An introduction to data visualization techniques. Data-driven decisions are increasingly embedded in business organizations, so professionals must be able to explore and communicate data with understandable and powerful visualizations.

Helps students gain a solid foundation in the concepts, processes, tools, and techniques needed in analyzing business processes and conducting information systems projects.

With vast quantities of data being generated, including new types of data such as web traffic, social network data, and reviews and comments on websites, "big data" and "analytics" are important topics. Data, when used correctly, can create a competitive edge for firms. Advances in computing hardware and algorithms have improved the quality of predictions and effectiveness of predictive business applications. Expertise in working with data, and deep knowledge of data mining/machine learning methods, is a sought-after skill. This course introduces key tools and techniques of data mining: classification, prediction, cluster analysis, and text mining. The methods covered are linear and logistic regression, k-nearest neighbors, naive Bayes, classification and regression trees, ensemble methods, neural networks, k-Means and hierarchical clustering, and association rules. The course will focus on business applications, with examples from Marketing, Finance, Healthcare, and Operations.

Data-driven decision-making is a key competitive advantage in today's business landscape. This course provides a foundational introduction to machine learning techniques and algorithms, equipping students with analytical and software tools to extract insights and create value from data. The course mainly focuses on predictive analytics methods, including linear models, decision trees, nearest neighbor classifiers, boosting, random forests, neural networks, probabilistic models, text analysis, complexity control, regularization, and model evaluation. Additionally, students will explore unsupervised learning techniques such as clustering, collaborative filtering, and dimensionality reduction, with applications in customer segmentation and recommender systems. Through real-world business examples, students will gain hands-on experience using Python in applying machine learning to marketing, forecasting and operations domain.

Uses a learning-by-doing approach to understand key concepts behind Cloud Computing and Big Data , the strategic drivers of these technologies and the value propositions that they provide to industry. The course cover technologies within this ecosystem including Hadoop, AWS, Pig, Hive, Amazon Web Services and Spark. The focus is on creating awareness of the technologies, providing some level of familiarity with them through hands-on exercises and projects, and enabling strategic thinking around the use of these technologies in business.

Artificial Intelligence (AI) is penetrating our daily routines deeply and is revolutionizing almost every aspect of business. Firms are increasingly using technologies such as natural language processing, neural networks, and deep learning to generate deep insights. At the same time, AI algorithms are challenged by issues of bias, ethics, and transparency. This course aims to equip students with the essential knowledge of the current wave of AI. It uses a hands-on, learning-by-doing approach to understanding the concepts behind AI, the strategic drivers of these technologies and the value propositions that they provide to industries. The focus is on creating awareness of the technologies, allowing some level of familiarity with them through assignments, and enabling some strategic thinking around the use of these in business.

There has been an explosion in the volume, velocity, and variety of financial data in the last decade. Most financial institutions have automated their processes and digitized their solutions. This has generated a healthy demand for professionals from a variety of fields who not only use cutting edge technologies but also understand financial terminology and concepts. The focus of the course is to introduce students to financial concepts, provide relevant background in the financial industry and teach them the basics of putting together multi-asset portfolios using stocks, bonds, commodities and options instruments. The course requires no background in finance; it uses a practical approach to understanding financial concepts using various open-source technologies such as Python. The IT know-how required for this course is prior experience with Python.