This course introduces the foundational principles of Python programming with an emphasis on its application in data analytics. Students will develop into key Python libraries, including Pandas for data manipulation and Numpy for numerical computations. Through hands-on exercises, they will gain practical skills in extracting, analyzing, and visualizing data, equipping them with the tools necessary for data-driven business decisions.
This course is designed for students who want to go beyond the basics and focus on advanced data analysis techniques, with an emphasis on statistical analysis. This course covers advanced data processing, statistical modeling, and predictive analysis using tools like Python and R. Students will learn how to manage real-world data, apply statistical methods, and build predictive models. By the end of the course, students will be able to perform comprehensive data analysis and interpret their findings effectively.
This course focuses on the core ideas and practical workflow of business analytics for decision-making in organizations. The goal is to help students learn how to define data-driven business questions, select meaningful metrics, apply foundational analytical methods, and communicate insights in a clear and structured way. Students develop a basic understanding of how analytics supports planning, evaluation, and strategic choices.
This course focuses on applying machine learning methods to common business problems using real-world data. The goal is to help students understand the end-to-end process of building predictive models, including data preparation, model training, evaluation, and interpretation. Students are exposed to up-to-date ML models, and learn how to assess model performance and use results to support practical business recommendations.
This course focuses on understanding how artificial intelligence is used in modern business and what it means for management and strategy. The goal is to help students grasp key AI concepts, learn about the Fundamentals of AI and Machine Learning, identify appropriate business use cases, and evaluate benefits and limitations of AI adoption. Students also try learn core considerations for responsible and effective AI use in organizations.