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.).
Machine Learning for Big Data Analytics - F24
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.
Data Science Capstone Project - F24
DAT 205
The capstone course is part of the Big Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. Students apply analytical models, methodologies, and tools learned in the program to create an analytics solution for your organization. Faculty mentors will work with students to ensure the capstone project reflects, and encompasses, best practices for big data analytics and project management.
Data Management - Fall 2024
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.
Artificial Intelligence for Business
DAT 105
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 presents the principles of artificial intelligence (AI) through an exploration of its history, capabilities, technologies, framework, and its future. AI applications in various industries will be reviewed through some case examples. Current trends in AI will be discussed and students will be encouraged to consider the potentials of AI to solve complex problems. This course will help students to understand the implications of AI for business strategy, as well as the economic and societal issues it raises
Data Analytics and Modelling - F24
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.
Statistical Analysis for Data Science - F24
DAT 200
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 provides a foundation of exploring data through computing and statistical analysis. Focus is placed on the structure and applications of probability, statistics, computer simulation, and data analysis for students exploring the field of data science. This course builds upon introductory statistics courses and is designed for students with experience/study in programming, calculus, and algebra. Programming in R will be used throughout the course.
Data Analysis and Visualization - F24
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.
Business Intelligence & Data Analytics - F24
DAT 103
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 course explains how to apply data analytics skills to the area of business intelligence (BI). Focus is placed on the components of the business intelligence project lifecycle such as project planning, BI tool selection, data modeling, ETL design, BI application design and deployment and reporting. The project(s) will allow the students to apply BI practices and analysis.
Statistics for Data Analysis Project
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 Programming
DAT 303
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 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.).
Predictive Modelling and Data Mining - Spring 2024
DAT 203
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 course will introduce predictive modeling techniques as well as related statistical and visualization tools for data mining. The course will cover common machine learning techniques that are focused on predictive outcomes. Students will learn how to evaluate the performance of the prediction models and how to improve them through time.
Data Management - Spring 2024
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.
Data Analytics and Modelling - Spring 2024
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.
Statistical Analysis for Data Science - Spring 2024
DAT 200
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 provides a foundation of exploring data through computing and statistical analysis. Focus is placed on the structure and applications of probability, statistics, computer simulation, and data analysis for students exploring the field of data science. This course builds upon introductory statistics courses and is designed for students with experience/study in programming, calculus, and algebra. Programming in R will be used throughout the course.
Data Analysis and Visualization - Spring 2024
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.
Statistics for Data Analysis Project - Spring 2024
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 Management - Winter 2024
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 - Winter 2024
DAT 301
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 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 2024
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 2024
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.
Predictive Modelling and Data Mining - Winter 2024
DAT 203
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 course will introduce predictive modeling techniques as well as related statistical and visualization tools for data mining. The course will cover common machine learning techniques that are focused on predictive outcomes. Students will learn how to evaluate the performance of the prediction models and how to improve them through time.