MAST - Mathematics, Analytics, Science and Technology

QTM6300 Machine Learning for Business
(Formerly Data Exploration and Analytics)
3 Blended Credits

This course will examine the methods and challenges faced in turning data into insightful analytics in business. With data sizes significantly increasing in the last decade, extracting meaningful information to compete successfully is essential. You will accomplish this by learning techniques for data gathering, data analysis, and visualization as well as in discussion on companies currently trying to turn the information they gather into business opportunities. We will learn a variety of methods and software for finding patterns(such as regression, neural networks, association rules, CART, forecasting etc.), building models, and ultimately making decisions using large data sets. Guest speakers who are executives and consultants in the field of analytics and visualization will discuss how they address these challenges in their companies. This is a hands-on course with in-class exercises and group projects to help students learn and apply data analysis techniques preparing them for the practical challenges analysts face in the real world. We will address questions such as:

- How does Amazon recommend products based on your past purchases?
- How to forecast energy consumption based on historical weather and consumption data?
- How do credit-card companies detect fraud?
- What challenges does Big Data pose to companies and how to handle these challenges?

QTM6610: Analytics for Decision-Makers

1.5 credits

Data exploration and data-driven decision making are integral to identifying and validating business opportunities. Depending on the nature of the problem and the entrepreneurial context, techniques ranging from classical statistical methods (descriptive and inferential statistics) to more recent advances in big data and tools (Excel, R, Tableau) might provide the greatest utility and deepest insights. In this course, students encounter a selection of these techniques and develop their ability to formulate analytics problems in ambiguous contexts, quantify the performance of various solutions, and articulate the key results of their analysis to a non-technical audience, including through visualization methods. 

QTM7200 Data, Models and Decisions

2 Credits

Data, Models and Decisions (DMD) - This course is concerned with identifying variation, measuring it, and managing it to make informed decisions. Topics include: numerical and graphical description of data, confidence intervals, hypothesis testing, regression, decision analysis, and simulation. Applications to Economics, Finance, Marketing, and Operations illustrate the use of these quantitative tools in applied contexts. The course utilizes spreadsheet, statistical, and simulation software.

QTM7571 Introduction to Machine Learning Methods for Business
(Formerly Business Intelligence, Analytics & Visualization)
3 Credits

This course introduces machine learning methods for business intelligence. Given the ease of data collection and storage, extracting meaningful information from data has become an essential trait for competitiveness, for companies large and small. In this course, you will learn a variety of supervised and unsupervised machine learning methods that companies use to turn data into insights, such as linear regression, k-nearest neighbors, logistic regression, classification and regression trees, etc. You will get hands-on experience in data pre-processing, generating business predictions, and model performance evaluation. Your learnings will be in practical contexts with in-class exercises and projects.

The various methods covered in this course will be implemented using a programming language. No prior knowledge in programming is required.

Prerequisites: QTM 7200 OR QTM7800

QTM7800 Business Analytics

2 Credits (Core MBA)

If you have taken and passed QTM7200, you cannot register for QTM7800, as these two courses are equivalent

In the BA stream of the course, regression models are used to understand dependence relations and thereby improve the accuracy of predictive modeling. Sensitivity analyses are used to determine which factors drive our decisions, and, thus, determine which factors need to be carefully managed. In the OIM stream of the paired course, strategic tradeoffs are discussed to understand the operations and information models for a variety of settings (e.g., startups, nascent or established organizations) and thereby improve any model by utilizing resources (e.g., physical assets, people, data, digital technologies, markets) and processes for the flow of goods, people and information.