XMSBA ACADEMICS

This course examines how statistical methods lead to understanding and describing data. We use R/RStudio to review basic data exploration and statistical methods. Topics covered include simple descriptive statistics through hypothesis testing and modeling.

In this course, students gain competence in practical database, data warehousing, and data management skills with emphasis on query, data modeling, ETL, and data management. They will also become familiar with major elements of the big data ecosystem.

We will study the fundamental principles and techniques of data mining in order to extract useful information and knowledge from data. We will improve our ability to approach problems "data-analytically,” examine real-world examples that place data mining in context, and apply data-mining techniques while working hands-on with data mining projects. The course will provide an understanding of the general framework for building and evaluating predictive models, both for classification and numeric prediction data mining tasks. The course will cover supervised predictive modeling techniques as well as unsupervised predictive modeling techniques.

This is an introduction to basic concepts in machine learning, both supervised and unsupervised. The basic ideas of neural networks will be presented. Machine learning from data streams (online learning) using stochastic gradient descent is also covered.

Where the art of graphic design meets with the science of data analytics. Learn how to perform exploratory analysis through visualization, how to create professional-looking visualizations for use in business reports and presentations, and how to design interactive visualizations and dashboards.

Delve into a number of selected current and emerging data analytics areas that are increasingly important for organizations. Areas include advanced elements of the predictive modeling process, ensemble methods, cost-aware data analytics, mining text and data, recommender systems, and other advanced topics.

This course introduces students to optimization and simulation, powerful techniques for better decision-making (in particular, linear, integer and non-linear programming), and Monte Carlo and discrete event simulation, and discuss their application to problems in business and data analytics.

The xMSBA Analytics Practicum leverages skills and techniques learned throughout the course of the program and applies them to real-world business situations. Students formally define problems, clean data, aggregate with other data sources, and identify and use appropriate analytical techniques to address questions.