Accredited engineering degree offered by the College of Engineering at Northeastern University. Source
Hassle-free pathways for learners with or without conventional technical experience.
(4 credits at $750/credit) - Pay-as-you-go tuition lets you manage the costs, course by course.
Lecture videos, hands-on projects and connection with instructors and peers, from anywhere.
As one of the first engineering master’s of its kind to offer admission into the program without a prior STEM degree requirement, the DAE addresses the growing need for professionals trained in advanced data analytics.
This program will prepare you with the skills and confidence to shape the future of data-driven decision-making.
Get a sneak peek at the IE 6400 | Foundations for Data Analytics Engineering course.
Summer 2025 Priority Application Deadline: March 28, 2025
10% New Student Scholarship Deadline: March 28, 2025
Enroll for your course(s) and pay the $399 initial deposit by March 28, 2025 to receive the 10% New Student Scholarship applied to your first term start.
Summer 2025 Start of Classes: May 5, 2025
Stay tuned
Access your personalized link to enroll here!
Congratulations—you're officially a Northeastern student! Be on the lookout for important messages from your success manager on next steps.
Summer 2025 Priority Application Deadline: March 28, 2025
10% New Student Scholarship Deadline: March 28, 2025
Enroll for your course(s) and pay the $399 initial deposit by March 28, 2025 to receive the 10% New Student Scholarship applied to your first term start.
Summer 2025 Start of Classes: May 5, 2025
Stay tuned
Access your personalized link to enroll here!
Congratulations—you're officially a Northeastern student! Be on the lookout for important messages from your success manager on next steps.
Want to learn more about the degree before you commit? Enrol in one of the University of Leeds' open courses to get a preview of the topics, materials and instructors in the MSc Data Science (Statistics) programme.
Programming for Data Science: Explore the basics of programming and familiarise yourself with Python.
Exploratory Data Analysis: Learn how to analyse and investigate data sets and explore ways to visualise data.
Statistical Methods: Understand the role of statistics in data analysis and gain experience using RStudio for creating numerical and graphical summaries.
This course will introduce students to basic techniques, which can be used to perform a preliminary investigation of data sets. Exploring data involves visualising the variables and relationships to help determine outliers, identify trends, suggest suitable statistical models and inform future data gathering.
On completion of this module students should be able to:
The module provides a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.
Indicative content for this module includes:
This module introduces the fundamental skills of programming in python. The aim is for students to develop the skills and experience to independently translate a broad range of data science related problems into functioning computer programs and communicate the results.
Indicative content for this module includes:
Students will undertake a sequence of programming exercises starting with the fundamentals of programming and building up to a system that performs significant data analysis on real data:
The objective of this course is to equip students with the skills necessary to undertake project work as a data scientist. Project planning, reviewing existing methodologies and the presentation of outputs in different forms all form part of this. This module will also include ethical considerations of data usage.
On completion of this module students will be able to:
Machine learning is a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. In this module you will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. You will learn how the different approaches relate to and are motivated by statistics and will gain practical experience in the application of these techniques on real and simulated datasets.
Indicative content for this module includes:
In big data with multiple variables, it is vital to discover pattern and infer valuable information from the data. This module introduces basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
On completion of this module students should be able to:
This course will equip students with understanding of the theory of linear models and be able to fit multiple linear regression models to data and interpret the results. The content will develop an appreciation of the limitations of linear models and the use of link functions to generalise the linear regression model. In particular, the module will explore logistic regression and log linear models.
On completion of this module students should be able to:
Indicative content for this module includes:
This course introduces key concepts and techniques in statistical learning which are relevant to a number of practical applications. These techniques include statistical machine learning for classification and regression.
On completion of this module students should be able to:
Data scientists work in a wide range of fields of application. This module gives an insight into some general principles of the work of a data scientist and some of the underpinnings of artificial intelligence and statistics in the practice of data science.
Indicative content for this module includes:
The module aims to equip students with the ability to apply standard methods for random number generation and apply different Monte Carlo methods and develop understanding of the principles and methods of stochastic simulation. The module will also instruct students on how to implement statistical algorithms for a given problem and develop familiarity with software for advanced statistical computing.
On completion of this module students should be able to:
Indicative content for this module includes:
The objective of this course is to introduce Bayesian statistical methods through the consideration of philosophical differences with traditional statistical procedures and the application of Bayesian techniques. This module also introduces the ideas of quantitative decision theory and rational decision making.
On completion of this module students should be able to:
The objective of this course is for students to plan, carry out and present the results of a short project in data science. The project will be presented in a professional format that could serve as an exemplar of their work for a future employer or client.
On completion of this module students will be able to:
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