Delivering Excellence in Lifelong Learning
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Foundations of Analytics

Business Intelligence, Data Analysis and Data Science

Your Introduction to the World of Analytics

Course Descriptions

Foundations of Analytics: Business Intelligence

DAT 101 Statistics for Data Analytics - Formerly BDA 201 (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 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. This course is designed for individuals with no, or limited, study in Statistics.

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

DAT 102 Working with Databases - Formerly BDA 202 (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 Structured Query Language (SQL). The remainder of the course explores how SQL can be used to query and manipulate data.

Prerequisite: Proficiency with computer operating systems is required.

DAT 103 Business Intelligence & Data Analytics - Formerly BDA 203 (3 Units)

Learn 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. This course is designed for individuals interested in BI practices and analysis without a detailed focus on statistical analysis and computer programming.

DAT 104 Data Analysis and Visualization - Formerly BDA 204 (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 the 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.

Foundations of Analytics: Data Analysis

DAT 100 Foundations of Computer Programming - Formerly BDA 200 (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.

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

DAT 101 Statistics for Data Analytics - Formerly BDA 201 (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 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. This course is designed for individuals with no, or limited, study in Statistics.

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

DAT 102 Working with Databases - Formerly BDA 202 (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 Structured Query Language (SQL). The remainder of the course explores how SQL can be used to query and manipulate data.

Prerequisite: Proficiency with computer operating systems is required.

DAT 104 Data Analysis and Visualization - Formerly BDA 204 (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 the 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.

DAT 201 Data Analytics & Modelling - Formerly BDA 101 (3 Units)

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.

Prerequisite: Introductory statistics course, or DAT 101 Statistics for Data Analysis

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

DAT 105 Artificial Intelligence (AI) for Business: An Introduction - Formerly BDA 207 (3 Units)

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.

DAT 200 Statistical Analysis for Data Science - Formerly BDA 205 (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.

Prerequisite: 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.

DAT 204 Data Analytics Tools - Formerly BDA 206 (3 Units)

Students will 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.

Prerequisite: Introductory statistics course, or DAT 101 Statistics for Data Analysis, or DAT 200 Statistical Analysis for Data Science