Data Analytics Tools
DAT 204
This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. Students learn how to collect, manage, analyze, and visualize data to deliver clear business insights from raw data sources. This course will cover the Hadoop ecosystem as it is a primary platform for any other tools like Spark or Kafka. This course also covers an example of NoSQL, such as Cassandra which is suited for distributed computing. Emerging tools and technologies may be presented as applicable to course content.
Statistics for Data Analysis Project - W25
DAT 101
The course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course introduces descriptive statistics, basic inferential statistics, linear regression, and probability concepts and calculations. Practical application activities in the course focus on how statistical methods are used in the analysis of data. Common statistical and programming tools will be introduced and employed in order to demonstrate how significant and insightful information is collected, used, and applied to problem-solving processes.
Data Analysis and Visualization - W25
DAT 104
This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. The students learn how to perform exploration of data in order to discover meaningful information to solve problems, and will allow for the application of analytics life cycle in the context of planning to solve a business problem. Emphasis is placed on framing the problem, proposing an analytics solution, communicating with stakeholders, and establishing an analytics-focused project plan. Common data visualization tools and techniques are explored and used as students learn best practices for the presentation and communication of analytical solutions and insights.
Data Analytics and Modelling - W25
DAT 201
This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course offers an introduction to data science and machine learning paving the way for students to learn data analytics principles. In particular, this course begins with a brief history of data analytics and data science, followed by regression analysis, regression and classification trees, and ends with introductions to K-means clustering, principal component analysis (PCA). Each lecture has associated with it a practical lab session in which students will put "theory into practice" offering students a hands-on approach to learning the material.
Data Management - Winter 2025
DAT 202
This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course explores the importance of managing data as an enterprise asset and the processes and components required in terms of the acquisition, storage, sharing, validation and accessibility of data for addressing business problems. An examination of Database Management Systems, database architectures (structured and non-structured) the differences between OLTP (Online transaction processing) OLAP (online analytical processing) as well as the administrative processes (Data Governance) that guide the data lifecycle will be a focus.
Machine Learning for Big Data Analytics - W25
DAT 301
This course is part of the Big Data Programming and Analytics certificate programs. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course builds on the fundamental principles of data analytics, this course advances to modern machine learning techniques such as neural network, deep learning, and reinforcement learning as well as NLP and text analysis. Application activities are structured to provide an introductory level of how machine learning techniques are applied to big data analytics.
Essentials of Cloud Computing - Winter 2025
DAT 304
This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. Students will explore the principles and practices of cloud computing with this introductory course, and discover the importance of cloud computing for today’s business and IT sectors through an examination of the development of cloud technologies over time. Common practices for delivery, deployment, architecture and security will be presented. Students will explore various cloud computing platforms to understand and assess current service options and to discuss future developments for cloud computing -- The project provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into an analytics problem. The projects, which can be short, will allow the student to apply the skills acquired on to address the business problem. Some examples are: Determine the characteristics of the collection system and select a collection system that handles the large data set Identify the right storage solution for analytics Design and implement a solution for transforming and preparing data for analysis Select the right data analysis and data visualization solution for a given scenario Apply the right authentication and authorization mechanisms Apply data protection and encryption techniques Manage and monitor data solutions You should submit a high-level proposal/business problem statement including relevant data sets and definitions, a list of acceptable tools (if applicable), and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and business problem. The course instructors will review the documents to confirm the scope and timing of the proposed problem and its alignment with the course requirements.
Big Data Programming and Architecture Capstone - Winter 2025
DAT 305
This course covers advanced-level topics in the areas of data science, machine learning, and technical/software applications. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. They will practice their knowledge of using various technologies as part of the course, such as real-time analytics tools (e.g., Kafka and HBase), NoSQL databases, and cloud technologies. Students will apply analytical models, methodologies, and tools learned in the program to create an analytics solution for your organization, with support from faculty mentors, who will work with students.
Data Programming II
DAT 303
This course is part of the Big Data Programming and Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course is designed to present the fundamental concepts and theories in Data Analytics and promote the application to the workplace and professional practice. Students begin with an exploration of MongoDB which is a document database with scalability and flexibility for queries and indexing, and progress to the ELK stack – a technology stack used for logging with different components, such as Elasticsearch, Logstash, and Kibana. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, group discussions, projects, etc.).
Data Programming I
DAT 302
This course is part of the Big Data Programming and Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course examines developing solutions for extracting and analyzing big data sets using various technologies. Students will learn Scala and Java, which are the fundamental part of Spark, Kafka, and HBase. The focus will be on Apache Spark and its different aspects. Students will explore real-time analytics tools such as Kafka and HBase. NoSQL will be covered in this course. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, group discussions, projects, etc.).