Education

Design and analysis of basic algorithms and data structures; algorithm analysis methods, searching and sorting algorithms, basic data structures, graphs and concurrency.

Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence.

Marketing intelligence (MI) combines marketing and customer analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make better data-driven decisions. Using MI, powered by the modern use of AI and ML, organizations can optimize their marketing and customer performance by maintaining a competitive advantage and achieving organizational goals and objectives. Building on the data and marketing analytics concepts and frameworks from the first three courses, participants in this course will focus on continuous improvement opportunities to optimize marketing and customer initiatives. This will require looking at an organization’s entire data ecosystem – internally and externally.

This is the final course and project for the Applied Data Analytics Certificate (ADAC) from BCIT Computing. Students must have completed all specific prerequisites in order to register. COMP 4254 is built on top of: Python Fundamentals for Data Analysis, Data Analytics Fundamentals, and Advanced Statistical Techniques for Data Analytics. Students must have experience using data visualization tools, be able to use R and apply statistical methods with algorithms in order to develop their final project. COMP 4254 is a BYOD (bring your own device) course, participants must provide their own current model PC with an i5 or higher equivalent processor, 8 GB of RAM minimum, 256 GB minimum storage and high-speed internet access. Participation is mandatory and students can expect a minimum of 3 hours per week online in real time via the BCIT Learning Hub plus using two-way audio and video. Attendance is required during scheduled class hours and students should expect an additional 10+ hours for reading and homework each week. Building on statistics for data mining, students will make use of previous tools and technologies used through out the program. A combination of R, Python, Data Analysis tools and visualization techniques will be integrated into an advanced data analytics project. There is an introduction to data analytics techniques with Big Data. Labs and exercises use Python and Apache Spark for data mining with a focus on Business Intelligence and Big Data. Participants will work with a variety of structured and unstructured data sources to model, analyze and visualize data. Upon successful completion students will be able to develop an advanced project from start-to-finish which includes data preparation, Business Intelligence and an in-depth analysis on a broad range of data sources.

This course provides an introduction to the various topics in Artificial Intelligence (AI). Topics to be covered include search, games, regression, classification and neural networks. Students will develop an understanding of the fundamentals underpinning AI applications and gain hands-on experience through the development of AI systems. Practical work in course projects focuses on developing components of AI systems as well as analyzing real-world datasets from different domains. The societal implications and ethical considerations in the design of AI systems will also be discussed.

This course examines the tools, techniques, and strategies required to collect, manage, and analyze customer data. Specifically, participants will look at the role of data in understanding, driving, and improving customer acquisition, customer support, and customer retention. Leveraging a variety of customer touch-points, the course will look at customer segmentation based on customer needs, customer life cycle, and customer experience. Once completed, participants will be able to use customer analytics to better understand their customers’ behaviors and to make effective and profitable customer decisions.
This course is recommended as a precursor to the Data Visualization and Storytelling course in the Associate Certificate in Data and Marketing Analytics program.

The Align program enables a direct path to a Master’s in Computer Science for non-computer science majors and people without programming experience.

This part-time, four-day course offers foundational data science and AI concepts that can be directly applied to your work and organization. Designed and taught by leading data science and AI experts at Simon Fraser University, this online interactive course offers a hands-on learning environment, enabling you to immediately apply concepts and techniques you learn with live feedback and guidance from instructors. You will come away with the kind of data science experience and knowledge that organizations and recruiters value.

This part-time five-day course offers foundational Artificial Intelligence, Machine Learning (ML) and Data science concepts that are applicable in the humanities and social sciences. Together with SFU's Digital Democracies Institute, this course will have you examine best practices to critically evaluate and mitigate unwanted bias from sources such as data, algorithms or users. Through collaborative hands-on labs guided by SFU experts, you will come away with the kind of data science experience and knowledge that organizations and recruiters value.

New – This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.

Build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.

This one-day course will provide foundational level knowledge on cloud concepts; core Azure services; security, privacy, compliance, and trust; and Azure pricing and support.
This course is a high-level overview of Azure. It will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure.

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