Core of Data Science and Open Source Resources
summary
Outlines a four-stage path: foundation (probability, linear and multivariable calculus, matrix algebra, R and Python hands-on), core (data mining, supervised 80–90% predictive analytics focus, SQL, Tableau, Jupyter notebooks), main (deep learning, algorithms, time series, optimization with Bayesian and time series demos), and advanced (reinforcement learning, CV, NLP, robotics). Recommends specific free courses (Gilbert Strang, Andrew Ng, David Silver, etc.), cites Hadoop/Spark/Kafka and MapReduce for scale, and notes recorded course materials and notebooks are available on YouTube.
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chapters (10)
- 0:00Technical setup
- 2:20Introduction and contest
- 4:33Data science roadmap
- 6:28Core foundations
- 10:06Data mining and analytics
- 13:08Deep learning and algorithms
- 16:19Advanced topics
- 17:43Open source resources
- 29:22Soft skills and projects
- 32:28Ethics and closing
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