Resources
Student resources for applied mathematics, data science, and machine learning
This page collects resources for students preparing for careers in data science, machine learning, artificial intelligence, applied mathematics, statistics, and operations research. The materials include programming support, mathematical background, software tools, career preparation, and Northeastern student support links.
Featured posts and teaching resources
Teaching with AI
A teaching showcase on using ChatGPT to support coding in applied mathematics, with emphasis on mathematical reasoning, programming logic, debugging, and responsible AI use.
GitHub simulation with AI
Interactive simulations and examples designed to help students connect applied mathematics, computation, and AI-assisted learning.
Teaching page
A list of courses taught at Northeastern University, including graduate and undergraduate courses in applied mathematics, machine learning, probability, statistics, and linear algebra.
Course support
Knowledge topics
Online resources summary
A general collection of online learning resources, including project-based programming resources.
Linear algebra
Resources for reviewing and strengthening linear algebra, especially for applied mathematics, data science, machine learning, and numerical computation.
Machine learning and statistical learning
Basic machine learning and statistical learning resources for students preparing for applied projects and technical interviews.
Deep learning
Books and resources for neural networks and deep learning, including online books such as Dive into Deep Learning and related references.
Computer vision
Resources for students interested in image data, convolutional neural networks, medical imaging, segmentation, and visual recognition.
Natural language processing
Resources for students interested in text data, language models, embeddings, transformers, and modern NLP applications.
Time series
Resources for students interested in stochastic processes, forecasting, sequential data, ARMA-type models, state-space models, and machine learning for time-dependent data.
Data science career preparation
Data science skills
A guide for students preparing for data analyst, data scientist, machine learning engineer, AI engineer, and related roles. Topics include mathematical knowledge, programming skills, communication skills, presentation skills, project building, GitHub, Kaggle, LinkedIn, and lifelong learning.
Resources for MS students at Northeastern
A collection of university support links, including College of Science graduate resources, registrar links, academic calendar, catalog, forms, library, Office of Global Services, Writing Center, and Global Student Success.
Suggested student checklist
- Build a strong foundation in linear algebra, probability, statistics, optimization, and machine learning.
- Practice Python, R, MATLAB, SQL, GitHub, and data visualization through real projects.
- Prepare a professional GitHub, LinkedIn profile, and personal website.
- Join seminars, workshops, data clubs, research discussions, and project opportunities.
- Use AI tools carefully: verify results, understand the mathematics, and explain your reasoning clearly.
Useful software and websites
Programming and computation
Mathematical software
AI tools
Mathematics and research websites
My academic profiles
Contact
Email: he.wang@northeastern.edu
Office: NI 550
Office phone: (+1) 617-373-5674
Mailing address: 567 Lake Hall, Department of Mathematics, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA