Our pick this week is a visualization of an apartment hunt in Munich, which includes some really funn
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October 23 · Issue #160 · View online |
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Our pick this week is a visualization of an apartment hunt in Munich, which includes some really funny charts in addition to the serious ones. We also have two interesting pieces on AB testing at Best Buy and Facebook. Stay Healthy!
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Munich: Our apartment hunting pain in numbers and graphs | by Daniel Ko | Oct, 2020 | Medium
Daniel takes the lemons of a 46 day unsuccessful Munich apartment hunt and turns it into the lemonade of some really interesting (and humorous) data analysis and visualization of the Munich rental market.
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Orchestration Frameworks for Big Data | by Javier Ramos | Oct, 2020 | ITNEXT
Javier looks data pipeline orchestration tools like Airflow, Ozzie, Dagster and Prefect, and picks the best use case for each tool.
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Analyzing healthcare FHIR data with Amazon Redshift PartiQL | Amazon Web Services
PartiQL is a SQL-compatible query language that can be used to directly query JSON data. This article is a step-by-step walkthrough that demonstrates the use of PartiQL to query data in the Fast Healthcare Interoperability Resources (FHIR) JSON format directly from Redshift.
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Get started with Amazon Redshift cross-database queries (preview) | Amazon Web Services
Cross-database queries are a new feature with Redshift R3 instances. This article includes examples detailing how to set up permissions and how to connect with tools.
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Meet whale! 🐳 The stupidly simple data discovery tool. | by Robert Yi | Dataframe | Oct, 2020 | Medium
Robert’s open source tool Whale is a simple metadata scraper that crawls your data warehouse and scrapes metadata for querying with a GUI, to allow you to find and understand data without time-consuming manual search.
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5 Data Granularity Mistakes That May Cost You | by Elena Marocco | Towards AI | Oct, 2020 | Medium
Ideal data granularity is critical for accurate and actionable business intelligence. This piece explains ideal granularity and helps you avoid common granularity-based mistakes.
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Loading large datasets in Pandas. Effectively using Chunking and SQL for… | by Parul Pandey | Oct, 2020 | Towards Data Science
The pandas’ library is a vital member of the Data Science ecosystem. However, the fact that it is unable to analyze datasets larger than memory makes it a little tricky for big data. This piece give you two different alternatives to get around this pandas limitation.
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Milestones in the History of Data Visualization | by Edward De Jesus | Oct, 2020 | Medium
A short introduction to data visualization milestones, from prehistoric to modern time.
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Explore Public Datasets with Google BigQuery and DataStudio | by Joe T. Santhanavanich | Oct, 2020 | Towards Data Science
A quick walk through of public data sets available for free use with Google BigQuery and DataStudio, including sample queries and visualizations of the Johns Hopkins’ COVID-19 dataset.
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What To Do When You Can’t AB Test | by Joshua Loong | Oct, 2020 | Towards Data Science
When you can’t do a proper AB test, try counterfactuals. Joshua from Best Buy Canada gives examples of market-based and time-series-based approaches to synthetic AB tests.
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Increasing the sensitivity of A/B tests by utilizing the variance estimates of experimental units - Facebook Research
An overview of Kevin Liou and Sean Taylor’s research into variance-weighting A/B test estimates, where they were able to reduce variance by 17 percent, with only a slight increase in bias.
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My Experience as a Data Scientist vs. a Data Analyst | by Vicky Yu | Oct, 2020 | Towards Data Science
Those of you looking for a data science job might be interested in Vicky’s experience of being hired as a data scientist without any data science experience. Here are this week’s jobs:
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