Prerequisite Requirements
Important Information About the MS in Applied AI Program Prerequisites and Quiz
- Prerequisites: Students applying to the Applied AI program should be proficient in Python, including data manipulation using Pandas. This can be demonstrated through any one of the following:
- Option 1: A degree (undergraduate or graduate) in data science, computer science, or a related field
- Option 2: MS-level coursework or equivalent education (e.g., bootcamps or MOOCs)
- Option 3: Completion of DTSC 520, DTSC 575, and DTSC 580
Note: DTSC 520 and DTSC 575 count toward the Certificate in Data Science, but do not count toward the MSAI. These courses do count toward the MS in Data Science and MS in Data Analytics programs.
The Applied AI Program Prerequisite Quiz is required but can only be taken by admitted students, as it requires Eastern University credentials. If accepted, you will receive the quiz link and can complete it before paying a deposit or registering.
Curriculum
| Course Number | Course Name | Credits |
|---|---|---|
| Required Courses | ||
| DTSC 540 | Introduction to Artificial Intelligence: Theory, Tools, and Applications | 3 |
| DTSC 670 | Foundations of Machine Learning Models | 3 |
| DTSC 690 | Philosophical and Ethical Issues in Data Science and Analytics | 3 |
| DTSC 691 | Applied Data Science | 3 |
| Electives- Choose 6 courses from these options | ||
| DTSC 620 | Cloud Foundations | 3 |
| DTSC 625 | Data Engineering | 3 |
| DTSC 671 | AI Solutions in the Cloud | 3 |
| DTSC 675 | Mathematics for Data Science | 3 |
| DTSC 680 | Applied Machine Learning | 3 |
| DTSC 685 | Natural Language Processing | 3 |
Course Descriptions
DTSC 540: Introduction to Artificial Intelligence: Theory, Tools, and Applications (3 credits): 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.
DTSC 620: Cloud Foundations (3 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.
DTSC 625: Data Engineering in the Cloud (3 credits): This course provides a comprehensive overview of modern data engineering principles and practices, with a specific focus on implementing these concepts within the cloud. Students will learn to design and build scalable, reliable, and cost-effective data pipelines using a variety of AWS services.
DTSC 670: Foundations of Machine Learning Models (3 credits): Introduction to machine learning landscape. Will address questions such as what is machine learning? Why do we use machine learning? What is machine learning appropriate for? What is it inappropriate for? Will explore supervised and unsupervised learning, regression and classification models, and support vector machines. Taught in Python.
DTSC 671: AI Solutions in the Cloud (3 credits): 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. Prerequisites: DTSC-670 (Machine Learning) with a recommendation of DTSC-620 or prior cloud experience.
DTSC 675: Mathematics for Data Science (3 credits): 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. Prerequisite: DTSC-670 must be completed before taking this course. Previous experience in calculus 1 is necessary to be successful in this course.
DTSC 680: Applied Machine Learning (3 credits): Continuation of DTSC 670. Further exploration of modern machine learning applications. Topics include neural networks and deep learning, including an emphasis on model selection and tuning. Taught in Python. Prerequisite: DTSC 670: Foundations of Machine Learning. Available for MSDS degree only.
DTSC 685: Natural Language Processing (3 credits): This course will introduce the field of Natural Language Processing (NLP) 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. Prerequisite: DTSC 670: Foundations of Machine Learning Models. Available for MSDS & MSAI degree only.
DTSC 690: Data Science Capstone: Ethical and Philosophical Issues in Data Science (3 credits): Part one of the capstone in the Masters in Data Science. Students will explore contemporary ethical and philosophical issues in data science and artificial intelligence. Subjects include basic and advanced issues, ranging from social media privacy to implications of machine learning and artificial intelligence for religiousness. Can be taken jointly with DTSC 691.
DTSC 691: Data Science Capstone: Applied Data Science (3 credits): Part two of the capstone in the Masters in Data Science. Students will also complete a capstone project integrating their learning across courses. Students will complete a project proposal, including their data source, acquisition, cleaning, analysis, and presentation intentions. Can be taken jointly with DTSC 690. Prerequisite: Students must have completed 15 credits to register.