Professional Diploma outline

1.Core Courses:

Introduction to Data Engineering (3 credit hours)

The emergence of massive datasets provides the primary drive for the field. Data can be video surveillance, social media, speech, text, telecommunications, astronomy, etc. This course emphasizes the practical techniques for working with large-scale data.

Introduction to Machine Learning (3 credit hours)

The course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms such as deep neural computing, and kernel methods.

Computer Systems for Data Engineering (3 credit hours)

This course is an introduction to computer architecture and distributed systems with emphasis on warehouse scale computing systems. Topics include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism.

Exploratory Data Analysis and Visualization (3 credit hours)

This course covers fundamentals of data visualization, such as layered grammar of graphics, perception of discrete and continuous variables, introduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification..

Data Engineering Lab 1 (1 credit hour)

This lab covers main programming paradigms for machine learning as well as platforms, including programming in Python, R, using tools such as Anaconda.

Data Engineering Lab 2 (1 credit hour)

This lab covers more advanced projects in data engineering in addition to data management and cloud engineering.

2.Elective courses:

In addition to the core courses, participants can choose 2 elective courses (3 credit hours each) from the list below:

• Probability and Statistics for Data Engineering
• Topics in Engineering: Applied Machine Learning
• Bioinformatics
• Applied Machine Learning for Image Analysis
• Deep Learning for Computer Vision, Speech, and Language
• Introduction to Big Data in Finance
• Fundamentals of Cloud Computing
• Quantum Communication and Cryptography
• Distributed Databases