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In other words, using metadata about datascience work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in datascience work is concentrated. The approach they’ve used applies to other popular datascience APIs such as NumPy , Tensorflow , and so on.
To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist, ETL or developer. Advanced Data Discovery ensures data democratization by enabling users to drastically reduce the time and cost of analysis and experimentation.
I spent the majority of my time helping clients decide which was the right Hadoop platform and which NoSQL / nonrelational data store to pick for specific use cases. Fast forward to early 2017. Then in the middle of 2017, a realization set in that we were one year away from GDPR and needed to focus on data governance.
I’ve been working remotely with Automattic since 2017, so I was pretty covid-ready as far as work was concerned. My main "day job" focus in 2020 was on being the tech lead for Automattic’s new experimentation platform (ExPlat). Remote work. Technical work.
Reinforcement learning fell by 5% in 2019; it’s up hugely—1,500+%—since 2017, however. Aggregating artificial intelligence and machine learning topics accounts for nearly 5% of all usage activity on the platform, a touch less than, and growing 50% faster than, the well-established “datascience” topic (see Figure 2).
The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for datascience work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”.
Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of datascience, streaming, and machine learning (ML) as disruptive phenomena. 1 again in proposals this year.
In Paco Nathan ‘s latest column, he explores the role of curiosity in datascience work as well as Rev 2 , an upcoming summit for datascience leaders. Welcome back to our monthly series about datascience. and dig into details about where science meets rhetoric in datascience.
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