Curriculum

Core Courses (18 credits)

• Project Management

  Introduces relevant project management body of knowledge that business and IS project managers are required to effectively plan and execute projects in contemporary organizations. Emphasizes the holistic and practical roles of structured and agile project management frameworks, approaches, techniques, and tools.

  Software: Traditional project mgmt. tools (e.g., MS Project) and agile project management and simulation tools.

• Database Management

  Covers principles of designing and implementing transactional databases within a contemporary cloud environment, extracting valuable insights, monitoring resource usage, and optimizing performance.

  Software: Oracle Cloud with APEX, or MySQL Workbench, Snowflake/AWS with Streamlit and Snowsight.

• Business Process Design

  Covers fundamentals concepts, principles, and techniques that can be used to improve business performance through the analysis, modeling and design of the as-is and the to be business processes.

  Software: Business process modeling and analytical simulation tools (e.g., Lucidchart).

• Systems Analysis and Design

  Introduces students to the field of Systems Analysis and Design in general, and focuses in-depth on object-oriented concepts, methodologies, models, and skills, along with associated software tools.

  Software: UML Design tools (Lucid, Visual Paradigm), Python, IDEs for Python (Thonny, Visual Studio Code).

• Information Security Management

  Focuses on the managerial aspects of information security including access control models, information security governance, and information security program assessment and metrics. Foundational and technical components of information security is included to reinforce key concepts.

  Software: Wireshark, Webgoat, Security Onion.

• Information Systems Strategy and Governance

  Equips students to align information systems with organizational goals, focusing on strategic planning, governance, and leveraging IS for competitive advantage.

• Python Fundamentals for Business Analytics

  Provides a comprehensive introduction to Python programming, focusing on the essential concepts and libraries required for data science, AI and business analytics. Covers basic programming constructs, data manipulation, and visualization techniques using Python. Students learn how to leverage Python's powerful libraries, such as Pandas, NumPy, and Matplotlib, to analyze and visualize business data, enabling them to make data-driven decisions. Students develop the foundational skills necessary to apply Python in various business analytics scenarios.

  Software: Jupyter Notebook.

• Quantitative Analytical Methods in Business

  Provides a practical introduction to the relevant quantitative techniques required for business analysis and decision-making, including hypothesis testing, decision models, statistics, and forecasting. Python and Excel will be used for quantitative analysis. This is a hands-on course in the use of quantitative methods for turning data into information and information into good business decisions for managers, researchers, and students in the field of business. Providing a framework for the development of sound judgement and the ability to utilize quantitative and qualitative approaches, this course introduces students to the important role that data plays in understanding business outcomes and improving business processes.

  Software: Jupyter Notebook, Google Colab, Microsoft Excel.

• Machine Learning for Business Applications

  Provides students with an in-depth understanding of the core concepts and techniques of machine learning, tailored specifically for business contexts. Using Python, students learn how machine learning can be effectively integrated into various business sectors and understand the benefits and challenges of integrating machine learning models into business operations.

  Software: Jupyter Notebook, Spyder.

• Business Data Exploration and Visualization

  Provides essential skills of business analytics: data exploration and visualization. It encompasses three main components: understanding data, formulating research questions, and utilizing data exploration and visualization tools. Students learn to explore and visualize various data types, enhancing communication with collaborators and target audiences. The curriculum covers identifying relevant data, addressing practical business research questions, and mastering tools such as Python, R, Tableau, and PowerBI to uncover meaningful patterns and insights.

  Software: Python, generative AI tools with Tableau and Power BI.

• Cloud Data Warehousing

  Examines contemporary methodologies shaping data warehousing, with emphasis on cloud based solutions, ingesting semi-structured data from data lakes, and leveraging AI tools to streamline data flows.

  Software: Snowflake Cloud Services with connection to AWS, Tableau Prep Builder and Tableau Desktop, and Power BI.

• Business Analytics Capstone (project-based)

  Offers practical experience in applying business analytics and artificial intelligence to solve complex, real-world business problems. Students explore predictive analytics, data visualization, and geospatial analysis while developing technical proficiency with tools like TensorFlow, PyTorch, and generative AI. Through collaborative projects, students design innovative solutions, conduct sentiment analysis, and refine strategies based on data-driven insights. The course culminates in a final presentation, integrating key findings into actionable recommendations that demonstrate the impact of analytics on business success.

  Software: Python, generative AI algorithms with Google Colab.

• Python Fundamental for Business Analytics

  Provides a comprehensive introduction to Python programming, focusing on the essential concepts and libraries required for data science, AI and business analytics. Covers basic programming constructs, data manipulation, and visualization techniques using Python. Students learn how to leverage Python's powerful libraries, such as Pandas, NumPy, and Matplotlib, to analyze and visualize business data, enabling them to make data-driven decisions. Students develop the foundational skills necessary to apply Python in various business analytics scenarios.

  Software: Jupyter Notebook.

• Quantitative Analytical Methods for Business

  Provides a practical introduction to the relevant quantitative techniques required for business analysis and decision-making, including hypothesis testing, decision models, statistics, and forecasting. Python and Excel will be used for quantitative analysis. This is a hands-on course in the use of quantitative methods for turning data into information and information into good business decisions for managers, researchers, and students in the field of business. Providing a framework for the development of sound judgement and the ability to utilize quantitative and qualitative approaches, this course introduces students to the important role that data plays in understanding business outcomes and improving business processes.

  Software: Jupyter Notebook, Google Colab, Microsoft Excel.

• Machine Learning for Business Applications

  Provides students with an in-depth understanding of the core concepts and techniques of machine learning, tailored specifically for business contexts. Using Python, students learn how machine learning can be effectively integrated into various business sectors and understand the benefits and challenges of integrating machine learning models into business operations.

  Software: Jupyter Notebook, Spyder.

• Artificial Intelligence Strategy

  Explores strategic implementation of AI in business, covering AI applications, value creation, ethical considerations, and organizational transformation. Focuses on developing AI strategies for competitive advantage across industries.

• Business AI Applications

  Covers the implementation of Al methods in business problem-solving. As an interdisciplinary set of skills, artificial intelligence draws from a broad range of fields such as deep learning, natural language processing and cloud computing to effectively collect, store and process large amounts of data. This course will provide an understanding of fundamental artificial intelligence concepts and applications in today’s business environment.

• Business AI Capstone (project–based)

  Equips students with the skills to develop, launch, and manage AI based software products. Focuses on the intersection of business, technology, and data, emphasizing the unique challenges and opportunities AI presents in product development.