Leading Data Science Teams: A Framework To Help Guide Data Science Project Managers - Jeffrey Saltz
Ғылым және технология
Data science managers (and senior leaders managing data science teams) need to think through many questions relating to how to best execute their data science efforts. For example, what is the most effective way to lead a data science project?
How to make sure my data science team does not expose my organization to issues relating to the misuse of data and/or algorithms?
How do I validate the results provided by the data science team?
This video will provide a framework managers can use to help ensure a successful data science project. The focus of this framework is not on which specific algorithm a team should use, but rather, how to ensure that the data science effort is progressing effectively and efficiently.
Key aspects of the framework, that will be discussed, include:
1. Forming Data Science Teams
2. Establishing Processes for Developing Analytical Solutions
3. Risk Management
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Пікірлер: 8
Hi Jeffrey, This video has been really awesome and something which I have personally experienced while implementing the Projects related to Machine Learning. It gives me some good ideas which can be adopted or experimented in the Project Management for Data Analytics to get the best out of it. Thanks once again.
@jeffsaltz6769
3 жыл бұрын
Very much appreciate the positive feedback! For more information (and additional training), check out www.datascienceprocess.com
I’ve worked as a data analyst/scientist in an agile framework for a while, and i had the exact issues that you mentioned. Thanks for the insights!
Thank you for the video, I hope to start a research company and this is me starting the research.
Very Informative talk, thank you
Great content, any idea on how to get these slides?
thank you so much for sharing the talk it was really insightful. I'm a data scientist with three years of experience at an AI startup. I've worked in a small team and gained skills in data analytics, data engineering, data pipeline architecture, model training workflows, reproducibility, and process improvement. Now, I'm interested in leading new data projects and teams. Do the age and number of years of experience matter for such role?
Rigor and reproducibility is huge. This is going to be the next frontier....