Curriculum
Curriculum Overview
In one year and 30 credit hours, develop the portfolio of skills you need to leverage business analytics and effectively translate and communicate your insights into strategic corporate actions, solutions, and results.
View the 2019-2020 academic calendar
Integrated Modules
A key advantage of the M.S. in Business Analytics Program is its highly integrated, modular curriculum, which includes five modules that each blend vital analytics, business, and leadership skills — managing projects and teams, executive communication and presentation skills, consulting skills, and design thinking.
Module 1: Enterprise Analytics: Using Data to Gain a Competitive Advantage
Description: In this module we discuss how firms are leveraging analytics at the enterprise level. Understanding the business is critical to uncovering actionable insights from data. We focus on understanding customers and clients, the processes by which organizations deliver their products and services, and how organizations create and measure value. We will cover how the data support these processes and may be used to create competitive advantage.
Technologies and Tools: Excel, Palisade StatTools, Oracle Crystal Ball, iRapidMiner
Module 2: Descriptive Analytics: Communicating the Value of Data-Informed Insights
Description: We will cover the fundamental analytical and statistical methods used to make data-informed business decisions. We will discuss best practices for the extraction and aggregation of useful information and use R to prepare data for more advanced analysis. We will also learn about key econometric and statistical models and metrics for pricing and customer lifetime value. Additionally, the strategy consulting process will be introduced to develop key communication strategies, including data journalism.
Technologies and Tools: R, Python, Excel, Tableau
Module 3: Predictive Analytics and Machine Learning: Driving Agility Through Corporate Foresight
Description: We will learn to use the patterns found in our business data to predict customer behavior and gain market intelligence, using classic statistical and machine learning techniques as well as emerging deep learning methods. In addition, we will learn how to use a company's data warehouse to create compelling visualizations for corporate dashboards. Design thinking will be employed to identify user needs and translate those into digital products or services.
Technologies and Tools: R, Python, SPSS, MySQL, Tableau, TensorFlow
Module 4: Big Data Analytics and Artificial Intelligence: Monetizing Data in an Era of Digital Transformation
Description: We will learn to work with large-scale unstructured data sets encompassing user-generated text, clickstreams, and networks. We will discuss NoSQL technologies and advanced machine learning methods for storing, analyzing, and deriving insights from big data. In addition, we will learn how web and search analytics is being used as part of the digital measurement framework, and how to apply agile project management techniques.
Technologies and Tools: Hadoop, Spark, NoSQL, Hive, Hortonworks, AWS, TensorFlow, Google Analytics, AdWords
Module 5: Corporate Analytics Capstone: Translating Your Knowledge into Strategic Business Solutions
Description: In our last module, we will work on a unique analytic challenge. We will design a solution to include a working prototype or software application, assess the business impact of your solution, and prepare and deliver an oral presentation.
As part of our closing residency, we will also discuss creating meaningful futures through ethical AI, innovative thinking, improvisation, and engaging with others.
Technologies and Tools: R, Python, R Shiny