Online Graduate Certificate in Data Analytics Engineering

College of Engineering

Master statistical analysis, visualization, and predictive modeling.

Submit your Fast App

100%

online

8+

Months

16

credits

$5,250

$329/credit

Program Overview: Build engineering-driven data analytics skills

Data is transforming how industries solve problems, optimize systems, and make decisions. The Online Graduate Certificate in Data Analytics Engineering at Northeastern University equips professionals with the technical skills and applied knowledge to turn data into actionable insight. Designed for learners with a background in engineering, computer science, or related fields, this program combines statistical analysis, data visualization, and mining techniques with real-world applications in engineering, healthcare, manufacturing, and beyond.

This certificate stacks into multiple master’s degrees—including Northeastern’s Online MS in Data Analytics Engineering, Online MS in Business Analytics, and Online MS in Healthcare Analytics—giving you a clear path to further credentialing and career growth.

What You'll Learn: Core technical skills for data-driven decision-making

Apply foundational statistical methods and computational techniques to clean, wrangle, and analyze complex data sets
Use Python, R, and modern data visualization tools to communicate insights across engineering and business contexts
Understand data management principles, including relational and NoSQL databases, data governance, and warehousing
Implement data mining algorithms for classification, prediction, clustering, and pattern discovery
Translate analytical findings into solutions for industries like healthcare, manufacturing, and operations

Data Analytics Engineering Program Benefits and Features

Engineering-focused, application-driven curriculum
Designed with an emphasis on technical rigor and applied learning, this program prepares you to handle real-world datasets and analytics challenges in complex, data-intensive industries.

Flexible entry into multiple master’s degrees
All courses in the certificate apply directly to the Online MS in Data Analytics Engineering and can also be used to stack into master’s programs in Business Analytics or Health Data Analytics. Earn a B or better in all your certificate courses to ensure stackability into an MS degree.

Expert faculty and hands-on learning
Learn from experienced faculty in Northeastern’s College of Engineering and gain practical skills through real-world case studies, projects, and interactive tools that mirror industry expectations.

Earn credits toward a master's degree

Lay the foundation for a future master’s degree: The Online Graduate Certificate in Data Analytics Engineering can pave the way to a master's degree at Northeastern University—if you earn a B or better, you can apply credits to the following degrees:

Incrementally advance your education with valuable credentials that immediately help you move forward in your career.

Want to Prepare?

Although there aren't any prerequisites besides any bachelor's degree for the Online Graduate Certificate in Data Analytics Engineering, you'll need to be familiar with Python Programming Proficiency, Fundamentals of Probability Theory, and Linear Algebra in order to succeed in the certificate.

This series of courses has been hand chosen by College of Engineering faculty and will provide you with the skills to enhance your success in the program:

How to Join

Join the Online Graduate Certificate in Data Analytics Engineering using Northeastern Online's Fast App, a streamlined graduate admissions process you can complete in less than 5 minutes. No lengthy application. No waiting for an admission decision.

All you need is a bachelor's degree in any field to join.

⇒ Learn more about the Fast App.

College

Current Fast App dates

Term	Submit app by	Enroll by	Start courses
Fall 2025	August 1	August 25	September 3

Submit your Fast App by August 11 to start September 3.

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Core courses

Take all four courses as part of the graduate certificate.

Foundations for Data Analytics Engineering

Offers topics and skills designed to prepare students for advanced courses in data analytics engineering. Covers basic concepts and implementation of methods related to probability, eigenvalues and eigenvectors, cluster analysis, text mining, and time series analysis. Offers students an opportunity to learn how to work with modern data structures and apply computational methods for data cleaning and data wrangling operations.

4 credits

Computation and Visualization for Analytics

Offers students an opportunity to learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, manufacturing, and related applications. Covers basics of Python and R for data mining and visualization. Introduces students to static and interactive visualization charts and techniques that reveal information, patterns, interactions, and comparisons by focusing on details such as color encoding, shape selection, spatial layout, and annotation.

4 credits

Data Management for Analytics

Covers the theory and applications of database management to support data analytics, data mining, machine learning, and artificial intelligence. Discusses the fundamental concepts and emerging technologies in database design and modeling, database systems, data storage, and the evolving world of data warehousing and data governance. Presents a balanced theory-practice focus and covers relational databases, NoSQL databases, data integration, data quality, data governance, big data, and data processing for analytics.

4 credits

Data Mining in Engineering

Covers the theory and applications of data mining in engineering. Reviews fundamentals and key concepts of data mining, discusses important data mining techniques, and presents algorithms for implementing these techniques. Specifically covers data mining techniques for data preprocessing, association rule extraction, classification, prediction, clustering, and complex data exploration. Discusses data mining applications in several areas, including manufacturing, healthcare, medicine, business, and other service sectors.

4 credits

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