Graduate Program Concentration:
Learn how to decipher data to make more informed business decisions.
How can you best use social media to connect with potential customers? What behaviors fuel purchase decisions? How can you spark a meaningful dialogue with a new generation of customers?
One of the most in-demand skill sets in 21st century business is the ability to uncover new opportunities, insights, and strategies through the analysis of business data. The Business Analytics Concentration takes a deep dive into the analytic mindset, giving you an in-depth look at the quantitative methods and cutting-edge analytics software tools used for strategic, data-driven decision-making, including:
An MBA with a business analytics background is an in-demand asset for nearly every industry and economic sector.
Careers in Business Analytics
This course provides an overview of business analytics, emphasizing how firms implement data-driven decision making. Students will learn an array of statistical concepts and different analytics methodologies, use spreadsheet modeling and learn through a mix of lectures, cases, practice problems and class discussions. The course will also provide opportunities to apply these concepts hands-on using a language such as “R.” An important goal of the course is to make students understand and implement fact-based decision making and enable them to become comfortable in gathering and analyzing data in order to make managerial decisions. Topics include exploratory data analysis, sampling, hypothesis testing, regression modeling, experimental design, analysis of variance, text mining, web analytics, and social media analytics. Case studies and assignments will introduce students to various contemporary business applications and innovative use of these ideas.
Prerequisite: Quantitative Skills 53:135:502, unless waived.
The course provides students with an in-depth introduction to the Hadoop ecosystem, which is an environment used by companies to store and manipulate “Big Data” of a size and scale that cannot be fit into traditional databases. Students will be exposed to tools, such as Sqoop, Pig, and Hive, for ETL (extract-transform-load) operations within Hadoop. The course also provides exposure to state of the art data mining algorithms for clustering, classifying, and collaborative filtering (a technique used by businesses to recommend products to customers). Participants will build machine learning applications with Apache Spark. In the second half of the course, students will learn information design, specifically tactics for visualizing Big Data that can consist of temporal, spatial, topical, or network relationships among entities.
Prerequisite: Data Management & Business Intelligence 53:623:517. Also, students should have familiarity with the Python programming language before taking this course.
The course enables students to ingest Big Data from APIs for social media platforms, such as Twitter. After assembling data from social media, students learn to analyze the data to gain business insights. Concepts for the analysis of social media, such as community detection and assignment, node centrality, information diffusion and opinion formation will be presented. The course also addresses analyzing unstructured text information using open source software. As an application of text analytics, the course explores the issues of analyzing product reviews and opinion mining. Students will learn the process for sentiment extraction, opinion mining, and recognizing opinion spam.
Prerequisite: Before enrolling in this course, students should have familiarity with Python programming language.