CurriculumOnline MS Economics

Big Data Analytics in the Cloud

This course emphasizes the role of AI in big data analysis. By utilizing cloud computing, this course serves as a platform for managers and data scientists who want practical experience in handling and analyzing large amounts of data. The course follows a three-module layout, starting with an introduction to cloud computing, its enabling technologies, and its key components. This module includes assignments using the Google Cloud Platform (GCP), providing students with hands-on learning in the cloud environment. The second module is devoted to AI tools like Generative AI, ChatGPT, LLM, and LangChain. These tools are used for moving, cleaning, structuring, storing, examining, and visualizing big data in the cloud. The module also focuses on 'Prompt Engineering', a field that improves the performance of AI models through optimized input prompts. The final module covers the creation of data pipelines in GCP. The knowledge gained in this module enables students to manage big data for training, analyzing, and predicting using AI/ML and other data science techniques.

Collaborative Innovation

This course asks learners to view innovation through the lens of collaboration - as a phenomenon that occurs horizontally across organizational boundaries. Learners will gain insights and skills to design and guide the conversations, platforms, and ecosystems that lead to shared-value innovation. The course brings together theories and insights from a variety of disciplines including management, psychology, and social science. Understanding how to design and guide collaborative innovation processes is a vital skillset and knowledgebase in the 21st Century economy, defined more by open networks than the rigid hierarchies of the past.

Frontiers in AI

Artificial Intelligence is a rapidly growing group of technologies that will radically change the shape of business and culture over the next several decades. This self-paced course will walk you through a high-level overview of the technologies that are considered AI and what is state of the art today. From there, we will look at three industry examples: Agriculture, Medical, and Marketing. These examples will include industry and academic expert perspectives and look at how each of the industries are already being impacted by the disruptive changes that AI is creating. Each week will consist of a series of readings and videos covering the material.

Web Data Analytics

This course, applicable across industries, aims to train leaders in algorithmic business problem-solving skills within the context of web data. It emphasizes the development of abilities to communicate with and manage teams that collect data and analyze them, and make data-driven decisions. We will cover tools to collect, manipulate, and analyze data from the web and other sources, with the objective of making students data savvy and comfortable with deriving insights from real-world, large datasets. Students will be exposed to the power of clickstream analysis and the possibilities that can be unleashed from testing and experimentation. A key aspect of the course is the integration of algorithmic thinking and computational abstraction. Students will learn to break down complex problems into manageable parts, filter out irrelevant information, identify key components, and create simplified solutions to problems. This approach not only simplifies problem-solving but also enhances efficiency and accuracy.