Instructor: Berkay Aydin
Email: baydin2@gsu.edu
Course Webpage: this webpage or can be reached via iCollege
Office Location: 25 Park Pl NE - Room 720
Credit Hours: 4.0 hours
Prerequisites: DSCI 4780 or CSC 4780 or CSC 4740 or CSC 4850 with a grade of “C” or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class. This means the student must be eligible to graduate in the same or the next semester.
Description: The Data Science Operations and Engineering course aims at providing students with an opportunity to integrate their accumulated theoretical knowledge and technical and project management skills to identify and solve a real-world data science problem, with a special emphasis on the application domain. This course has two parts: In the first part, students will learn about data science project lifecycle methodologies, project management, machine learning operations, and governance. In the second part, they will design, manage, and implement a data-intensive analytics project. A capstone project may be sponsored by a partner institution (governmental, NGOs or industry) or by an academic research group, or it can be led by students, pending approval from the course instructor. The Data Science Operations and Engineering course serves as a final preparation for students entering into the profession. Students will learn best practices in data science project management and conduct a team-based project through the entire data science pipeline, by following the steps in popular industry-standard data science processes. These include, but are not limited to, (i) understanding the problem and needs, (ii) task-relevant data collection, exploration and cleaning, (iii) building models to address the problem and designing evaluation pipelines suitable for the problem and its solution, (iv) development and deployment, and lastly (v) communicating the progress and results.
Outcomes: Upon successful completion of this course, students will demonstrate an ability to understand project and lifecycle management processes in data science and handle a problem in data science end-to-end, meaning from the definition of the problem, through assessing the needs and deployment and delivery of the solution. In doing so, they will demonstrate proficiency in collecting and processing real-world task-relevant data, in designing the best data science techniques to solve the problem, in implementing an auspicious solution, and evaluating the robustness and accuracy of their model. Students
will demonstrate competence in presenting material (by delivering proposal, progress and final (deployment) presentations).
will learn how to work in small data science teams with at least one other student on their project.
analyze, and evaluate the implementation of different solutions to various data-related problems.
will learn how to effectively communicate with reports
will employ relevant project management/communication/reporting/development tools and technologies.
Requirements: Students are expected to have advanced programming skills in a data science-appropriate language and understanding of relevant mathematics (including but not limited to calculus, probability, or linear algebra) and data science skills (including but not limited to exploration, pre-processing, learning models, or model evaluation).
Topics Covered:
Introduction to Data Science & Project Management
Data Science Lifecycle & Methodologies
Data Engineering - Basic
Data Engineering - Advanced
Model Development & Experimentation
MLOps Fundamentals
Deployment & Production Readiness
Monitoring, Maintenance & Governance
Projects (2 checkpoints, posters, implementation, and final report)
Grade DSc 4350
A+ [100, ∞)
A [95, 100)
A- [90, 95)
B+ [85, 90)
B [80, 85)
B- [75, 80)
C+ [70, 75)
C [65, 70)
C- [64.99, 65)
D [60, 64.99)
F [0, 60)