Artificial Intelligence and Data Science

Courses

• First Year - Semester One
• AIGS1005 AI Principles and Best Practices in Canada
• In this course students will learn about the principles of artificial intelligence and data science and how to apply best practices to their
  work within a Canadian industry context.
• AIGS1006 Deep Learning
• This course represents concepts of deep learning such as basics of neural networks, deep neural networks and recurrent neural networks (RNN). Topics covered include the fundamentals of artificial neural networks and how to implement them in Python programming language.
• AIGS1004 Deterministic Models and Optimization
• The main objective in this course is to provide students with a thorough grounding in optimization models, theory and algorithms. The course scope is broad, covering the most important representative models and algorithms. Material will be closely linked to modern statistical
  methods such as network analysis, Quintilian regression and high dimensional statistics.
• AIGS1003 Machine Learning
• This course introduces students to some of the basic techniques of machine learning required for data science. It provides a solid training in computational algorithms for supervised problems (classification and regression), such as decision trees and forests, support vector machines or nearest neighbours. There is a hands-on, lab section that focuses on the use of scientific scripting languages and special attention is devoted to Python language.
• AIGS1002 Mathematics for Data Science
• This course covers fundamental mathematical concepts relevant to data and computer science and provides a basis for further study in data science, statistics and cyber security. Topics covered are probability: sets, counting, probability axioms, Bayes theorem; optimization and calculus: differentiation, integration, functions of several variables, series approximations, gradient descent;
  linear algebra: vectors and matrices, matrix algebra, vector spaces; and, discrete mathematics: induction, difference equations. This course helps make connections between each of these fundamental mathematical concepts and modern data science applications. It also leverages Python programming for data wrangling, algorithms and visualization.
• AIGS1000 Programming Languages for AI- Python R
• In this course, you’ll learn the fundamentals of Python and R programming languages and explain why they’re the best options for data science projects. Data structures, data manipulation, data
  exploration, loops and conditions, and data visualizations will be explored.
• AIGS1001 Statistical Modelling and Inference
• In this course students will be exposed to the concepts of probability and statistics. Probability and statistics form the basis of data science. The probability theory is important for predicting. Estimates and predications form an important part of data science. This course explains how we can make estimates for further analysis using statistics and includes the basis of statistics such as central tendency theorem and types of distribution.

• First Year - Semester Two
• AIGS1014 Capstone Project
• This capstone project will enable you to work on real-world problems using public data. You will apply exploratory data analysis skills to build an accurate prediction model.
• AIGS1010 Computer Vision
• This course presents concepts and fundamentals of computer vision. Image processing and computer vision techniques such as objective detection, object tracking and action recognition will be covered, as well as image segmentation and synthesis.
• AIGS1009 Data Storytelling Techniques
• This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to detect and articulate the stories behind data sets and communicate data findings in visual, oral and written contexts for various audiences and stakeholders. During this course, students will become familiar with associated tools.
• AIGS1007 Data Visualization
• This course focuses on data visualization and explains visualization tools. Topics covered include various data sources, metadata, data preparation, joints and data blending. Basic functions such as sorting and filtering will be included in this course and techniques such as drill down and hierarchies, reference lines and trend lines will be explored.
• AIGS1008 Data Warehousing and Business Intelligence
• In this course, you’ll get an overview of modern solutions to storing and analyzing big data, as well as hands on practice working with databases, building systems to collect data from the internet
  and crating live web dashboards. You’ll be encouraged to think creatively and use knowledge from other courses from the first semester to come up with an informative display of data that you can
  create with the dashboard tools taught in class.
• AIGS1011 Natural Language Processing
• This course presents the concept of natural language processing (NLP), the concepts of text mining and its applications, the basics of NLP such as POS, entity recognition, regular expression and how
  to implement them. Sentiment analysis, topical modelling and clustering in text courses will be also be covered.
• AIGS1012 Professional Portfolio
• A carefully prepared and professionally presented portfolio provides you with a valuable advantage in the highly competitive process of securing employment. In a broader sense, a portfolio gives you a measure of control over your future, which is particularly important in times of rapid change and economic upheaval. Practical tips, tools, practices, and approaches are addressed to create your unique portfolio of talents, professional skills, experiences and background.

*Courses subject to change.