11-637 Foundations of Computational Data Science

12 units, Carnegie Mellon University

This course provides an introduction to foundational concepts, learning material, and projects related to the three core areas of Data Science: Computing Systems, Analytics, and Human-Centered Data Science. Students completing this class will be prepared for further graduate education in Data Science and/or Artificial Intelligence. Students acquire skills in solution design (e.g., architecture, framework APIs, cloud computing), analytic algorithms (e.g., classification, clustering, ranking, prediction), interactive analysis (Jupyter Notebook), applications to data science domains (e.g., Natural Language Processing, Computer Vision) and visualization techniques for data analysis, solution optimization, and performance measurement on real-world tasks. Technologies used in this course include: Python, Pandas, Numpy, Scikit Learn, Pandas, PyTorch, JupyterLabs / Jupyter Notebook, Spacy, nltk, sentence-transformers, Azure ML Deployment, Beautiful Soup 4, selenium, matplotlib or seaborn, tqdm, gensim, scipy ( sparse and linear packages ), various packages in the Python standard library ( collections, requests, datetime, re, and a couple of others ), and pdfminer.

Prerequisites: none listed

Offered: Spring 2027, Fall 2026, Summer 2026, Spring 2026, Fall 2025

Courseweave needs JavaScript to show sections, workloads and planning tools.