Pre-req: CS1010/E/% and [ EE2012A or ST2334 or {ST2131/MA2216 and ST2132} ]

Pre-req Advisory:
-Minor in Data Engineering students should take EE3801 before EE4802
– Familiarity with scientific programming language such as Python. All assignments in class will be done in Python.
% is a wildcard for any of the CS1010 variants

Workload: 2-0-1-2-5

This course covers the fundamental principles of data engineering, which includes the tools and technologies to build the data pipelines and data services needed to do find insights in big data. Specific topics include data collection, data cleansing, data wrangling, and data integrity. Techniques for data analytics, data storage and retrieval, and data visualization will also be covered. In addition to basic principles of data engineering, the course will expose students to open source industry tools and best practices, as well as ethical considerations.

Pre-req: CS1010/E/% and [ EE2012A or ST2334 or {ST2131/MA2216 and ST2132} ]
Preclusion / Anti-req: EE4211, CS3244, IT3011

Pre-req Advisory:
1. Minor in Data Engineering students should take EE3801 before EE4802
2. Familiarity with scientific programming language such as Python. All assignments in class will be done in Python.

Workload: 3-1-0-2-4

This course teaches students data analytics and machine learning techniques for solving large scale engineering problems. The course covers statistics and machine learning concepts and algorithms such as linear regression, support vector machine, decision trees, feature engineering, deep learning, and reinforcement learning. How these algorithms are scaled up and incorporated in data engineering pipelines to tackle large scale problems will be covered. Students will be exposed to practical case studies of engineering problems with realistic datasets or streaming data, and perform data engineering operations using software tools.

Pre-req: Nil
Workload: 1.5-0.5-0-2-1
This course provides an introduction and exposure to the technology concepts that underlie blockchain and its applications. The engineering considerations of a blockchain system will be discussed along with examples from different fields including data science and engineering. Topics include distributed computing systems and their problems, peer-to-peer networks and distributed ledgers, trust models for blockchain and the essentials of cryptography. We will also look at the value proposition of blockchain technology solutions.