arrow_back Design for Data
The power of intuition in data science, and why it will always have a role
Submitted by Avi Patchava (@avipatch) on Wednesday, 4 July 2018
Section: Full talk Technical level: Beginner
Data science, fueled by big and growing datasets, has enabled the rapid discovery of new relationships and predictability in the world. If the algorithm can find the relationships backed by mountains of historical data, why the role of intuition? This seems counter to the purpose and modus operandi of data science. This talk will explain why intuition remains vital to Data science: 1) What it is; 2) How it combines with algorithms; 3) Examples of its impact across data science use cases; 4) Our 7 tip on how to develop it.
- Context setting: why does a subject driven by data need the notion of intuition? Isn’t intuition akin to black magic?
- What is intuition: perspectives and definitions
- Where does intuition come from? What is the science behind it?
- Why is intuition needed even in data science, where we have abundant data
- What does it take to develop intuition as a data scientist? Our 7 tips
- Is intuition the Data scientist’s defence against replacement by machine?
Nothing - will use slides and discuss specific examples
Avi Patchava is Vice-President of Data Sciences, Machine Learning and Artificial Intelligence at InMobi – a leading Indian company in the world of Mobile AdTech. Previously, he was with McKinsey&Co driving large-scale machine learning initiatives in sectors such as Banking, Automotive, and Manufacturing. His background is in economics and the social sciences, with Masters’ degrees from the University of Oxford and the London School of Economics