Master's in Data Science Online

Northwestern University

Issuing School School of Professional Studies
Description from the School

The integration of data science and business strategy has created a demand for professionals who can make data-driven decisions that propel their organizations forward. You can build the essential analysis and leadership skills needed for careers in today's data-driven world in Northwestern’s online Master of Science in Data Science program.

MSDS students gain critical skills for succeeding in today's data-intensive world. They learn how to utilize relational and document database systems and analytics software built upon open-source systems such as R, Python, and TensorFlow. They learn how to make trustworthy predictions using traditional statistics and machine learning methods.

Choose a general data science track or one of four specializations: Analytics and Modeling, Artificial Intelligence, Data Engineering, or Analytics Management. The specializations are designed to foster individual career growth based on your professional goals. You can further customize your studies with a wide range of elective courses, including financial and risk analytics, artificial intelligence and deep learning, analytics systems analysis, and information retrieval and real-time analytics.

In-State Cost $56,100
Out-of-State Cost $56,100
Delivery Model Fully Online
Completion Time 12 - 36 months
Diploma Different? No
Application Deadline 1 Sep 20, 2021
Application Deadline 2 Sep 20, 2021
Alumni Companies
Exam Required No Info
Transfer Available No
Credits Needed to Complete 36
College Funded Aid No
Admission Requirements

12 courses required.


MSDS 400-DL Math for Data Scientists MSDS 401-DL Applied Statistics with R MSDS 402-DL Introduction to Data Science OR MSDS 403 Data Science in Practice MSDS 420-DL Database Systems and Data Preparation MSDS 422-DL Practical Machine Learning MSDS 460-DL Decision Analytics MSDS 475-DL Project Management OR 480-DL Business Leadership and Communications


MSDS 410-DL Data Modeling for Supervised Learning MSDS 411-DL Data Modeling for Unsupervised Learning MSDS 413-DL Time Series Analysis and Forecasting MSDS 430-DL Python for Data Science MSDS 432-DL Foundations of Data Engineering MSDS 434-DL Analytics Application Engineering MSDS 436-DL Analytics Systems Engineering MSDS 440-DL Application Engineering for Real-Time Analytics MSDS 450-DL Marketing Analytics MSDS 451-DL Financial and Risk Analytics MSDS 452-DL Web and Network Data Science MSDS 453-DL Natural Language Processing MSDS 454-DL Advanced Modeling Techniques MSDS 455-DL Data Visualization MSDS 456-DL Sports Performance Analytics MSDS 457-DL Sports Management Analytics MSDS 458-DL Artificial Intelligence and Deep Learning MSDS 459-DL Knowledge Engineering MSDS 462-DL Computer Vision MSDS 464-DL Intelligent Systems and Robotics MSDS 470-DL Analytics Entrepreneurship MSDS 472-DL Analytics Consulting MSDS 474-DL Accounting and Finance for Analytics Managers MSDS 490-DL Special Topics in Data Science MSDS 491-DL Special Topics MSDS 499-DL Independent Study

Analytics and Modeling Specialization

In the world of data science, the analysts and modelers specialize in testing real-world predictions about data. Data analysts and modelers conduct research and take complex factors into account to build predictive models and create forecasts upon which data-driven decisions can be made. With a focus on traditional methods of applied statistics, this specialization prepares data scientists to utilize algorithms for predictive modeling and analytics, developing models for marketing, finance, and other business applications.


MSDS 410-DL Data Modeling for Supervised Learning MSDS 411-DL Data Modeling for Unsupervised Learning

Analytics Management Specialization

As the strategic and tactical decisions of organizations become increasingly data-driven, analytics managers bridge the work of analysts and modelers with business operations and strategy to lead data science teams, address future business needs, identify business opportunities, and translate the work of data scientists into language that business management understands. This specialization equips data scientists with the communication and management strategies needed to be data-driven leaders who utilize models, analyses, and statistical data to improve business performance.

Note: Students in this specialization are required to take both MSDS 475-DL and MSDS 480-DL to complete the program. Whichever one was not taken to fulfill the core requirement should be taken to fulfill the specialization requirement.


MSDS 474-DL Accounting and Finance for Analytics Managers MSDS 475-DL Project Management or MSDS 480-DL Business Leadership and Communications

Artificial Intelligence Specialization

Advances in machine learning algorithms, growth in computer processing power, and access to large volumes of data make artificial intelligence possible. Recent advances flow from the development of deep learning methods, which are neural networks with many hidden layers. Artificial intelligence builds on machine learning, with computer programs performing many tasks formerly associated with human intelligence. Students in this specialization learn how to move from the traditional models of applied statistics to contemporary data-adaptive models employing machine learning. Students learn how to implement solutions in computer vision, natural language processing, and software robotics.


MSDS 453-DL Natural Language Processing MSDS 458-DL Artificial Intelligence and Deep Learning

Data Engineering Specialization

After analysts and modelers have built and tested models, data engineers implement models to scale within an information infrastructure, creating systems and workflows to organize and manage large quantities of data. This means understanding computer systems (including software, hardware, data collection, and data processes) and solving problems related to data collection, security, and organization. This specialization trains data scientists to utilize system-wide problem-solving skills, choose hardware systems, and build software systems for implementing models made by data analysts to scale in productions systems.


MSDS 432-DL Foundations of Data Engineering MSDS 434-DL Analytics Application Engineering

General Data Science Track

Students seeking a less prescriptive curriculum may tailor elective coursework to their personal and professional needs. This generalist track is particularly useful for data scientists seeking employment with small businesses and smaller-scale projects, in which a single data scientist might have to serve as data analyst, data engineer, and analytics manager. Instead of two required courses and two electives, students choosing the general data science track (no specialization) are able to take four electives.


Choose four electives from above.

About the Final Project

As their final course in the program , students take either a master's thesis project in an independent study format or a classroom final project class in which students integrate the knowledge they have gained in the core curriculum in a team project approved by the instructor. In both cases, students are guided by faculty in exploring the body of knowledge of data science. The master’s thesis or capstone class project count as one unit of credit.


MSDS 498-DL Capstone Project MSDS 590-DL Thesis Research

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