Requirements gathering in Data Science Projects
My talk will cover requirements gathering in data science projects, which can vary widely - from simple ad-hoc queries to complex researches involving different math tasks and, especially, machine learning tools. It will be based on my experience in this area. I will speak about things you need to get from your customer, how to measure work results, and how to enrich data when dataset that you have is not enough to get good results.
Requirements gathering and indeterminacy
While requirements are gathered, it is possible to get into situation where product owners wants very flexible system and can't describe all of the logic. However, this flexibility shouldn't affect fault tolerance and performance, and, at other side, development should be completed at reasonable time. Among other, there is Business Rule approach to build such system. But flexibility has other side - the price of error in rules can lead to significant loss both in terms of performance and service logic.
Where to find a balance between flexibility, performance and fault tolerance? Where requirements gathering should be started? My talk will cover this and some other aspects around building of BRE systems.