Lifelong Learning for a Brighter World

female data analyst sitting in front of her computer in a modern office charting data female data analyst sitting in front of her computer in a modern office charting data

Foundations of Analytics

Business Intelligence, Data Analysis and Data Science

Your Introduction to the World of Analytics

Course Descriptions

Foundations of Analytics: Business Intelligence

BDA 201 Statistics for Data Analytics (3 Units)

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 tools will be introduced and employed in order to demonstrate how significant and insightful information is collected, used and applied to problem-solving processes. This course is designed for individuals with no, or limited, study in Statistics.

Prerequisite: Grade 11/12 Mathematics (College/University prep)

BDA 202 Working with Databases (3 Units)

This course introduces students to database management concepts using a practical approach. The course will begin with an introduction to data modeling and how these models are implemented through the use of the Structure Query Language (SQL). The remainder of the course explores how SQL can be used to query and manipulate data. Proficiency with computer operating systems is required.

BDA 203 Business Intelligence & Data Analytics (3 Units)

Learn to apply data analytics skills to the area of business intelligence (BI). Focus is placed on the components of business intelligence project lifecycle such as project planning, BI tool selection, data modelling, ETL design, BI application design and deployment and reporting. This course is designed for individuals interested in BI practices and analysis without a detailed focus on statistical analysis and computer programming.

Prerequisite: Programming experience is not required; however, proficiency with computer operating systems is required.

BDA 204 Data Analysis and Visualization (3 Units)

This course will examine the exploration of data in order to discover meaningful information to solve problems. The course will present the analytics life cycle in context of planning to solve a business problem. Emphasis will be 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 will be explored and used as students learn best practices for the presentation and communication of analytical solutions and insights.

Prerequisite: University or college introductory course in Statistics; Statistics for Data Analytics

Foundations of Analytics: Data Analysis

BDA 101 Data Analytics & Modelling (3 Units)

This course offers an introduction to data science and machine learning paving the way for students to learn big data principles. In particular, this course begins with a brief history of 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 which students will put "theory into practice" offering students a hands-on approach to learning the material.

Prerequisite: This course is the first course in the Big Data Analytics program and is offered for students who have completed the Statistical Analysis for Data Analysis and/or Statistical Analysis for Data Science with a minimum grade of 65% (“C” letter grade). Knowledge and experience working with R and Python required.

BDA 200 Foundations of Computer Programming (3 Units)

This course introduces the students to the fundamentals of structured programming and problem-solving. A current programming language will be used to introduce problem analysis, algorithm design, object-oriented programming concepts and program implementation. Topics include variables, conditional processing, loops, functions, data structures, error handling and file input/output. Programming experience is not required; however, proficiency with computer operating systems is required.

BDA 201 Statistics for Data Analytics (3 Units)

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 tools will be introduced and employed in order to demonstrate how significant and insightful information is collected, used and applied to problem-solving processes. This course is designed for individuals with no, or limited, study in Statistics.

Prerequisite: Grade 11/12 Mathematics (College/University prep)

BDA 202 Working with Databases (3 Units)

This course introduces students to database management concepts using a practical approach. The course will begin with an introduction to data modeling and how these models are implemented through the use of the Structure Query Language (SQL). The remainder of the course explores how SQL can be used to query and manipulate data. Proficiency with computer operating systems is required.

BDA 204 Data Analysis and Visualization (3 Units)

This course will examine the exploration of data in order to discover meaningful information to solve problems. The course will present the analytics life cycle in context of planning to solve a business problem. Emphasis will be 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 will be explored and used as students learn best practices for the presentation and communication of analytical solutions and insights.

Prerequisite: University or college introductory course in Statistics; Statistics for Data Analytics

OSI 101 Tradecraft and Operations

Participants will learn the fundamentals of Open Source Intelligence (OSINT) tradecraft and operations for traditional and new media sources, including how to accurately assess and effectively monitor new sources, collect and process relevant information, and characterize and analyse information for intelligence insights. In addition, this course offers an overview of data literacy and data management, and highlights the increasing importance of large-scale data collection and analysis for effective OSINT work.

Instructors: Sami Khoury

OSI 102 Python for Basic Collection

Participants will learn how to treat the Internet as a data resource and how to create automated data collection mechanisms. The course starts by teaching trainees the essentials of Python programming, and then walks them through the practical steps of constructing their first web scraper.

Instructors: Sami Khoury

Foundations of Analytics: Data Science

BDA 205 Statistical Analysis for Data Science (3 Units)

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. Pre-requisite: Grade 12 U level Mathematics (Advanced Function, or Calculus and Vectors, or Mathematics for Data Management, or Mathematics for College Technology); University or college introductory course in Statistics; Statistics for Data Analytics.

BDA 206 Data & Web Technologies for Data Analysis (3 Units)

This course introduces students to the identification and use of, essential web technologies for data science. Students will discover how to access, collect, and analyze data from various sources with a focus on integrating robust technologies to a data project. This course is designed for individuals with previous study in statistics, information technology.

Prerequisite: University or college course in Statistics; or Statistical Analysis for Data Science, AND Foundations of Computer Programming, Working with Databases (SQL)

BDA 207 Introduction to Artificial Intelligence (3 Units)

This course presents the basics of artificial intelligence (AI) through an examination of its history and evolution. A review of the applications of AI in various industries will serve as the focus on inquiry for the course. Current uses and trends in AI will be discussed and students will be encouraged to consider the potential of AI to solve complex problems.

Prerequisite: Introductory level computer programming and statistics. This course is an inquiry/exploratory course into artificial intelligence; it is not a programming or technical course in AI.