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SF Data Weekly - Charlatans, SPICE and Open Source

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This week's pick will help you identify, and avoid, data charlatans. There's also some event-driven
 
October 16 · Issue #159 · View online
SF Data Weekly
This week’s pick will help you identify, and avoid, data charlatans. There’s also some event-driven SPICE, and a couple of open-source projects from Lyft and Uber. Stay healthy!

Our Pick
How to spot a data charlatan. Tips for identifying fakers and… | by Cassie Kozyrkov | Oct, 2020 | Towards Data Science
Data Pipelines
Event-driven refresh of SPICE datasets in Amazon QuickSight | Amazon Web Services
DASK: A Guide to Process Large Datasets using Parallelization | by Paras Varshney | Towards AI — Multidisciplinary Science Journal | Oct, 2020 | Medium
Data Storage
Building high-quality benchmark tests for Redshift using open-source tools: Best practices | Amazon Web Services
Data Analysis
Exploratory Data Analysis (EDA) — Hands-on NYC Airbnb Dataset | by Rajvi Shah | Towards AI — Multidisciplinary Science Journal | Oct, 2020 | Medium
New in Datalore: Sidebar, Startup Time Improvement, Stack Trace Navigation, Tensorflow Code Insight, and Two New tutorials – Datalore Blog | JetBrains
Data Visualization
COVID-19 and the Economy: insights from a cross-country view | by Kenza Bouhaj | Oct, 2020 | Noteworthy - The Journal Blog
Streamlit vs. Dash vs. Shiny vs. Voila vs. Flask vs. Jupyter | by Markus Schmitt | Oct, 2020 | Towards Data Science
Data-driven Products
Improving Deep Learning for Ranking Stays at Airbnb | by Malay Haldar | Airbnb Engineering & Data Science | Oct, 2020 | Medium
Amundsen: one year later. On October 30, 2019, we officially… | by Tao Feng | Oct, 2020 | Lyft Engineering
Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform | by Jesus Rodriguez | DataSeries | Oct, 2020 | Medium
Data Engineering Jobs
Sponsors

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