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July 1 · Issue #246 · View online |
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Our pick this week is a piece by Google’s Chief Decision Scientist, elaborating the difference between professional and amateur analysts. We also have a post on using GPU-driven data pipelines, and one on whether vertical line charts are ever worth using. Stay healthy!
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10 Differences Between Amateurs and Professional Analysts | by Cassie Kozyrkov | Jun, 2022 | Towards Data Science
Analytics is like the writing profession: the basics are easy to get started with and it’s an art, so there are few barriers to entry and anyone can call themselves a “writer.“ How to become a "real” data analyst.
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GPU-Accelerated Data Loading With DALI, Part 2: Pipelines and Data Loaders | by Borna Ahmadzadeh | Jun, 2022 | DataDrivenInvestor
DALI is a Python library that interfaces with Nvidia GPUs to accelerate data processing. This piece describes how to use it. Here’s Part 1 explaining the theory behind it.
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11 API Trends to Watch for in 2022 | DreamFactory Software
As the world undergoes a digital transformation, APIs are becoming increasingly important. As a business owner, staying up-to-date on the latest API trends is essential. [Sponsored]
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Create cross-account, custom Amazon Managed Grafana dashboards for Amazon Redshift | Amazon Web Services
Grafana is a open source analytics and visualization platform. This piece is a step-by-step showing how to visualize Redshift performance using Grafana.
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GitHub - dolthub/dolt: Dolt – It's Git for Data
Dolt is a SQL database that you can fork, clone, branch, merge, push and pull just like a git repository.
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Some Of The Most Important SQL Commands | Towards Data Science
Five advanced SQL concepts worth knowing.
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Crash-Course: P-Value, Statistical Significance, and Confidence Level | by Stefan Pircalabu | Jun, 2022 | Medium
Key statistical concepts for the data analyst or data scientist.
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Are Vertical Line Charts Ever a Good Idea? | Nightingale
How the author’s mind was changed about the value of vertical line charts after asking his followers on Twitter to give him good examples.
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The Battle of Choropleths — Part 3 — Folium | by Francis Adrian Viernes | Jun, 2022 | Towards Data Science
After looking at Geopandas and Altair, this series turns to Folium for creating Choropleths.
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T-LEAF: Taxonomy Learning and EvaluAtion Framework | by Cen(Mia) Zhao | The Airbnb Tech Blog | Jun, 2022 | Medium
How Airbnb applied qualitative learning, human labeling and machine learning to iteratively develop Airbnb’s Community Support Taxonomy.
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Causal Forecasting at Lyft (Part 1) | by DJ Rich | Jun, 2022 | Lyft Engineering
How the Lyft data science team helps to provide meaningful financial incentives to Lyft drivers.
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