Information for Employers
Business, banking, and big data are a winning combination! Meet former SFU professional master's students Abhishek Arora and Amit Tiwari who joined RBC for their co-op placement in summer 2016.
Are you an employer looking for big data, visual computing or cybersecurity talent? SFU's professional master's students complete a co-op semester as part of their degree. For many students, co-op is a great opportunity to gain hands-on work experience by applying the skills and knowledge they have obtained in the classroom to real world challenges. Employers benefit from the flexibility of the program, the simple hiring process, and, most of all, the recruitment advantage: make better hiring decisions by working with potential employees before you recruit!
BCIC is offering the BC Tech Co-op Grants Program to assist small technology firms with funding their first co-op student.
RHI and BuildDirect are just two of the many organizations that take advantage of the professional master's program's co-op component. Will you?
Big Data Co-op: The Rick Hansen Institute Experience
Catch a glimpse into Manekta's Co-op experience at the Rick Hansen Institute, where she uses machine learning algorithms and complex statistical tools to help develop a mortality risk index for spinal cord injury.
Big Data Co-op: The BuildDirect Experience
Ankit describes his Co-op experience at BuildDirect, where he uses predictive analytics techniques, such as lead grading and demand forecasting, to improve sales and create efficiencies in the company's operations.
The Big Data curriculum covers the following areas:
- Analysis of scalability of algorithms to big data.
- Data warehouses and online analytical processing.
- Efficient storage of big data including data streams.
- Scalable querying and reporting on massive data sets.
- Scalable and distributed hardware and software architectures.
- Software as a service. Cloud Computing (e.g. Amazon EC2, Google Compute Engine).
- Big data programming models: map-reduce, distributed databases, software for implementing streaming and sketching algorithms.
- Dealing with unstructured data such as images, text or biological sequences.
- Scalable machine learning methods such as online learning.
- Data mining: methods for learning descriptive and predictive models from data.
- Distributed algorithms over very large graphs and matrices.
- Social media analysis.
- Visualization methods and interactive data exploration.
What Can Big Data Students Do?
As part of their program, big data students gain skills in the following areas:
- Large-scale data processing
- Apache Hadoop ecosystem
- Distributed file systems
- Map/Reduce programming model
- Large graph analysis
- Relational databases
- Nosql databases
- Social media APIs
- Cloud computing services
- Visualization tools
The Visual Computing curriculum covers the following areas:
- Fundamentals and advanced knowledge of representation, modelling, understanding, and manipulation of visual data, e.g. images, videos, 3D objects, and virtual scenes.
- Machine learning and data-driven techniques in visual computing.
- Geometric, procedural, and physics-based modelling in computer graphics and computer animation.
- Pattern and action recognition in images and video.
- Visual data acquisition, e.g. computational photography, laser scanning, and geometry and motion tracking.
- Human-computer interfaces and interactive techniques for visual computing.
- Fundamentals and advanced technologies in augmented/mixed/virtual reality.
- Visual computing on specialized data, e.g. medical, simulation, AR/VR, and robotics, etc.
- Computational design and fabrication, e.g. 3D printing.
What Can Visual Computing Students Do?
As part of their program, visual computing students gain skills in the following areas:
- Machine learning
- SLAM systems
- Image and geometry processing
- Computer vision
- Visualization systems and tools
- Multi-robot and multi-sensor systems
- Computational fabrication
- Computational photography
- HCI and interactive systems
The Cybersecurity curriculum covers the following areas:
- Fundamentals and advanced knowledge of information security, risk management, situation analysis, data analytics, applied cryptography, cyber ethics and cyber forensics
- Penetration testing and ethical hacking with hand-on experience to assess risks associated with potential security breaches
- Studying attacks on computer systems, network and cloud infrastructure and how to prevent and detect them, protocol-specific attacks and generic attacks, new technologies related to containers, IoT and 5G
- Machine learning and predictive analytics for cybersecurity risk assessment, intrusion detection and prevention, and critical infrastructure protection
- Cyber risk assessment and mitigation strategies to identify, estimate, and prioritize cyber risks, threats and vulnerabilities
- Secure software design, rigorous development and use of software that reliably preserves the security properties of the information and systems it protects
- Applied cryptography, cryptographic and cryptoanalytics techniques such as cryptographic primitives, public key encryption, digital signature, message authentication codes and cryptographic protocols
- Cybersecurity of blockchain technology and cryptocurrencies
- Information privacy and fundamental privacy concepts in a broad sense with emphasis on challenging and emerging topics in privacy
What can Cybersecurity students do?
- Information security and privacy
- Penetration testing and ethical hacking
- System, network and cloud security
- Intrusion detection and prevention
- Cyber situational awareness
- Machine learning and predictive analytics
- Cyber risk assessment and mitigation
- Cryptographic protocols
- Secure software design
- Blockchain technology
PROFESSIONAL MASTER'S PROGRAM CO-OP TEAM
Co-op Career Advisor (Mon - Fri)
Office: ASB 9701 (Burnaby)
Co-op Coordinator (Acting)
Office: ASB 9701 (SFU Burnaby)
Co-op Coordinator (On maternity leave until September 2020)
Office: ASB 9701 (Burnaby)