Information Technology: Business and Advanced Analytics

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:

• DATA1046A

This course is designed to provide learners with knowledge of the business analysis lifecycle as it relates to data analytics projects. It prepares the learners for application of these principles and practices further along in their program.

This course builds upon the knowledge acquired in the Business Analysis for Data Projects course. Here, students delve more deeply into specifics of the business data analytics lifecycle. They apply this knowledge to identify a specific business problem, the appropriate research questions to address the business problem, how to source the right data and determine if the analysis findings solve the business problem.  

Prerequisites:

• DECI1008A
• DATA1045A

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 is designed to provide learners with the knowledge to help facilitate and support data-driven decision making. Here, students learn the impact of data analytics integration into key business processes and how the data findings can, and should, influence business decisions. They focus on the soft-skill techniques used to build consensus, negotiate, and influence a proposal or solution.

This course builds upon the knowledge acquired in the Business Analysis for Data Projects II course. Here, learners delve into deploy and maintain phases of the lifecycle. They learn the components of both implementation and transition plans to support the implementation of the data-driven solution as well as the techniques used to validate the solution.

Prerequisites:

• DECI1009A

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 introductory course is designed to provide learners with insight into the ethical, moral, and social implications of the data age. Through real-world examples, students critically examine the decisions and actions of individuals, corporations, and governments related to the collection, protection, and use of consumer information and big data as well as the algorithms used to process data and automate reasoning. Learners can then use this insight to later plan, implement, and evaluate their own projects with ethical responsibility. In Data Ethics II, students apply the tools and methods used for ethical data management.  

This course builds upon the knowledge acquired in Data Ethics I and focuses on the tools and methods used for ethical data management.

Prerequisites:

• ETHI1062A

This course provides students with foundational knowledge and skills of programming and the Python language. Here, students learn key programming concepts like data structures and their application in the Python language. Learners apply this knowledge to build a basic python application using built-in data structures and custom functions.
 

This course builds upon the knowledge and skills acquired in Python I. Here, students learn to build Python programs using advanced data structures for data operations. They also learn to extract the data from various sources for data analysis in future courses.

Prerequisites:

• PROG1322A
• 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:

• PROG1323A
• STAT1037A

This course builds upon the knowledge and skills acquired in Python III. Here, learners focus on data analysis using machine learning models and the packages used to apply these models. The machine learning packages may include those such as Scikit Learn. More in-depth knowledge and skills of machine learning concepts and models are taught and applied in the Machine Learning courses.
 
Students also learn the packages to visualize preprocessed data. The visualization packages may include those such as Seaborn, Matplotlib, and/or Plotly.

Prerequisites:

• PROG1324A
• STAT1037A

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
• STAT1037A

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:

• STAT1037A

This course builds upon the knowledge acquired in Machine Learning I. Here, learners build and implement supervised machine learning models using regression models to classify data or predict outcomes. They utilize Python, R, and/or cloud-based tools to build and run the models.  

Prerequisites:

• PROG1325A
• PROG1327A
• SYST1058B
• PROG1328A

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

Prerequisites:

• PROG1325A
• PROG1327A
• SYST1058B
• PROG1328A

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:

• STAT1036A

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:

• SYST1057A
• PROG1328A
• PROG1329A

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.