One of the most important skills for data scientists to have, is being able to clearly communicate results so different stakeholders and decision makers can understand. Since data projects are collaborative across functions and data science results are often incorporated into a larger final project, which will require approvals for costs and resources and support from these decision makers; helping them understand the value of the data science work will make it easier for everyone working on these projects. Deep Learning is one of the topics that may not be easy to explain to stakeholders who don’t have a mathematical or statistical background… in this session, we will explore the theories and methods that will help us understand our audience and their learning style and successfully interpret our work into business language to emphasize the true impact of a data scientists’ work which heavily relies on how well others can understand it to take further actions.
Top rated books
- AI 2041: Ten Visions for Our Future
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
- The Data Detective: Ten Easy Rules to Make Sense of Statistics
- Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies
- The Art of Statistics: How to Learn from Data