Data analytic literacy is essential in order to interpret and leverage data, and data-driven decision making can greatly enhance organizational competitiveness. The goal of this course is twofold: First, it is to offer an introduction to business analytics by exploring key concepts, tools, and techniques; second, it is to offer a broad overview of data-driven decision making processes and approaches. Students will learn how different data types and structures can be transformed into valid and reliable decision-guiding insights, and how objective evidence can be used to substantiate and guide organizational tactical and strategic choices. 

This course delves into statistical concepts and techniques that are of core importance to business analytics. Students will be immersed in select descriptive, confirmatory, and predictive methodologies and techniques including sampling and probability theories, hypothesis testing, correlation and crosstabulation, as well as regression and time series analyses. Students will learn how to apply these methods to real-world business problems, enhancing their ability to draw meaningful insights from data.

This course focuses on review, preparation and basic descriptive analysis of data. Using real-world data, students will learn how to assess analytic usability of available data, identify and execute any necessary data restructuring and feature re-engineering steps, plan and execute basic exploratory analysis, as well as design and create compelling visual dashboards, scorecards and other summaries. The course emphasizes hands-on learning using real-world data.

This course introduces students to the rationale and methods used to fit and validate predictive models and forecasts. Predictive modeling techniques covered by this course include linear and logistic regression, and decision trees; forecasting techniques cover widely used time-series methods such as Autoregressive Integrated Moving Average (ARIMA). The course emphasizes hands-on learning using real-world data.

In this course, students will learn how to evaluate, select, and use supervised and unsupervised machine learning algorithms for classification and prediction tasks, focusing on structured numeric data. The key differences between supervised and unsupervised machine learning algorithms will be highlighted in the context of real-world problems and data; core methodological differences between classification and prediction focused algorithms will also be discussed.

This course explores the challenges and opportunities presented by unstructured text data. Students will be introduced to leading unstructured data warehousing technologies, including Hadoop, Spark, and NoSQL databases, as well as approaches and tools used to mine unstructured text data for insights. The course highlights the general text mining process in the context of the rudimentary bag-of-words analysis and more advanced sentiment analysis.

This course takes a broad view of analytics by bringing together longstanding statistical approaches to analyses of data, established machine learning algorithms, and newly emerging generative artificial intelligence (AI) methods of exploratory inquiry, with the goal of demonstrating how complex business problems can be tackled by synthesizing diverse types of decision-guiding evidence. The core purpose of this course is to bring to light unique strengths and weaknesses of each of the three broad analytic approaches, and by doing so to create awareness of potential value creation opportunities associated with thoughtfully amalgamating different sources of decision-guiding insights.

The capstone project is a comprehensive, hands-on course where students apply their knowledge to a real-world business problem. Working individually or in teams, students will gather data, perform analysis, and present their findings and recommendations. This project allows students to showcase their analytical skills and gain practical experience.