Issue-number: 267

summary: Overfitting: A Guided Tour. Data project checklist. Learning ML. Optimizing sample sizes in A/B testing. Chess w/ GPT-2. On DS consulting. number: 267 title: Issue 267 url: https://dataelixir.com/issues/267 published_at14.01.20, 19:22

Issue-number: 261

summary: ⚽ Team formation analysis. ML systems design. Quantitative finance notebooks. Cost-benefit analysis. Local-first data. Audio analysis w/ ML. number: 261 title: Issue 261 url: https://dataelixir.com/issues/261 published_at27.11.19, 00:12

Issue-number: 263

summary: Probability Distribution Explorer. Why ML can’t save NFL. Working w/ 100GB+ datasets on a laptop. DS salaries around world. Vega-Lite 4.0. number: 263 title: Issue 263 url: https://dataelixir.com/issues/263 published_at10.12.19, 20:28

Issue-number: 264

summary: Top DS Python libs of 2019. Reusable data workflows for polyglot teams. Machine unlearning. Data valuation. AI Index Report. number: 264 title: Issue 264 url: https://dataelixir.com/issues/264 published_at21.12.19, 07:51

Issue-number: 259

summary: ML product dev. Modern SQL. Scaling a data team. Data discovery. Probablistic scripts. Sci Vis w/ openGL. Bar chart race tutorial. number: 259 title: Issue 259 url: https://dataelixir.com/issues/259 published_at19.11.19, 23:15

Issue-number: 262

summary: End to end Jupyter. Kubernetes intro. ML trading system. Visual intro to BERT. ~Git for data. Curse of expertise. Privacy vs data. number: 262 title: Issue 262 url: https://dataelixir.com/issues/262 published_at03.12.19, 14:53

Issue-number: 265

summary: Practical decision-making w/ causality. SQL for DS. Lacking uncertainty. Urban sensing. Efficient computation w/ R. NLP best practices. number: 265 title: Issue 265 url: https://dataelixir.com/issues/265 published_at31.12.19, 17:29

Issue-number: 260

summary: Key ML/NLP paper summaries. Confident Learning. Data trends in tech. Journalism AI. Better viz for science. number: 260 title: Issue 260 url: https://dataelixir.com/issues/260 published_at19.11.19, 23:38

Issue-number: 266

summary: Intro to autoencoders. Move predictions to your db. ML/NLP research summaries. Modeling salary and gender. Why R? Bayesian inference. Sheets-fu. number: 266 title: Issue 266 url: https://dataelixir.com/issues/266 published_at07.01.20, 19:42

Issue-number: 268

summary: Answers from data you can’t see. Smarter than the market. Cool projects w/ spotifyr. On being self-taught. number: 268 title: Issue 268 url: https://dataelixir.com/issues/268 published_at21.01.20, 21:49