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Thursday 1:20 p.m.–4:40 p.m.

Hands-on with Pydata: how to build a minimal recommendation engine.

Diego Maniloff, Christian Fricke, Zach Howard

Audience level:


In this tutorial we'll set ourselves the goal of building a minimal recommendation engine, and in the process learn about Python's excellent Pydata and related projects and tools: NumPy, pandas, and the IPython Notebook.


A recommendation engine is a software system that analyzes large amounts of transactional data and distills personal profiles to present its users with relevant products/information/content. We see them in a wide variety of domains and applications and they help us navigate the overwhelming choice that we face everyday. This tutorial will formally introduce the concepts and definitions of the recommendation systems literature and will quickly move on to an iterative process for building a minimal reco engine. In the process, we'll learn about the building blocks for scientific computing in Python: NumPy and (more recently) pandas.

Student Handout

No handouts have been provided yet for this tutorial

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