Data, Python and Humans
summary
Five concrete challenges for doing data science in Latin America are presented along with five real Quantil case studies showing practical solutions: adopting Python despite low trust, using pandas and Altair to analyze legacy data, deploying pipelines and models with TensorFlow and PyTorch, and cleaning datasets containing millions of files. The talk gives impact numbers for public and private clients, contracts obtained, salary increases, and explains what worked and what failed in each case.
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chapters (16)
- 0:00Introduction
- 0:24If you stay
- 0:57A little about me
- 4:17We are not only learning, but proposing
- 8:30They will often look at you like this
- 8:54They show you this
- 13:58Jupyter and Pandas
- 14:51However, you may realize that
- 15:41With Visualization
- 17:25What visualization is good for
- 19:01Tamara Munzner's framework
- 19:30Something like "user stories"
- 21:20It's a set of skills
- 22:35Possible careers
- 22:51Careers that teach Python (2020)
- 23:54Other paths 300
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