MBA in Business Analytics

This course is an introduction to data science—the iterative exploration of data as a means to uncover new insights and inform future business process optimizations. You will move on a journey through the lifecycle of a data analytics project, using a real-world company as your basis. Learn to frame analytics problems and map business questions to appropriate predictive analytics techniques within the context of real-world applications from diverse business areas.

Learn to transform a wide range of statistical and machine learning methods into innovative data insights. You will gain hands-on experience as you implement statistical and machine learning methods, from data pre-processing to generating predictions and evaluating model accuracy.

Work with fundamental programming concepts, including data structures, networked application program interfaces, and relational databases using the Python programming language. You will also apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular Python toolkits to gain data insights.

Python, a general-purpose programming language, has rapidly become one of the most popular languages among data scientists. In this course, you’ll learn powerful ways to conduct data analysis, using Python to efficiently collect, clean, analyze, visualize, and narrate insights from your data.

Discover some of the most important analytics-related trends, how analytics are transforming whole industries, and how you can build a competitive data strategy. In this course, you’ll discuss various approaches to analyzing structured and unstructured data and you’ll also explore several popular business intelligence tools. The highlight of this course will be an industry-specific team project, during which you will employ the concepts and best practices discussed in class.

Do you anticipate making or evaluating economic and business forecasts in your career? This hands-on course will provide you with a comprehensive introduction to the field of forecasting and econometrics and covers three main areas of forecasting: basic statistical methods, structural models, and time series models.

Discover the benefits of using a systematic and analytical approach to marketing decision-making. This course integrated marketing concepts with practice, emphasizes learning by doing, and provides software tools to help you apply marketing concepts to real world situations.

In this class, you will learn several methods to address the numerous challenges associated with modeling and forecasting financial and macroeconomic variables. In particular, we will address cointegration (to address non-stationarity), vector autoregression (to allow for interdependence between variables), and a machine learning algorithm to conduct unbiased model selection from "big data" while addressing outliers and structural change.

Tackle real-world problems throughout the healthcare industry in this hands-on, two pronged course. Not only will you develop your understanding of the healthcare market and the organizational structures of major stakeholders, but you will also use empirical methods to analyze the challenges these stakeholders face, assess their business opportunities, and design your own evidence-based strategies.

Explore current industry trends and strategies as you dive into the three prongs of digital marketing: owned, paid, and earned media. You will learn how to use and integrate these three types of media while building the basic technical skills you need to thrive in the digital space.

In this course, you’ll work with cognitive technologies (another phrase for "artificial intelligence") to understand their value and how you could implement them within a business. You will review a range of cognitive cools, from machine learning to natural language processing to "deep learning." This course also includes several guest lectures from external experts throughout the semester. No programming background is required.