I am sharing my my keynote presentation at Data Science Conclave 2017 in Chennai. Thanks Rajesh for the invite.
1. Major improvements in accuracy in speech recognition and image recognition opens up a new field in human computer interaction. With computers able to correctly interpret almost all interactions without direct contact with keyboard or mouse, a major data source has opened up for Data Scientists to explore.
2. A system which is 80% accurate may not usable, however, when accuracy crosses 95%, there is a major turnaround in large scale adoption.
3. Self driving cars will lead to major leaps in technologies for object recognition -> not just previously known objects, also to anticipate and correctly handle unexpected objects.
4. In my view, there are four key dimensions of Data science, these are Data, Domain Expertise, Machine learning algorithms and Technology of Deployment. Value creation is possible across all the dimensions of Data Science. Better quality data, higher volume of relevant and contextual data can create value, and domain expertise remains critical in making successful deployments of data science projects. Our focus on machine learning algorithms is important, however, value creation happens across all the four dimensions.
5. We have seen a 5X increase in jobs which require machine learning and neural networks expertise.
Data Science is now mainstream and it is important for every organization to invest in Data Science and benefit from it.
https://www.slideshare.net/vjain99/data-science-conclave-keynote-presentation
1. Major improvements in accuracy in speech recognition and image recognition opens up a new field in human computer interaction. With computers able to correctly interpret almost all interactions without direct contact with keyboard or mouse, a major data source has opened up for Data Scientists to explore.
2. A system which is 80% accurate may not usable, however, when accuracy crosses 95%, there is a major turnaround in large scale adoption.
3. Self driving cars will lead to major leaps in technologies for object recognition -> not just previously known objects, also to anticipate and correctly handle unexpected objects.
4. In my view, there are four key dimensions of Data science, these are Data, Domain Expertise, Machine learning algorithms and Technology of Deployment. Value creation is possible across all the dimensions of Data Science. Better quality data, higher volume of relevant and contextual data can create value, and domain expertise remains critical in making successful deployments of data science projects. Our focus on machine learning algorithms is important, however, value creation happens across all the four dimensions.
5. We have seen a 5X increase in jobs which require machine learning and neural networks expertise.
Data Science is now mainstream and it is important for every organization to invest in Data Science and benefit from it.
https://www.slideshare.net/vjain99/data-science-conclave-keynote-presentation
