Online Master of Science in Applied Business Analytics Degree

Prereq: AD100 Pre-Analytics Laboratory and ADR100 Introduction to R
This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. [4 credits]

Prerequisite: PY100 Intro to Python. Python is a modern, high-level programming language. One of the most popular programming languages, its use has steadily increased across a large number of industries. This course introduces students to the Python environment and teaches a solid foundation in the basic syntax and structure. Structured Query Language (SQL) is the most common language globally for interacting with relational databases. Employers have indicated that knowledge of SQL is one of the most important skills for new graduates entering the workforce. Even with advances in database technologies and languages for handling heterogeneous data types, SQL remains the core skill for interacting with data. This course introduces both languages to equip students pursuing an analytics education with the skills necessary to succeed in the analytics and data visualization field. The outcome of this course will be a focused survey of Python and SQL topics designed to equip analytics professionals rather than a deep focus on technical programming topics. [4 credits]

Introduction to the concepts, methods and problems of accounting and financial analysis. Includes accounting principles, measurement and disclosure issues, financial statement analysis, time value of money, cash flow projection and analysis, capital budgeting and project evaluation, bond and equity valuation, cost of capital and capital structure. 4 cr. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Critical Thinking. [4 credits]

The purpose of this course is to help improve business problem solving and managerial decision-making through the use of quantitative and qualitative decision-making tools and techniques. This course will provide the student with an overview of how decisions are made to solve management problems in the business environment. It introduces the fundamental concepts and methodologies of the decision-making process, problem-solving, decision analysis, data collection, probability distribution, evaluation, and prediction methods. Students will learn how to apply different quantitative and qualitative analytical tools commonly used in business to provide a depth of understanding and support to various decision-making activities within each subject area of management. Through the use of case studies of decisions made by managers in various production and service industries and a business simulation package specifically prepared for this course, the scope and breadth of decision-making in business will be described. [4 credits]

Prerequisite: MET AD 571. The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. [4 credits]

Prereq: METAD571
Become familiar with the foundations of modern marketing analytics and develop your ability to select, apply, and interpret readily available data on customer purchase behavior, new customer acquisition, current customer retention, and marketing mix optimization. This course explores approaches and techniques to support the managerial decision-making process and skills in using state-of-the- art statistical and analytics tools. Students will have an opportunity to gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive models, and quantify the incremental impact of specific marketing actions. Python, R, SQL, and Power BI software are used in this course. [4 credits]

Prerequisites: AD100, ADR100, AD571. Explore web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google Analytics tool is used for the collection of website data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module presents how web crawlers process and index the content of websites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. R, SQL, and Power BI software are used in this course. [4 credits]

This course is designed to provide students with practical experience in Generative AI applications, preparing them to leverage AI-powered tools in business, analytics, and automation. [4 credits]

Prereqs: AD100,ADR100,AD571
Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal to convert the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does. R, SQL, and Power BI software are used in this course. [4 credits]

Prereq: MET AD 599 or MET CS 521 or consent of instructor. Neural networks have revolutionized business domains. In many tasks, they perform better than traditional predictive models, which quickly become core business analytics components. In line with the market trend, this course aims to equip students with knowledge on business-related implementations of neural networks. The topics covered in this course include neural network architectures, techniques, models, and their business applications in time- series forecasting, sentiment classification, and recommendation system. Each module first explains the core concepts of a neural network model and guides students to write Python scripts. Please note that this course is not for developing neural network models from scratch but for understanding and using the models employed in TensorFlow and Keras for business applications. [4 credits]

Prereq: at least three of the ABA specialization courses AD616, AD654, AD688, AD699
The Business Analytics Capstone Project provides valuable learning experiences and opportunities to apply a set of techniques, competencies, and procedures acquired after the completion of all core and specialization courses within the MS in Applied Business Analytics program. The purpose of this course is to obtain insights about a business that results in improved data-driven decision- making to create value on different levels of an enterprise. Includes application of statistical, stochastic, and dynamic modeling, data mining, forecasting, and operations research techniques to the analysis of problems of business organization and performance. R, Python, SQL, and Power BI software are used in this course. The solving of real problems facing different size companies are assigned to small teams of students and is overseen by our curriculum advisory board, ABA faculty, and business partners from a range of industries. [4 credits]