Data Engineering, M.S.

Program Overview

The Master of Science in Data Engineering program at the University of Wisconsin-Madison equips students with the skills to manage data at scale. The program emphasizes data collection, storage, management, and processing, preparing graduates for careers in data engineering, data science, and related fields. The curriculum includes core courses in distributed systems, big data systems, database management systems, and machine learning, along with electives in algorithms, systems, and human-computer interaction.

Program Outline

Degree Overview:

Overview:

The Master of Science in Data Engineering program at the University of Wisconsin-Madison focuses on the principles and practices of managing data at scale. It emphasizes the valid and efficient collection, storage, management, and processing of datasets to support computation and data-driven systems important to data science and data analytics functions.

Objectives:

• To provide students with a comprehensive understanding of the principles and practices of data engineering.
• To develop students' skills in designing, implementing, and evaluating data engineering solutions.
• To prepare students for careers in data engineering and related fields.

Program Description:

The MS in Data Engineering program consists of 30 credits of coursework, including:

• Data Engineering Foundations: 12 credits of core courses in distributed systems, big data systems, database management systems, and machine learning.
• Machine Learning Requirement: 6 credits of coursework in machine learning, statistical pattern classification, or deep learning.
• Algorithms Requirement: 3 credits of coursework in algorithms or optimization.
• Systems Requirement: 3 credits of coursework in operating systems, computer networks, or mobile systems.
• Humans and Data Requirement: 3 credits of coursework in data visualization or human-computer interaction.
• Approved Electives: 3 credits of coursework from a list of approved electives.

Outline:

Content:

The MS in Data Engineering program covers a wide range of topics, including:

• Data collection and storage
• Data management and processing
• Data analysis and visualization
• Machine learning
• Algorithms
• Systems
• Human-computer interaction

Structure:

The program consists of 30 credits of coursework, which can be completed in 3-4 semesters of full-time study. Part-time study is also an option.

Course Schedule:

The course schedule for the MS in Data Engineering program is available on the program website.

Individual Modules with Descriptions:

• COMP SCI 739 Distributed Systems: This course introduces the concepts and techniques of distributed systems, including distributed consensus, fault tolerance, and load balancing.
• COMP SCI 744 Big Data Systems: This course covers the principles and practices of big data systems, including data storage, processing, and analysis.
• COMP SCI 764 Topics in Database Management Systems: This course explores advanced topics in database management systems, including data modeling, query optimization, and transaction processing.
• COMP SCI 838 Topics in Computing: This course covers a variety of topics in computing, including machine learning, data mining, and natural language processing.
• COMP SCI 540 Introduction to Artificial Intelligence: This course provides an overview of the field of artificial intelligence, including topics such as search, planning, and machine learning.
• COMP SCI/E CE 760 Machine Learning: This course covers the principles and algorithms of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
• COMP SCI 762 Advanced Deep Learning: This course explores advanced topics in deep learning, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
• STAT 451 Introduction to Machine Learning and Statistical Pattern Classification: This course introduces the basic concepts of machine learning and statistical pattern classification, including supervised learning, unsupervised learning, and feature selection.
• STAT 453 Introduction to Deep Learning and Generative Models: This course introduces the basic concepts of deep learning and generative models, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
• STAT 615 Statistical Learning Algorithms: This course covers the theory and algorithms of statistical learning, including linear regression, logistic regression, and support vector machines.
• COMP SCI/E CE/ISY E 524 Introduction to Optimization: This course introduces the basic concepts of optimization, including linear programming, nonlinear programming, and convex optimization.
• COMP SCI 577 Introduction to Algorithms: This course covers the basic concepts of algorithms, including asymptotic analysis, data structures, and algorithm design techniques.
• COMP SCI/ISY E/MATH/STAT 726 Nonlinear Optimization I: This course covers the theory and algorithms of nonlinear optimization, including unconstrained optimization, constrained optimization, and convex optimization.
• COMP SCI 407 Foundations of Mobile Systems and Applications: This course introduces the basic concepts of mobile systems and applications, including mobile operating systems, mobile application development, and mobile networking.
• COMP SCI 537 Introduction to Operating Systems: This course introduces the basic concepts of operating systems, including process management, memory management, and file systems.
• COMP SCI 564 Database Management Systems: Design and Implementation: This course covers the principles and practices of database management systems, including data modeling, query optimization, and transaction processing.
• COMP SCI 640 Introduction to Computer Networks: This course introduces the basic concepts of computer networks, including network protocols, network topologies, and network security.
• COMP SCI/E CE 707 Mobile and Wireless Networking: This course covers the principles and practices of mobile and wireless networking, including cellular networks, Wi-Fi networks, and Bluetooth networks.
• COMP SCI 740 Advanced Computer Networks: This course explores advanced topics in computer networks, including network performance analysis, network security, and network management.
• COMP SCI 765 Data Visualization: This course covers the principles and practices of data visualization, including data visualization techniques, data visualization tools, and data visualization applications.
• COMP SCI/ED PSYCH/PSYCH 770 Human-Computer Interaction: This course covers the principles and practices of human-computer interaction, including user experience design, user interface design, and user evaluation.

Assessment:

Students in the MS in Data Engineering program are assessed through a variety of methods, including:

• Exams
• Projects
• Presentations
• Papers

Teaching:

The MS in Data Engineering program is taught by a team of experienced faculty members who are active in research and industry. The program uses a variety of teaching methods, including:

• Lectures
• Discussions
• Labs
• Projects

Careers:

Graduates of the MS in Data Engineering program are prepared for careers in a variety of fields, including:

• Data engineering
• Data science
• Data analytics
• Machine learning
• Artificial intelligence
• Software engineering
• Systems engineering