MITXpro-DSx-LDA. Case Study 1.1.2: Finding Themes in Project Descriptions Instructor: Tamara Broderick Activity Type: Optional Case Study Description: Using Latent Dirichlet Allocation to discover topics in a corpus of text. Finding Themes in Project Descriptions - LDA Analysis. Self-Help Documentation: In this document, we walk through some tips to help you with doing your own analysis on MIT EECS faculty data using stochastic variational inference on LDA. We provide some examples for the following programming environment: Python. Download Self-Help Documentation Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience. Have questions? Feel free to discuss this case study with other participants in the Discussion Forum under Module 1 - Case Studies Section.

Problem

The full read on this project is a sign-in away — what it solves, who it serves, and where it sits in the market.

Audience

The full read on this project is a sign-in away — what it solves, who it serves, and where it sits in the market.

Market

The full read on this project is a sign-in away — what it solves, who it serves, and where it sits in the market.

Pricing

The full read on this project is a sign-in away — what it solves, who it serves, and where it sits in the market.

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