Online Master of Science in Data Analytics Engineering
Coursera
Get a master's degree in one of the hottest fields—data analytics engineering—from anywhere even without a conventional tech background.
100%
online
18+
Months
8
Courses
$24k
$3,000 per course
About the program
Prepare for a career on the frontier of data analytics, with the online MS in Data Analytics Engineering (DAE) program from the College of Engineering at Northeastern University.
Whether you’re an experienced engineer or brand-new to data analytics, this program will immerse you in specializations such as data visualization, database design and data mining, giving you the skills to bridge the gap between raw data and actionable insights.
Once you’ve mastered essential data analytics techniques such as data normalization and data mapping, and become proficient in multiple programming languages such as R, SQL and Python, you’ll be ready to take on roles like predictive modeling analyst, advanced software engineer, data integration engineer and more, to help shape the evolution of data as a transformative force in modern society.
Designed with 100% online flexibility for working professionals.
Skills
- Data storytelling using Python 70%
- Industrial engineering 60%
- Database design 80%
- SQL 50%
- Data mining 70%
Skip the application—let your performance pave your way
With the DAE program’s fast app pathway, you can skip the application process, and use your performance on two initial courses to secure your place in the DAE program.
Complete two performance-based courses from the DAE program, get a ‘B’ or better, show proof of your bachelor's degree by the time you complete the two courses, and you’ll join the next cohort on the master’s degree. All credits earned in the DAE performance-based courses apply directly to the DAE degree program.
View the pathway courses for the upcoming term.
How to prepare
Although there aren't any prerequisites for the DAE fast app performance-based pathway, you'll need to be familiar with Python Programming Proficiency, Fundamentals of Probability Theory, and Linear Algebra in order to succeed in the DAE performance-based courses.
We recommend reviewing the following preparatory courses to fill in any gaps in skills and knowledge before enrolling in the DAE performance-based courses. This series of courses has been hand chosen by College of Engineering faculty and is designed to equip you with skills and knowledge to enhance your success in the PBA pathway courses.
You can also apply to the program using the standard graduate application for direct admission consideration. Learn more about the standard application requirements at the College of Engineering graduate admissions.
Current Fast App dates
Term |
Submit Fast App between |
Enroll by |
Start course(s) |
| Spring 2025 | August 22 - December 15 | December 20, 2024 | January 6, 2025 |
| Summer 2025 | December 18 - April 15 | April 28, 2025 | May 5, 2025 |
Missed an enrollment deadline? Use the enrollment link in your email to enroll in the current term.
Fill out the fast app to begin your MS in Data Analytics Engineering degree today.
Spring 2025 pathway courses
Take a look at the fast app performance-based pathway courses starting September 2024. Use the course outlines to start preparing in advance of the courses starting.
IE 6400: 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.
IE 6700: Data Management for Analytics
This course covers the theory and applications of database management to support data analytics, data
mining, machine learning, and artificial intelligence. This course introduces the fundamental concepts and
emerging technologies in database design and modeling, database systems, data storage, and data
governance. It presents a balanced theory-practice focus and covers the entity-relationship and UML models, relational models and databases, Structured Query Language, two flavors of NoSQL databases in MongoDB and Neo4j graph database, and a comprehensive introduction to big data management.
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