The evolution of data science skill sets: An analysis using exponential family embeddings


Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity among words and phrases in a corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.

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