Education, Training, Courses

Project Overview

The transdisciplinary education in data science and analytics cannot be accomplished by simple collecting existing courses from different departments into a new degree program. Therefore, we propose the transdisciplinary approach in which students must learn how the different disciplines interact and relate to each other. Our solution will apply innovative approaches: (a) develop normalization courses, (b) develop in-depth transdisciplinary courses, and (c) create different testbeds from different application domains.

Technical Principles and Courses

Technical topics will be incorporated in two types of courses: normalization courses and in-depth elective courses. Normalization courses will serve to provide students with different background with the basics of data science and analytics and its applications. They will also provide a strong foundation in data science skills including statistics, probability, mathematics, data mining, machine learning, and predictive analytics. Testbed approach will provide students with on-hands experience in several critical data science applications. For example, one testbed will be designed to provide platform, software tools, and data for early detection of some medical illnesses, such as melanoma and brain tumor. Table 3 shows the proposed normalization courses, and in-depth elective courses.

Tables below show typical 3-semester schedule for Master students (it includes summer semester), and program schedule for PhD students.

Typical Course Schedule for Master Students (Total 10 courses, 30 credits).

Semester 1	Semester 2	Summer Semester	Semester 3
3 normalization courses Booth-camp sessions	2 normalization courses + 1 in-depth elective course or 3-in-depth elective courses	1 in-depth elective course	3 in-depth elective courses

 

Typical Course Schedule for PhD Students (Total 6 courses,18 credits), and Dissertation Research (33 credits).

Semester 1	Semester 2	Semester 3+
1-3 normalization courses Up to 2 in-depth elective courses Booth-camp sessions	3 in-depth elective courses	Qualifying exam Dissertation research Professional development Communication training

 

Normalization Courses

The main objective is to solve the problem of students entering graduate programs in data science and analytics, who have different backgrounds. For this reason, we will develop five normalization courses (full semester courses), which will provide students with the critical background in data science and analytics. Master students will take minimum 3 normalization courses, however they may take all 5 courses depending on their background. PhD students will take 1-3 normalization courses depending on their needs.

All normalization courses will be developed by two faculty members from different disciplines. The faculty names are shown in Table 3. The same approach will be taken in developing in-depth elective courses; they will be developed by two faculty from different disciplines. In this way, we will be able to show the transdisciplinary view.

For example, Data Mining and Machine Learning course will be developed by Dr. Khoshgoftaar (Computer Science) and Dr. Barenholtz (Science, Machine Learning and Cognitive Robotics Lab). Similarly, Introduction to Data Science course will be developed by Dr. Newman (Statistician, College of Nursing), and Dr. Marques (Computer Science).

Testbeds, Boot-camps, and Case Studies

We will develop multiple testbeds from different application domains. The focus will be on the following application modules: medical and healthcare, industry applications, and data science technologies including AI.

Each testbed will include (a) computer platform, (b) software tools, and (c) set of learning modules. The learning modules will be designed in such a way to teach specific concepts in the course. The course will in such a way employ hands-on projects, and team-based educational methods. Application themes and the testbeds will be based on active and funded transdisciplinary research projects in data science and analytics.

Booth-camps sessions will be organized every semester for new students to get them familiar with the platforms for various test-beds. The booth-camps will consist of student teams from different departments working with 2 faculty members from different disciplines.

 

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Previous Courses Offered

Cohort 1 (Fall 2021 - Summer 2022) 

• Introduction to Data Science
• Deep Learning 
• Computational Foundations of AI 
• Data Mining and Machine Learning
• Practical Aspects of Modern Cryptography
• Advanced Internet Systems
• Summer choices: Intro to Neural Networks, Cloud Computing 

Cohort 2 (Fall 2022 - Summer 2023)     

• Artificial Intelligence in Medicine and Healthcare
• Deep Learning 
• Information Retrieval
• Natural Language Processing
• Advanced Internet Systems
• Intro to Data Science
• Summer choices: Artificial Intelligence, Computer Performance Modeling, Software Engineering   

Cohort 3 (Fall 2023 - Summer 2024)     

• Artificial Intelligence in Medicine and Healthcare
• Intro to Data Science
• Video Communications
• Advanced Internet Systems
• Practical Aspects of Modern Cryptography
• Deep Learning 
• Summer choices:  Data Mining and Machine Learning, Artificial Intelligence, Intro to Neural Networks, Computer Performance Modeling

Cohort 4 (Fall 2024 - Summer 2025)   

• Introduction to Data Science
• Deep Learning
• Natural Language Processing
• GenAI Software Dev. Lifecycles
• Artificial Intelligence
• Data Mining and Machine Learning
• Summer choices:  TBA