With the proliferation of data across all sectors of the global economy, there is an immediate need for individuals to be knowledgeable in how to harness this data for continuous analysis and study. This spectrum spans from commercial to nonprofit, from higher education to government, and is constantly expanding with new sectors as data mining becomes the standard for knowledge gathering in the digital age.
The Master of Professional Studies in Analytics helps to meet the demand from employers with a graduate program that provides students with an end-to-end analytics education through a core curriculum with integrated experiential learning opportunities. The program is designed to prepare students with a deep understanding of the mechanics of working with data (i.e., its collection, modeling, and structuring), along with the capacity to identify and communicate data-driven insights that ultimately influence decisions.
Not only will students graduate with a portfolio of work samples that demonstrate their range and depth of skill, they will be part of a larger network of analytics professionals who will serve now and in the future.
The program offers students opportunities to:
- Build portfolios of real-world projects demonstrating competency with key technologies, visualization and communication techniques, and the ability to translate information into recommended actions.
- Gain a core analytical skill set upon which to layer more specialized technical skill sets or industry-specific applications.
- Develop a relationship to industry leaders and peers to leverage the Northeastern University education long after the formal education ends.
- Choose from a host of flexible programming options—all of which share an industry-defined core curriculum and a required, credit-bearing experiential requirement.
- Anticipate and contribute to the future direction of data analytics.
Complete all courses and requirements listed below unless otherwise indicated.
Required Courses
Course List | Code | Title | Hours |
| ALY 6105 | Introduction to Applied Statistics | 3 |
| ALY 6115 | Foundations of Data Analytics | 3 |
| ALY 6125 | Intermediate Analytics | 3 |
| ALY 6135 | Introduction to Enterprise Analytics | 3 |
| ALY 6145 | Communication and Visualization with Data | 3 |
| 4 |
| Integrated Experiential Learning | |
| Healthcare Analytics: From Data to Decisions | |
| Introduction to Queries | |
| Machine Learning Operations | |
| Text Analytics and Language Processing | |
| Topics | |
| ALY 6980 | Capstone | 3 |
Concentrations or Electives Option
A concentration is not required. Students may complete the electives option in lieu of a concentration.
Program Credit/GPA Requirements
34 total semester hours required
Minimum 3.000 GPA required
Applied Machine Intelligence Concentration
Course List | Code | Title | Hours |
| AAI 6600 | Applied Artificial Intelligence | 3 |
| AAI 6620 | Applied Natural Language Processing | 3 |
| AAI 6630 | Applied Computer Vision | 3 |
| 3 |
| Applied Deep Learning | |
| Recommender System | |
| AI for Cybersecurity | |
Statistical Modeling Concentration
Course List | Code | Title | Hours |
| ALY 6600 | Data Warehousing and Querying | 3 |
| ALY 6610 | Data Mining Applications | 3 |
| ALY 6620 | Big Data and Cloud Computing | 3 |
| ALY 6630 | Programming for Data Science | 3 |
Electives Option
Course List | Code | Title | Hours |
| 12 |
Optional Experiential Learning Opportunities
The College of Professional Studies encourages its students to incorporate optional experiential learning opportunities in their academic plans.
Course List | Code | Title | Hours |
| |
| INT 6200 | Experiential Project Preparation | 1 |
| COP 6945 | Co-op Work Experience—Full Time | 0 |
| or COP 6946 | Global Co-op Work Experience—Full Time |
| or COP 6954 | Co-op Work Experience - Half-Time |
| |
| INT 6943 | Integrative Experiential Learning | 3 |
