INFO6105 (Data Science Engineering Methods and Tools) course reviews and ratings at Northeastern (NEU). Average rating 4.3/5. Compare professors with RateMyProfessor reviews.
Introduces the fundamental techniques for machine learning and data science engineering. Discusses a variety of machine learning algorithms, along with examples of their implementation, evaluation, and best practices. Lays the foundation of how learning models are derived from complex data pipelines, both algorithmically and practically. Topics include supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks, deep learning) and unsupervised learning (clustering, dimensionality reduction, recommender systems). Based on numerous real-world case studies.
Data updated October 2026.