appropriate for learners from all disciplines.

Introduction to foundational concepts, theories, and techniques of statistical analysis for data science. Students will begin with descriptive statistics and probability, and advance through multiple and logistic regression. Students will also conduct analyses in R. This course is approachable for students with little statistical background and prepares them for DTSC 650.

This course explores the various ways data and science can be applied to business contexts. Particular emphasis will be placed on analytics using data to make informed business decisions. Approachable for students who have taken DTSC-550 or have an understanding of basic statistics and beginner-level experience with R.

This course will teach students the introductory skills of programming, problem solving and algorithmic thinking in Python. Topics include variables, input/output, conditional statements/logic, Boolean expressions, flow control, loops and functions. Approachable for students who have taken DTSC 520 or have beginner-level experience with Python.

This course focuses on the loading, manipulating, processing, cleaning, aggregating, and grouping of data. Students will practice on real world data sets, learning how to manipulate data using Python and continue their study of intermediate and advanced topics from the NumPy and Pandas libraries. Students should have taken DTSC 520 and DTSC 575, or have previous Python for data analysis knowledge/experience.

This course aims to provide DTSC students with the skills they need to identify, pursue, and attain their career goals. By offering clarity around some of the most challenging obstacles applicants face in the job search process, including resume writing, networking skills, and experiential learning opportunities, this class will prepare learners to enter into (or progress in!) their chosen field with confidence. Additionally, this course will encourage students to develop their skills in the NACE Career Readiness Competencies, a compilation of some of the most sought-after skills employers are looking for today. Our goal is to demystify the job application process and equip students to identify next steps, navigate career decisions, and seek opportunities that empower them to serve and thrive.

This course is designed to teach students the best practices in Data Visualization, the key trends in the industry, and how to become great storytellers with data. Students taking this class will learn the importance of using actionable dashboards that enable their organizations to make data-driven decisions. For this class students will use Tableau, one of the most used visual analytics platforms in the industry.

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.

This course is a continuation of DTSC 550, with an emphasis on statistical techniques most used in modern data science. Students will explore in greater depth linear and logistic regression, and continue to additional regression and classification techniques with a focus on application. Analyses will be completed in R.

This course offers a comprehensive overview of data organization and management using relational database systems and the SQL programming language. The course introduces students to database systems and their applications with a focus on designing and implementing database solutions based on user and data requirements.

Introduction to the machine learning landscape. Will address questions such as what is machine learning, why do we use machine learning, and what is machine learning appropriate and inappropriate for? The course will explore supervised and unsupervised learning, regression and classification models, decision trees and ensemble learning, along with other traditional machine learning algorithms. Taught in Python. Students should have taken DTSC 520, DTSC 575, and DTSC 580 or have previous Python for data analysis knowledge/experience.

This course will introduce students to situations when artificial intelligence (AI) or machine learning (ML) solutions would be advantageous, with a particular focus on cloud computing. Students will utilize common cloud services, along with advanced services, to create automated solutions to solve problem cases. Note: Students will use their personal AWS account and will incur charges to complete assignments.

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 throught the course to reinforce learning concepts. Prerequisites: DTSC-670 must be completed before taking this course. Previous experience in calculus 1 is necessary to be successful in this course.

Continuation of DTSC 670. This course will further 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.

This course will introduce the field of Natural Language Processing and its related algorithms and ideas. Students will gain experience writing NLP algorithmic code in python, as well as working through text-based machine learning problems.

Students will explore contemporary ethical and philosophical issues in data science, analytics, and artificial intelligence. Students will engage with a wide range of interdisciplinary readings examining moral challenges and responsibilities inherent in the development and deployment of new data-driven technologies. Topics include societal and psychological impacts of AI, challenges of misinformation and algorithmic bias, the complexities of privacy and surveillance, and global implications of technological development.

Students will complete a capstone project challenging students to integrate and apply the knowledge and skills gained throughout the coursework. Students will conceive and execute a comprehensive project, from proposal through final presentation. The capstone project is a showcase of student capability to independently navigate complex, data-centric problems, and formulate viable, data-driven solutions. Prerequisites: Students must have completed 15 credits to register.

Students who have not successfully completed their DTSC 691 Applied Data Science coursework by the end of DTSC 691 must register for DTSC 692 until the project is completed. Gradings is pass/no credit. These credits do not accure although students are billed for three credits.

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.

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.

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. Students in DTSC 780 will complete all of DTSC 680 as well as additional PhD-level work, as described in the syllabus.

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.

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.

Building upon DTSC 830, 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.

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.

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.