Issue-number: 317

summary: Top Python libraries for DS. ⚽ Analytics 2020 Review. Uncertainty Toolbox. Real-time ML. Medicine’s ML Problem. Dark data. number: 317 title: Issue 317 url: published_at05.01.21, 20:28

Issue-number: 312

summary: Hands-on ML. COVID mobility modeling. Prediction markets vs polls. Julia Notebooks. Colliding worlds of BI and DS. ML for Java. Data discovery at Uber number: 312 title: Issue 312 url: published_at18.11.20, 01:17

Issue-number: 314

summary: ML case studies. Least squares as springs. Ensuring data quality. Open data in 2020. Version control for ML. number: 314 title: Issue 314 url: published_at02.12.20, 02:25

Issue-number: 316

summary: Code reviews for Jupyter. How models leak data. Advanced Data Science 2020. Algorithmic bias: past, present, future. End-to-end ML monitoring. number: 316 title: Issue 316 url: published_at16.12.20, 01:37

Issue-number: 313

summary: 🔥 ML tutorial w/ crypto. ⚽ Analytics 2021. Dynamic data testing. Modeling vaccination strategies. Experimentation w/ resource constraints. number: 313 title: Issue 313 url: published_at24.11.20, 22:33

Issue-number: 318

summary: Real-time ML in practice. Search tool for obscure datasets. Intro to probablistic ML. Python data validation. number: 318 title: Issue 318 url: published_at13.01.21, 03:11

Issue-number: 315

summary: Data quality at scale. Key statistical ideas of past 50 years. Beautiful plotting w/ ggplot2. 2020 top papers. Distill for R Markdown. number: 315 title: Issue 315 url: published_at09.12.20, 03:49

Issue-number: 319

summary: DS as Atomic Habit. Elegant SQL w/ R. Bayesian statistics Primer. Julia update: Python challenger? Best-of Python ML. Notes from NeurIPS 2020. number: 319 title: Issue 319 url: published_at19.01.21, 22:56

Issue-number: 320

summary: Data monitoring at scale. AI for Good: for REAL? Legal questions for DS. Why business intuition > ML. Density plots. Internal tool design. number: 320 title: Issue 320 url: published_at27.01.21, 01:53

Issue-number: 321

summary: Causal design patterns. Why ML is hard to tune. Experimentation guardrails. Python EDA toolkit. COVID-19 modeling lessons. How to cite data sources. number: 321 title: Issue 321 url: published_at03.02.21, 06:05