Startup Pivoting

This project aims to use data science with historical version of startup websites to identify when do they pivot to new strategies.

Firm strategies—what they choose to to do or not to do, and why—represent the main way in which firms shape the economy. In a time of widely encompassing platforms, corporate-led crypto currencies, activist CEOs, and socially-oriented corporations, characterizing how firms differ in their strategies, and in the choices they take, appears as important as ever. There is a need for tools to measure firm strategy.

As a student scholar, your role in this position would be to work in the nascent Measuring Strategy Lab, using natural language processing methods to devise new ways to understand and measure firm strategy. Specifically, using a large sample of startup websites downloaded through the Wayback Machine, develop systematic ways to understand when a startup is changing their strategy and why, and how does this predict their performance. This work builds also on the Startup Cartography Project, and is part of the ongoing efforts of bringing data science into strategy research.

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The Value of Data

Many scholars and policymakers view establishing functioning data markets as essential for the digital economy to bring prosperity and stability to society at large. A key challenge is to determine the value of an individual’s specific data. Is one buyer’s data more valuable than another’s for an e-commerce platform? How much should each be paid?

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This study is the first step in exploring an emerging and previously understudied data stream - verbal communication between healthcare providers and patients. In partnership between Columbia Engineering, School of Nursing, Amazon, and the largest home healthcare agency in the US, the study will investigate how to use audio-recorded routine communications between patients and nurses to help identify patients at risk of hospitalization or emergency department visits. The study will combine speech recognition, machine learning and natural language processing to achieve its goals.

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All volcanoes on earth are driven by the degassing of volatile elements, mostly H2O and CO2 from their host magma. To model the degassing process, one needs to know the solubility laws of these volatile. To that end, petrologists have been performing high-pressure high-temperature experiments for sixty years to determine how much water and CO2 dissolves in magma as a function of Pressure, Temperature, Melt composition (12 oxides) and oxidation state. To model how these fifteen parameters affect solubility laws petrologist have then relied on empirical interpolation between experimental data points and some extrapolations using classical thermodynamic theory to infer the expected behavior beyond experimental calibration.

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Columbia Data Science Institute (DSI) Scholars Program

The DSI Scholars Program is to engage and support undergraduate and master students in participating data science related research with Columbia faculty. The program’s unique enrichment activities will foster a learning and collaborative community in data science at Columbia.

Columbia University DSI

New York, NY