Learnings

<p>The complexities of data visualization literacy </p>

<p>Guesses vs. Data as Basis for Design Recommendations </p>

<p>Data means nothing if you don't know about usefulness </p>

<p>Misunderstanding Regression to the Mean: </p>

<p>The data model behind Notion's flexibility: </p>

<p>Practical SQL for Data Analysis: </p>

<p>Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics. </p>

<p>Emerging Architectures for Modern Data Infrastructure: </p>

<p>Designing User-Friendly Data Tables </p>

<p>How to use Benford's Law as a tool to determine the natural occurrence of numbers in a dataset </p>

<p>The New Business of AI (and How It’s Different From Traditional Software): </p>

<p>Continuous Delivery for Machine Learning: Automating the end-to-end lifecycle of Machine Learning applications </p>

<p>Probably the biggest dataset of Names, worldwide: </p>

<p>GDPR Compliance &amp; The Elastic Stack: </p>

<p>Great blogpost about data anonymization: </p>

<p>What does it mean Statistical Significance? </p>

<p>Bloom Filter Calculator: </p>

<p>2019 Ecommerce KPI Study </p>

<p>Fast, Flexible, Easy and Intuitive: How to Speed Up Your Pandas Projects </p>

<p>Amazing article on what Machine Learning means for tech and general companies. A must read! </p>

<p>glom - useful Python library for extracting and transforming nested data: </p>

<p>Always export and import CSV data specifying the dialect. I've just lost an hour with a bug. The lib I used for export was using the excel dialect and the lib I used for import was wrongly guessing some other dialect (because it reads only part of the CSV for guessing)</p>

<p>A curated list of awesome data visualization libraries and resources </p>

<p>Python Graph Gallery: </p>