This course introduces students to the field of Artificial Intelligence (AI). They will learn the fundamentals of machine learning, deep learning, Natural Language Processing (NLP), generative AI, and the role of agents in intelligent automation. By the end of the course, students will emerge cognizant of the critical underlying concepts, resources, algorithms, issues, and infrastructure needed to design, manage, and monitor a modern AI application.

This course introduces students to the practical application of artificial intelligence in real world software systems. Building upon foundational AI concepts and prior programming experience, students will learn how to integrate existing AI services, APIs, and pre-trained models into modern software projects. Emphasis is placed on AI integration patterns, software architecture, ethical use, and problem-solving across key sectors such as business, public health, climate, disaster management, and the non-profit sector. Through lectures, demonstrations, and guided labs, students gain hands-on experience developing small scale applications that utilize AI for automation, data analysis, prediction, and decision support. The course emphasizes responsible design and evaluations of AI-driven features while ensuring technical feasibility and maintainable software design.

Students learn the core principles of data analytics using industry-standard tools to extract insights from real-world data. Python and Microsoft Excel are used to analyze datasets for patterns, trends, and decision-making. Students also explore the workflow of a modern data engineering project, including data collection, cleaning, analysis, and visualization. Topics include pivot tables, charts, Jupyter Notebook, NumPy, and pandas.

Students develop practical skills in data analysis and visualization using tools commonly found in modern software and data-driven organizations. Microsoft Excel and Python are used to apply summarization techniques and transform raw data into meaningful charts, graphs, and pivot tables. Students also use a modern data visualization platform such as Microsoft Power BI or Tableau to design interactive dashboards that enable end users to explore, analyze, and interpret data through clear, user-friendly visual interfaces.