BUS-K 579 Experimental Courses
- Variable
- Prerequisite(s): See individual course descriptions below
Data Visualization
- 7 weeks
- 1.5 credits
- Prerequisite(s): MBA Core or MSIS Core
Information overload in becoming central to the information economy. In his book, Information Anxiety, Richard Saul Wurman describes how the New York Times on an average Sunday has more information than a Renaissance-era person had access to his or her lifetime. We are getting better at collecting data, but we still lag in what to do with it. With increasingly large amounts of data that needs to be processed for effective decision making, graphical encoding of data, or data visualization, can better support analytic reasoning. Based on principles of visual perception and cognitive science, this course focuses on many different data visualization techniques that support different types of analytic tasks, for example, time-series analysis, part-whole analysis, deviation analysis, distribution analysis, bivariate analysis. Students will learn how to graphically display information using charts, maps, diagrams, and visual narratives, and how to bring them to life using Tableau, a closed source application, and d3.js, a Javascript library, which is quickly becoming a standard in visualization for communication.
Business Analytics Foundations
- 16 weeks
- 3 credits
- Prerequisite(s): MSIS Core
Business analytics is the use of database queries, statistics, machine learning algorithms to find patterns of relationships between data elements in large and noisy data sets, which can lead to actions that accrue organizational benefits, for example, by reduction of costs, enhancement of revenue, and better management of business risks. By finding patterns previously not seen, business analytics not only provides a more complete understanding of data but is also the basis for models that predict, thus, enabling managers to make better decisions. This course discusses several commonly used exploratory and predictive data analytic techniques, such as association rule mining, clustering, nearest-neighbor classification, decision trees, naïve bayes, SVM, ensemble learning, etc. You will learn how the above business analytics techniques are applied in a variety of business applications and organizational settings, and understand the process of introducing data analytics technologies into the business environment. This includes, for example: collecting relevant data; applying appropriate data analytics techniques to various business problems; evaluating and Interpreting exploratory and predictive data analytics models.
Business Applications of Artificial Intelligence
- 7 weeks
- 1.5 credits
- Prerequisite(s): MSIS Core
To rethink business capabilities for a digital future, we will focus on Artificial Intelligence (AI) and its applications on both digital processes and digital products. The course starts by examining the rise of AI and explaining different types of AI techniques and their applications for business. That is followed by a discussion about the increased capacity of machines to “sense” the world from speech to visual recognition and how that can be used to facilitate interactions with organizational stakeholders. We then use this context to discuss digital processes through Robotic Process Automation (RPA). The course will examine not only what RPA is, but also take a closer look on how bots can integrate with human employees in the workforce of the future. We follow these topics with a discussion of how society and organizations will be impacted by AI. First, we look at how cities will become “smarter” with the adoption of AI and the implications for organizations and individuals. Second, we look at the potential ethical, legal, and societal consequences of adopting new technologies and the roles organizations have in setting the future. We conclude the course with an idea showcase in which students will have an opportunity to present concepts of new products and services enabled by AI.
Business Applications of Machine Learning
- 7 weeks
- 1.5 credits
- Prerequisite(s): MBA Core and K513
High quality information is the key to successful management of businesses. Despite large quantity of data that is collected by organizations, managers struggle to obtain information that would help them in decision making. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. This course provides a broad introduction to machine learning: tree models, rule models, linear and non-linear models, distance-based models, and probabilistic models. By finding patterns previously not seen, machine learning not only provides a more complete understanding of data but is also the basis for models that predict, thus, enabling managers to make better decisions.