Learners will demonstrate proficiency in business communication by effectively applying strategies and techniques to convey information in professional settings. They will exhibit competency in utilizing digital tools for communication, develop strong interpersonal skills through practical, real-world exercises, and show collaborative competence by actively contributing to group projects and teamwork.

Understanding the data-driven programming methodology and having a sound programming background are foundational skills for anyone interested in working with data. This course introduces students to the principles of programming and application design. In addition, students begin to apply the concepts of data structures and algorithms to develop data-driven software applications.

Learners will demonstrate proficiency in foundational data tools (such as Microsoft Excel) by effectively navigating, consolidating, and analyzing data across multiple worksheets. They will exhibit competency in creating macros for efficient data analysis, managing complex nested formulas for scenario planning, and designing professional corporate dashboards tailored to business needs.

This course enables learners to develop competencies in analyzing the relationship between thinking, human behaviour, and organizational effectiveness. Learners will demonstrate the ability to critically evaluate their own behaviour and its impact on their professional and personal environments. Through applied learning, learners will acquire the skills to transform self-awareness, interpersonal relationships, and workplace dynamics using principles of human behaviour.

This course is specifically focused towards supporting the mathematical principles required to apply the concepts of data analysis and big data analytics. Learners apply concepts such as probability, distributions, regression, topological analysis, and descriptive and inferential statistics to data-related contexts.

Ethical data management involves ensuring that data is collected, stored, processed, and analyzed responsibly and ethically (including privacy regulations, data protection laws, ethical guidelines, and individuals' privacy and rights).

Learners locate and select relevant, high-quality data that meets the requirements and constraints of a project. Learners then extract data from different sources (a database, a website) using the appropriate techniques(SQL, web-scraping, and Application Programming Interface (API)). Once data has been collected, it must be prepared - reviewed, cleaned, structured, and enriched - prior to analysis. The result of data preparation is a finalized dataset that is well documented and ready for analysis.

Effective data analysis requires the integration of business understanding and data understanding. In this course, learners synthesize information about common business processes, workflows, and management strategies across a range of sectors (such as sales, marketing, accounting, quality improvement, product/service delivery, product development, and human resources), and their associated data needs. Learners then apply this knowledge to real-world business contexts to identify and define business goals and design appropriate data projects.

Learners will develop and demonstrate key management competencies essential for navigating today's dynamic business environment. These include strategic planning, effective decision-making, critical thinking, and the organization of human capital to optimize work practices. Learners will also show proficiency in fostering positive influence to engage a diverse workforce, with a focus on employee well-being, satisfaction, and performance. Additionally, they will implement control mechanisms to establish and measure organizational performance. Through this course, learners will gain hands-on experience in the multifaceted role of a manager within an organization.

In this course learners use Structured Query Language (SQL) on commercial relational databases. Using SQL and SQL procedural language, learners create and manage a relational database, addressing data integrity and security. In addition, learners explore the relationship between database administration and software development.
Prerequisite: DATA1201

The first step in exploratory data analysis (EDA) is pattern identification. To identify patterns within the data, learners utilize descriptive statistical methodology that is relevant and meaningful to the project goals. Learners use graphical analysis to explore relationships and correlations within the dataset. A variety of methods are used to handle missing data and outliers within the dataset. To categorize the data, learners build unsupervised learning models. Learners summarize the outcomes of their analysis from unsupervised learning models and use the outcomes to develop actionable business insights and recommend further analysis.

Learners will demonstrate the ability to apply marketing principles using digital analytics platforms to address data-driven challenges in organizational contexts. They will develop the skills to analyze sample data sets, draw correlations between consumer behavior, media, and campaign strategies, and present performance metrics effectively. Learners will also evaluate and articulate how various types of customer engagement evolve throughout the customer lifecycle in response to changing consumer behaviors.

Learners will develop and demonstrate the ability to analyze and apply risk control techniques, understand relevant laws, and navigate key concepts and practices in the insurance industry. By exploring real-world scenarios, learners will gain the skills necessary to effectively manage and mitigate risk in both personal and professional contexts.

Using identified relationships within the data, learners build simple predictive models based on regression and classification. Learners fit the data to the model, assess the model's performance, and make adjustments to the model's parameters to develop actionable business insights. Then they integrate their technical and communication skills to build and refine simple and complex visualizations to optimize effectiveness. Finally, learners close off a project, ensuring that key stakeholders have what they need to make decisions.

This course acts as the exam preparation for the Certification in Business Data Analytics Exam through the International Institute of Business Analysis. Business Analysis and Data Analytics builds upon strategic business analysis and data analytics. Learners will delve into the dynamic interplay between these two disciplines to illuminate their pivotal roles in maximizing stakeholder value by identifying, articulating, and recommending solutions for organizational change, leading to improved evidence-based decision-making.

This course will cover the application of the following concepts: production, marketing, R&D, HR, financial operations, and how key decisions impact business performance within a competitive market.

Working alone or in a small team, students research, design, develop, and implement an applied big data analytics research project to satisfy a real business, organizational, or community need. Students are expected to apply their knowledge and skills to produce a functioning prototype of their project idea.