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CRISP-DM Methodology for Data Science Projects: Business Understanding, Data Preparation, Modeling, Evaluation & Deployment
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CRISP-DM Methodology for Data Science Projects: Business Understanding, Data Preparation, Modeling, Evaluation & Deployment
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Episode Overview
Learn the CRISP-DM methodology for managing data science projects. Step-by-step guide covering business understanding, data preparation, modeling, evaluation, and deployment
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- 0:00 - Transcript checkpoint 1: I will start with an introduction. Thank you very much for coming to this
- 2:34 - Transcript checkpoint 2: Thanks again for joining. Today we will talk about processes in a machine
- 5:34 - Transcript checkpoint 3: Back then data science was called data mining and things were different, but
- 7:55 - Transcript checkpoint 4: Imagine we have an online classified website where people sell items they
- 10:58 - Transcript checkpoint 5: Measuring how many users cannot finish posting can be tricky. It is not always
- 13:25 - Transcript checkpoint 6: The problem is important, we can measure its size, and we have a way to measure
- 15:46 - Transcript checkpoint 7: If all the needed data is already in the data lake then data engineers do
- 17:05 - Transcript checkpoint 8: If the baseline accuracy is sufficient we can move to the evaluation step.
- 18:23 - Transcript checkpoint 9: When making these decisions we always keep the business objective in mind.
- 19:25 - Transcript checkpoint 10: Crisp DM may not explicitly highlight data collection, but it is part of the
- 33:04 - Transcript checkpoint 11: If you want to ask a question, go to Slido.com or use the QR code and enter
- 36:03 - Transcript checkpoint 12: See you, and thanks for attending. Goodbye.