Practitioner - Healthcare

Admission Requirements

18 years of age or older

Program Overview

Sample courses include:

Cloud Foundations 
Students will be exposed to the current practices in cloud computing. Topics may include cloud service models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), technologies supporting cloud computing such as virtualization, motivating factors, benefits and challenges of the cloud, cloud storage, performance and systems issues, disaster recovery, federated clouds, data centers, and cloud security. Students will be exposed to a leading public cloud computing platform.

Blockchain 
This course will be an introduction to Blockchain. There will be a modularized approach, to focus development on the skills for each aspect of blockchain. Labs based on practical applications e.g. bitcoins are incorporated.

Data Mining 
This module provides an overview of basic concepts in data mining and main methodologies and approaches used for knowledge discovery from data. Topics include the definition of data mining field; data mining process; data preprocessing; predictive and descriptive data mining; evaluation of data mining models; and privacy and security of data. The module uses case studies from healthcare.

Cognitive Computing
This module provides lectures and labs on deep learning and machine learning using combination of tools such as cognitive services APIs, Deep Learning frameworks and libraries. Two major breakthroughs for cognitive computing in healthcare are: 1) consumer/user engagement (e.g. IBM Watson social network Welltok) and 2) discovery applications, such as drug discovery and analysis of human health. Internet of Medical Things integrates sensors and AI algorithms, which are vulnerable to cyber-attacks. Examples of attacks are: 1) access by malicious actors; 2) loss or corruption of enterprise information and patent data. Topics include: Neural Networks, Convolutional Neural Network (CNN), Long – Short Term Memory (LSTM), Natural Language Processing (NLP) and Deep Reinforcement Learning (Deep RL).

Information Security 
This module is designed to give students a basic competency in the principles of information security and how they relate to computing systems, particularly at the level of the operating system. Key areas of focus include fundamental security design principles, the adversarial model, data security, virtualization, viruses & malware, and operating-system specific information security concepts for both desktop and mobile systems.

Network Security 
The module discusses fundamental concepts and principles of network security. The course covers basic security topics, including networking basics, network traffic signatures, cryptography, wireless networking, wireless security, firewalls, IDPSs, virtual private network, and web security.

Digital Forensics 
This module covers the fundamentals of digital forensics. Topics will include historical issues, key concepts, and tools and techniques of the trade. In addition, reporting methods will be discussed. Various open-source tools will be used in the lab portion of the module.

Cyber Threat Hunting 
This module covers the basics of cyber threat hunting in a secure operations center. Students will learn the mindset of the adversary and methods to identify new and emerging cyber threats in an organizational environment. Topics include log analysis, event correlation, open-source threat intelligence, use of AI in threat identification, honeypots, and threat sharing. Students will practice new skills in a simulated threat environment.

What You'll Learn

• Moderate knowledge on cybersecurity threats, attacks, vulnerabilities
• Network traffic
• Blockchain
• Cloud security