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Courses
Data Analytics-2025/2026
| Level 1 | ||||
| Take all of the following Mandatory Courses: | ||||
| COMM-6019 | Advanced Professional Communication | 3 | ||
This course focuses on refining and advancing students workplace communication abilities. The advanced communication documents and strategies covered include presentation skills, research skills, business document writing, meeting and management team strategies, business etiquette, and advanced employment communications. Additionally, students learn about interpersonal and intercultural communication (high/low and monochromic/polychromic context) concepts and strategies. | ||||
| MGMT-6211 | Foundations of Data Analytics | 4 | ||
This course is an introduction of data analysis principles, practices, and approaches used in research, big data, data science, and AI. Students will be provided with an overview of models supporting analytics-based decision-making. | ||||
| MGMT-6212 | Dashboarding & Data Visualization | 3 | ||
This course will focus on software (PowerBI, Tableau) that supports analytics to express meaningful data to position analytics for effective decision-making. | ||||
| MGMT-6213 | Analytics for Project Management | 3 | ||
This course will cover the general principles of project management, including agile project management. Topics included will be estimating, scoping, planning, and risk mitigation techniques that draw upon data analytics for informed communication with key management and stakeholders. | ||||
| MGMT-6214 | Web Analytics | 3 | ||
This course will cover data collection from websites and social media to support critical business decisions (e.g., Google Analytics). Topics will include data mining using pattern recognition and knowledge extraction. | ||||
| METH-6014 | Introduction to Statistical Analytics | 3 | ||
This course provides an overview of application of statistical methods at different phases of data analytics. Topics include statical methodologies for creation, collection, analysis, validation and visualization of quantitative data, and forecast modelling. | ||||
| INFO-6196 | Programming Fundamentals for Analytics | 3 | ||
This course provides an Introduction of programming languages (R, SQL) for effective data analysis, use for extraction or various databases. | ||||
| Level 2 | ||||
| Take all of the following Mandatory Courses: | ||||
| MGMT-6215 | Data Privacy, Ethics & Governance | 3 | ||
This course will review ethical considerations for data privacy initiatives, security at both the personal and corporate levels, compliance, and legislation. | ||||
| MGMT-6216 | Predictive Analytics | 4 | ||
This course will incorporate the use of various analytics techniques, including machine learning, to analyse current and historical data to predict future behaviours and events. Predictive modelling and regression analysis employed to reach data-informed business decisions, forecasting & optimization, simulations. | ||||
| INFO-6197 | Introduction to AI & Machine Learning | 3 | ||
This course will focus on fundamental principles and history of data science and machine learning. Topics covered will include sentiment analysis, application of modern tools for reorganization of large quantities (and sources) of unstructured data, unsupervised & supervised modeling, plus reinforcement modeling. | ||||
| MGMT-6217 | Data Analytics for CRM | 3 | ||
This course will explore programming that will analyse, aggregate, and visualize data effectively to facilitate efficient decision-making. Form recommendations regarding segmentation and targeting based on data analytics. Students will review different customer relationship management (CRM) tools. | ||||
| MGMT-6218 | Data Strategy & Decision Making | 3 | ||
This course uses big data and business intelligence to evaluate business needs, solve problems, manage risks, and implement strategies. Additional topics include data accuracy/integrity, data maintenance and data clean up. | ||||
| MGMT-6219 | Data Analytics Capstone | 4 | ||
This course will employ business data analytics methodologies throughout the identification of business needs, data exploration and preparation, data analysis and predictive modeling, recommendations, and subsequent results analysis following decisions. | ||||
| Program Residency | ||||
| Students Must Complete a Minimum of 11 credits in this program at Fanshawe College to meet the Program Residency requirement and graduate from this program | ||||