Online Applied Business Analytics—Graduate Certificate

At a Glance

Completion Time

Months

Credits Required

Tuition

$14,850

Part-Time*

*Based on 2026–2027 Boston University tuition and fees.

Turn Data into Decisions with Applied Business Analytics Skills

The online Graduate Certificate in Applied Business Analytics provides comprehensive coverage of data analytics concepts, techniques, and state-of-the-art tools used in the process of data-driven business decision-making. Students have the opportunity to gain hands-on experience with a variety of analytical models and decision-support tools and to apply them to interlinked data-inputs and large data sets in the areas of marketing, operations, product and technology innovations, financial services, human resources management, and others. The curriculum covers advanced software tools and functions such as predictive modeling, text and data mining, visual analytics, simulations, and OLAP tables. Graduates of the program will be able to analyze data-driven business processes, select appropriate analytical methods to monitor and identify performance issues, and propose optimal data-based solutions.

The Graduate Certificate in Applied Business Analytics is also available on campus in Boston. Learn more.

Curriculum

Boston University’s Graduate Certificate in Applied Business Analytics consists of four required online courses (16 units).

Students who earn the Graduate Certificate in Applied Business Analytics may waive four specialization courses for the Boston University Metropolitan College Master of Science in Applied Business Analytics degree program.

Prerequisites

Core Courses

(Four courses/16 units)

MET AD 571 Business Analytics Foundations

4 credits. Fall and Spring

Corequisite: MET AD 100 Lab - 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). You 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 and Power BI software are used.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Yu	Independent	M	06:00:00 PM–08:45:00 PM	STH B22

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Ritt	Independent	T	12:30:00 PM–03:15:00 PM	MET 122

Section A3, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Parzen	Independent	W	02:30:00 PM–05:15:00 PM	CDS 264

Section A4, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Page	Independent	R	06:00:00 PM–08:45:00 PM	MET 122

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Parzen	Independent	ARR	12:00:00 AM–12:00:00 AM

And three courses from the following:

MET AD 616 Enterprise Risk Analytics

4 credits. Fall and Spring

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 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. Python and R software are used in this course.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Parzen	Independent	T	06:00:00 PM–08:45:00 PM	MCS B29

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Ritt	Independent	ARR	12:00:00 AM–12:00:00 AM

MET AD 654 Marketing Analytics

4 credits. Fall and Spring

Prerequisite: MET AD 571. 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 techniques to support the managerial decision-making process and skills in using state-of-the-art statistical and analytics tools. Students will gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive machine learning models, and quantify the incremental impact of specific marketing actions. Python and Tableau are used in this course. No prior Python experience is required.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Page	Independent	W	06:00:00 PM–08:45:00 PM	EPC 208

MET AD 688 Big Data and Cloud Analytics for Business

4 credits. Fall and Spring

Prerequisite: MET AD571 Work with large, complex datasets beyond traditional desktop analytics using Apache Spark (PySpark), DuckDB, Polars, SQL-based data warehousing, and AWS cloud services. You will explore SQL databases through AWS’ Relational Database Service (RDS), learn to build feature engineering pipelines, and scalable machine-learning workflows using Spark MLlib. Key topics include cloud computing parallel processing, management of massive data stores, cloud data architectures, web scraping, API-based data collection, text and web mining, and batch and streaming analytics. You will develop an end-to-end analytics solution from data ingestion and cleaning to modeling, evaluation, and communication, using Python (PySpark), SQL, Git, and GitHub in a cloud-ready AWS environment. By the end of the course, you will be able to design scalable analytics pipelines, manage cloud data environments, and apply distributed machine learning to real-world datasets. The course culminates in a term project implementing a complete big data and cloud analytics workflow.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Padalkar	Independent	M	02:30:00 PM–05:15:00 PM	MCS B29

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Padalkar	Independent	ARR	12:00:00 AM–12:00:00 AM

MET AD 699 Data Mining for Business Analytics

4 credits. Fall and Spring

Prerequisites: AD571 Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal of converting 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 of understanding (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. Python, R, SQL, and Power BI software are used in this course.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Athaide	Independent	R	06:00:00 PM–08:45:00 PM	MCS B29

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Athaide	Independent	ARR	12:00:00 AM–12:00:00 AM

Dates & Deadlines

View BU MET’s academic calendar for online programs, including important dates and deadlines.

Application Requirements

Learn about application requirements for BU MET graduate degree and certificate programs.

Two graduate students focusing in a classroom at Boston University

How You Benefit from a
Boston University Education

A BU credential can help lay the foundation for career advancement and personal success.

Benefit from a 24:1 average student-to-instructor ratio.
Work closely with highly qualified faculty who draw from active research and substantial professional achievements in areas such as descriptive, predictive, and prescriptive analytics.
Focus on practical, hands-on education, ensures you develop expertise in integrating the “5 Vs of big data”—volume, velocity, variety, veracity, and value of information—into sound analytics-based business decisions.
The ABA advisory board actively engages with students, alumni, and industry veterans. It provides access to the latest industry trends in data science and business analytics and facilitates learning about career pathways through seminars and talks.
Two levels of preparatory analytics laboratories offer access to advanced tools and provide opportunities to hone analytics skills using cases that are populated with realistic data.

“To me, the best part of the ABA program is the balance between the technical aspects of data analytics/science and the broader implications of those methodologies to the underlying business at large.” Read more.

Greg Parker (MET’23)
Vice President of Business Intelligence, Rockland Federal Credit Union
MS, Applied Business Analytics

Advance Your Career

BU MET’s Applied Business Analytics graduate programs prepare you for a wealth of different roles, such as business analyst, data analyst, financial analyst, project and program managers, business intelligence analyst, sports statistical analyst, among others.

Recent graduates have found job opportunities and career paths at companies such as:

Amazon Web Services (AWS)
Boeing
Collins Aerospace
Huawei

Procter & Gamble
PUMA Group
The Kraft Heinz Company

Applied Business Analytics Faculty

Vladimir Zlatev

Associate Professor of the Practice

Director of Digital Learning, Administrative Sciences

Coordinator, Applied Business Analytics

Krystie Dickson

Lecturer, Administrative Sciences

Christopher Athaide

Assistant Professor, Administrative Sciences

Gregory Page

Master Lecturer, Administrative Sciences

David Ritt

Lecturer, Administrative Sciences

Nakul Padalkar

Associate Professor of the Practice, Administrative Sciences

Sree Kumar Valath Bhuan Das

Lecturer, Administrative Sciences

Hanbo Yu

Lecturer, Administrative Sciences

Michael Parzen

Associate Professor of the Practice, Administrative Sciences

View All Faculty

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Online Applied Business Analytics Graduate Certificate

At a Glance

Turn Data into Decisions with Applied Business Analytics Skills