Use these tips to maximize the success of your data science project
Managing large-scale data science and machine learning projects is challenging because they differ significantly from software engineering. Since we aim to discover patterns in data without explicitly coding them, there is more uncertainty involved, which can lead to various issues such as:
- Stakeholders’ high expectations may go unmet
- Projects can take longer than initially planned
The uncertainty arising from ML projects is major cause of setbacks. And when it comes to large-scale projects — that normally have higher expectations attached to them — these setbacks can be amplified and have catastrophic consequences for organizations and teams.
This blog post was born after my experience managing large-scale data science projects with DareData. I’ve had the opportunity to manage diverse projects across various industries, collaborating with talented teams who’ve contributed to my growth and success along the way — its thanks to them that I could gather these tips and lay them out in writing.
Below are some core principles that have guided me in making many of my projects successful. I hope you find them valuable…
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