Online MSBA Curriculum - Brandeis University

Curriculum Details

32 Total Credits Required

Through eight in-depth courses, the online master’s degree in business analytics allows you to learn in a flexible yet rigorous part-time format. Every step of the MSBA degree involves a real-world approach. All core competencies are applied to industry challenges in a variety of top industries, including finance, marketing, healthcare and sports management, with the help of expert faculty.

Required Courses

Credits

OBAN 200 Introduction to Business Analytics Credit Hours: 4

Introduction to business analytics emphasizing the business case for data in a decision-making framework. In addition, data analytics pre-requisite fields of probability and statistics are presented.

OBAN 201 Computational Tools Credit Hours: 4

Detailing the computational tools required. Computer programming via R and Python and the packages used for data analytics are presented. Also, common tools such as RStudio, Jupyter notebooks, and Amazon Web Services are presented.

OBAN 202 Data Analytics Foundations and Visualization Credit Hours: 4

Detail the process to convert a business problem into a data analytics form. Understanding data types and relationships through the use of relational databases and the Structured Query Language (SQL). Also, provide an overview of the field of visualization, presenting current technologies, best practices, and real-world implementation.

OBAN 203 Machine Learning Credit Hours: 4

Present the underlying data science models used for supervised, both classification and prediction, and unsupervised learning. In addition, topics of feature engineering, overfitting, and performance are discussed. Extensive use of data and computer code is used to illustrate the salient features of the models.

OBAN 204 Applied Data Analytics Credit Hours: 4

Real-world applications of data analytics to specific data analytics problems. Students are encouraged to ascertain a working knowledge of which models should be used for the business cases considered and the data provided. Students will become experienced in methods of data storytelling, reproducible model building, presentation and communication of analysis results.

OBAN 205 Natural Language Processing and Deep Learning Credit Hours: 4

Detail the paradigms of Natural Language Processing (NLP) and Deep Learning for applications in language and computer vision.

Elective Courses – Choose 2

Credits

OBAN 220 Healthcare Analytics Credit Hours: 4

Application of business analytics to the healthcare industry. Detailing business use cases, common data, feature engineering, and overall model building to achieve a data-driven decision-making framework.

OBAN 230 Marketing Analytics Credit Hours: 4

Application of business analytics to the marketing industry. Detailing business use cases, common data, feature engineering, and overall model building to achieve a data-driven decision-making framework.

OBAN 240 Finance Analytics Credit Hours: 4

Application of business analytics to the finance industry. Detailing business use cases, common data, feature engineering, and overall model building to achieve a data-driven decision making framework.

OBAN 250 Sports Analytics Credit Hours: 4

Application of business analytics to the sports industry. Detailing business use cases, common data, feature engineering, and overall model building to achieve a data-driven decision making framework.

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