Treffer: Data science in chemistry: artificial intelligence, big data, chemometrics and quantum computing with Jupyter De Gruyter graduate./ Thorsten Gressling.

The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity - data science - includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing. ; Introduction -- Technical setup and naming conventions -- 1. Data science: introduction -- 2. Data science: the "fourth paradigm" of science -- 3. Relations to other domains and cheminformatics -- Part A: IT, data science, and AI -- IT basics (cloud, REST, edge) -- 4. Cheminformatics application landscape -- 5. Cloud, fog, and AI runtime environments -- 6. DevOps, DataOps, and MLOps -- 7. High-performance computing (HPC) and cluster -- 8. REST and MQTT -- 9. Edge devices and IoT -- Programming -- 10. Python and other programming languages -- 11. Python standard libraries and Conda -- 12. IDE's and workflows -- 13. Jupyter notebooks -- 14. Working with notebooks and extensions -- 15. Notebooks and Python -- 16. Versioning code and Jupyter notebooks -- 17. Integration of Knime and Excel -- Data engineering -- 18. Big data -- 19. Jupyter and Spark -- 20. Files: structure representations -- 21. Files: other formats -- 22. Data retrieval and processing: ETL -- 23. Data pipelines -- 24. Data ingestion: online data sources -- 25. Designing databases -- 26. Data science workflow and chemical descriptors -- Data science as field of activity -- 27. Community and competitions -- 28. Data science libraries -- 29. Deep learning libraries -- 30. ML model sources and marketplaces -- 31. Model metrics: MLFlow and Ludwig -- Introduction to ML and AI -- 32. First generation (logic and symbols) -- 33. Second generation (shallow models) -- 34. Second generation: regression -- 35. Decision trees -- 36. Second generation: classification -- 37 Second generation: clustering and dimensionality ...