Data Science at The New York Times – Data Science Blog by Domino

12 bookmarks. First posted by ra88it 7 days ago.

In the Rev session, “Data Science at The New York Times”, Chris Wiggins provided insights into how the Data Science group at The New York Times helped the newsroom and business be economically strong by developing and deploying ML solutions. Wiggins advised that data scientists ingest business problems, re-frame them as ML tasks, execute on the ML tasks, and then clearly and concisely communicate the results back to the organization. He advocated that an impactful ML solution does not end with Google Slides but becomes “a working API that is hosted or a GUI or some piece of working code that people can put to work”. Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. The data science group, in particular, includes people from a “wide variety of intellectual trainings” including cognitive science, physics, finance, applied math, and more. Wiggins closed the session with indicating how he looks forward to hiring from even more diverse job applications.

A few highlights from the session include

Defining the data scientist mindset and toolset within historical context
Seeing data science as a craft where data scientists apply ML to a real world problem
The importance of data scientists having analytical technical skills coupled with the ability to clearly and concisely communicate with non-technical stakeholders.
Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively.
Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.
For more insights from this session, watch the video or read through the transcript.
nytimes  datascience  machinelearning  DataJournalism 
20 hours ago by cnk
RT sungchi : 뉴욕타임즈의 데이터 사이언스: “데이터 과학자는 비즈니스 문제를 파악해 머신러닝 작업으로 재구성하고 결과를 명확하고 간결하게 조직에 전달해야한다. 그것은 구글 슬라이드로 끝나는 게 아니라 사람들이 바로 쓸 수 있는 GUI나 API로 제공해야한다" 잘나갈 수 밖에 없네 July 11, 2019 at 06:17PM
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6 days ago by seoulrain
Data Science at The New York Times – Data Science Blog by Domino
datascience  MachineLearning  from twitter
6 days ago by aratob