Introduction
How does one execute a data science project? What are the tried and tested methods to get it done? how does one do it most effectively? That and more is what the article linked here to your talks about.
Summary of Article
The article talks about the concept of the phases of data science maturity the Journey of building impactful data science practices consisting of different phases. Three of them in particular. Winning and planning, scaling the data science discipline, and growing yourself. The article further elaborates on the scaling aspect when it says that involves building team infrastructure and culture which ensure that your efforts are being multiplied to an output that’s exponentially greater than what you could have done yourself. Then the article moves on to a human-centered design approach identifying the right data science projects requires empathy and knowledge and that’s where that humanistic side of it comes into that empathy. Then the article gets into the actual deliverables of delivering iteratively and the MVP mindset. The article stresses that you should always go with the least possible thing that you can do and then get feedback to build from there and then you always keep iterating every single time to get better and better and better and better.
My Take
Overall I like this article it’s focus on Delivering iteratively and the minimum viable product mindset. This mindset in data science projects and business overall I think will become useful in the future for me. I also really like the author’s writing style as it’s very engaging and kept me up through the whole article. I also like the Articles use of visualizations and Graphics to break up the tax and make it into more manageable chunks to read and process information.
Conclusion
All in all this article was a good one and it’s focused on the minimum viable product mindset as well as delivering iteratively I’m sure will help me in the future both in data science and business in general. I highly recommend you give it a read when you have some time with the link here.