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This course is designed to provide learners with foundational knowledge of data analysis and the variables involved in its collection, storage, organization, maintenance, use, and distribution. Students learn data types and applications, the data pipeline, data infrastructure, and the tools commonly used in the data analysis process. Students then apply basic data analysis principles and practices using spreadsheet software.

This course is designed to provide learners with knowledge of fundamental data management principles and practices as it relates to structured data and to apply these principles using Structured Query Language (SQL).

Prerequisites:

• DATA1045A

This course is designed to build upon the knowledge and skill acquired in Data Management I. Here, learners apply data management principles and practices as it relates to unstructured data using a NoSQL database management (DB) program such as MongoDB.

Prerequisites:

• DATA1046B

A Business Case captures in detail the business drivers, costs, benefits, and economic justification for the investment and resources needed to implement a proposed solution. The purpose of this course is to provide learners with the skills and tools to analyze, build, and present a business case to justify the building and deployment of a solution. Course concepts and content are applied through the use of real-world case studies.

This course provides learners with the knowledge to help facilitate and support data-driven decision making. They learn the impact of data analytics integration into key business processes and how the data findings influence business decisions. They focus on commonly used analytical techniques for application in industry environments.

This course provides students with knowledge of data visualization and storytelling principles and practices. Students learn the techniques used to create a powerful data narrative as well as those to create clear and impactful visualizations to support their data story.   
 

Prerequisites:

• DATA1045A

This course provides students with the foundational knowledge and skills using Power BI to perform data modelling and create interactive insights.

Prerequisites:

• DECI1013A
• DATA1045A

This course builds upon the knowledge and skill acquired in Power BI I: Data Modelling and Visualization. Here, students learn to setup data models using Data Analysis eXpressions (DAX) and perform advanced analytics using Power BI.

Prerequisites:

• DECI1015A

This course provides learners with the knowledge and skills to determine the business and data objectives for a given data project. They acquire knowledge of general business data analytics concepts, business and data understanding, as well as the Software Development Life Cycle (SDLC). They use this knowledge to formulate the analytics problem, produce a project proposal, and apply elements of the SDLC.

Prerequisites:

• DECI1010B

This course provides learners with the knowledge and skills to analyze data to meet business objectives. Through theoretical knowledge and practical application, learners navigate the analytical journey, from generating the data description report through to communicating the analysis findings and recommendations.

Prerequisites:

• DECI1021A
• DATA1045A

This course builds upon the knowledge and skills acquired in Business Analysis for Data Projects I and II, providing learners with knowledge of the operational components of machine learning models. It emphasizes the integration of data solutions and the software life cycle, highlighting the essential processes and considerations necessary to transition machine learning models from conceptual frameworks to operational software.

Prerequisites:

• DECI1022A

This course provides learners with the knowledge and skills to assess and apply ethical and privacy standards throughout the data analytics life cycle. Learners gain insight into the ethical and social implications of the data age. Through real-world examples, they critically examine the decisions and actions of individuals, corporations, and governments related to the collection, protection, and use of consumer information and big data. They also critically examine the algorithms used to process data and automate reasoning. Learners use this insight to later plan, implement, and evaluate their own projects with ethical responsibility.  

This course provides learners with foundational knowledge and skills in the programming and analytical tool “R”. It provides another method of data manipulation, calculation, analysis, and graphical display. By the end of this course, learners can build a basic R program using built-in data structures and custom functions.

This course builds upon the knowledge and skills acquired in RI: Foundations and Data Operations. Here, learners build R programs to explore and visualized data as well as apply statistical analyses.   

Prerequisites:

• PROG1326A
• STAT1037B

This course is designed to provide learners with knowledge of key machine learning concepts, principles, and practices. This knowledge is then applied in other Machine Learning courses to develop applications that learn and adapt without following specifics instructions and to analyze the patterns of data drawn from the machine learning models.

Prerequisites:

• STAT1037B

This course builds upon the knowledge acquired in Machine Learning I. Learners build and implement supervised machine learning algorithms using regression models to predict outcomes. They utilize Python to build and run the models.

Prerequisites:

• PROG1328B
• DECI1013A
• PROG1350A

In this course, learners continue to build their machine learning skills. They build and implement supervised and unsupervised machine learning algorithms using classification and clustering models to classify data or predict outcomes. They utilize Python to build and run the models.

Prerequisites:

• PROG1328B
• DECI1013A
• PROG1350A

This course provides learners with the foundational knowledge and skills to build a basic python application using built-in data structures and user-defined functions. They learn key programming concepts as well as problem-solving, code debugging, and basic software development practices.

This course builds upon the knowledge and skills acquired in Python I. Learners acquire knowledge of key object-oriented programming concepts and apply these concepts to manage data. They also learn to install and manage third-party packages and implement file input/output (I/O) operations with the intention of applying data acquisition techniques.

Prerequisites:

• PROG1348A
• DATA1045A

This course builds upon the knowledge and skills acquired in Python II and focuses on using Python packages for data preprocessing and statistical analysis. The preprocessing packages may include those such as Pandas and NumPy. The analysis packages may include those such as Scikit Learn and Statsmodel. Learners also continue to reinforce their statistical skills by applying statistical techniques to analyze data.

Prerequisites:

• PROG1349A
• STAT1037B

This course builds upon the knowledge and skills acquired in Python III and focuses on using Python packages for data analysis and visualization. Here, learners combine analytical results with visualizations to present a clear and comprehensive view of the data. This includes visualizing the outputs of statistical tests, regression models, and correlation analysis to aid in the interpretation of the analysis. Learners also become adept at using popular Python visualization libraries such as Matplotlib, Seaborn, and Plotly.

Prerequisites:

• PROG1350A
• STAT1037B

A safe and healthy workplace is the responsibility of the employer and the employee. This course introduces students to the importance of working safely and addresses how employers and employees can control the hazards and risks associated with the workplace. Students will also learn about the roles and responsibilities of key stakeholders including WorkSafeNB, the employer and the employee in ensuring workplaces are safe.

This course introduces students to key statistical concepts as it relates to data science. It focuses on areas such as descriptive statistics, sampling distribution, and plotting techniques for data analysis.
 

This course builds upon the knowledge and skills acquired in Statistics I to focus on areas such as hypothesis testing, regression analysis, linear regression, and nonparametric testing. Learners apply standard statistical techniques for the purpose of analysis, visualization, and interpretation of data.

Prerequisites:

• STAT1036B

This course provides learners with the foundational knowledge of cloud concepts, security, and architecture. This knowledge is applied in future program courses for analytics purposes.

This course is designed to provide students with the skills to implement a machine learning (ML) model in a cloud-based environment and using cloud-based tools. More in-depth knowledge and skills of machine learning concepts and models are taught and applied in the Machine Learning courses.

Prerequisites:

• SYST1057B
• PROG1329C

This Capstone course represents the culmination and integration of students’ learning across the Business and Advanced Analytics program. Here, learners apply the full spectrum of their learning to a new, real-world situation. They solve a business problem/need using supervised or unsupervised machine learning models and data analytics tools. As part of this self-directed application, they submit a project proposal to their instructor for review and approval. If/as approved, they carry out the complete analytics workflow/cycle and the business analytics components related to it. They analyze, visualize, and present their business solutions to their instructor, peers, and/or key stakeholders.