This lecture introduces the tools and libraries used in Python for data science and engineering. It covers basic concepts such as data processing, feature engineering, data visualization, modeling, and model evaluation. With this lecture, participants will better understand end-to-end data science and engineering with a real-world case study.
About the Speaker:
Boyan Angelov is a CTO and data strategist with a decade of experience in a variety of academic and business environments such as bioinformatics, clinical trials, HRTech, management consulting, and LegalTech. He has published articles in applied machine learning and authored the O’Reilly.
Table of Contents:
00:00 – Introduction
02:26 – What is a Data Product
04:47 – Data Product Requirements
06:54 – Closing the Loop
11:30 – Natural Language Tool Kit Demo
20:27 – Basic System Design
21:54 – Product Development
28:28 – Exercise I
28:56 – Exercise II
30:30 – Resources
31:54 – Closing Remarks
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