Courses
Technical Data Analytics-2025/2026
| Level 1 | ||||
| Take all of the following Mandatory Courses: | ||||
| COOP-1020 | Co-operative Education Employment Prep | 1 | ||
| This workshop will provide an overview of the Co-operative Education consultants and students' roles and responsibilities as well as the Co-operative Education Policy. It will provide students with employment preparatory skills specifically related to co-operative education work assignments and will prepare students for their work term. | ||||
| INFO-6174 | Intro to Data Analytics | 3 | ||
| This course is designed to acquaint students with the fundamental principles and techniques of data analytics. The course explores the processes involved in collecting, processing, and interpreting data to extract meaningful insights and support decision-making. Students will learn the basics of data manipulation, visualization, and statistical analysis using popular tools and programming languages such as Python or R. | ||||
| INFO-6185 | Data Modeling and Schema Design | 4 | ||
| This course delves into the principles and practices of structuring data for efficient storage, retrieval, and analysis. The course focuses on teaching students how to design effective database schemas, emphasizing the importance of logical and physical data modeling. | ||||
| INFO-6175 | Statistical Analysis 1 | 3 | ||
| This course introduces students to the principles of statistical reasoning and analysis. The course covers essential topics in descriptive and inferential statistics, providing students with the tools to make sense of data and draw meaningful conclusions. Students will learn how to summarize and visualize data, calculate measures of central tendency and dispersion, and understand probability distributions. | ||||
| INFO-6176 | ETL Processes and Data Integration | 4 | ||
| This course focuses on Extract, Transform, Load (ETL) processes-a crucial component of data warehousing and business intelligence. This course is designed to familiarize students with the principles, techniques, and tools involved in collecting, transforming, and integrating data from various sources into a unified and accessible format. The course includes hands-on exercises using popular ETL tools and languages | ||||
| INFO-6177 | Introduction to Data Warehousing | 3 | ||
| This course is designed to provide students with a comprehensive understanding of the fundamental concepts and principles of data warehousing. The course focuses on the architecture, design, and implementation of data warehouses and Data Lakes. Practical aspects of the course may involve hands-on exercises in designing and building a simple data warehouse, understanding the role of star and snowflake schemas, and using tools commonly employed in the field. | ||||
| INFO-6184 | Data Technology and the Business | 4 | ||
| This course explores the intersection of data and business strategies, focusing on how technology is leveraged to drive decision-making and innovation within organizations. Students will learn about the role of data in business decision-making, the technologies that enable efficient data management, and the integration of data-driven insights into business strategies. Key topics will include data governance ethical considerations, privacy concerns, and regulatory aspects related to the use of data in business contexts. | ||||
| Level 2 | ||||
| Take all of the following Mandatory Courses: | ||||
| INFO-6179 | Data Mining and Predictive Modeling | 4 | ||
| This course explores the methods and techniques for uncovering patterns, trends, and insights from large datasets to inform predictive modeling. The course is designed to equip students with the skills necessary to analyze data, discover hidden patterns, and build models that can make informed predictions about future trends or outcomes. | ||||
| INFO-6180 | Statistical Analysis 2 | 3 | ||
| This course builds upon the foundational concepts introduced in "Statistical Analysis I". This course focuses on a deeper exploration of statistical methods, hypothesis testing, and advanced analytical techniques. Students further develop their statistical reasoning skills and gain a more nuanced understanding of complex data sets. | ||||
| INFO-6181 | Big Data & Advanced Analytics | 4 | ||
| This course explores the principles, technologies, and applications of handling vast and complex datasets for in-depth analysis and decision-making. The course is designed to equip students with advanced skills and knowledge required to harness the potential of big data for actionable insights. Students will look into the architecture and components of big data ecosystems, exploring distributed storage and processing frameworks | ||||
| INFO-6182 | Text & Social Media Analytics | 3 | ||
| This course looks into the methods and techniques for extracting valuable insights from unstructured text data, particularly originating from social media platforms. Students in this course will explore natural language processing (NLP) techniques, machine learning algorithms, and sentiment analysis methods tailored to text data. | ||||
| INFO-6183 | Machine Learning Fundamentals | 4 | ||
| This course will introduce students to the core principles and techniques of machine learning. The course is designed to provide a solid understanding of the fundamental concepts and algorithms that form the basis of machine learning, a subfield of artificial intelligence. | ||||
| INFO-6178 | Big Data Systems | 4 | ||
| This course explores the principles, technologies, and challenges associated with handling and processing massive datasets, commonly referred to as "big data." The course is designed to equip students with the knowledge and skills required to manage, analyze, and derive insights from large and complex datasets that surpass the capabilities of traditional data processing systems. The curriculum may cover topics such as data parallelism, fault tolerance, and scalability in the context of big data processing. | ||||
| 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 | ||||