Explore the fundamentals of data science in this comprehensive conference talk from the 2016 Microsoft Data Science Summit in Atlanta, GA. Delve into key topics such as data acquisition, formulating sharp questions, identifying target data, and understanding data quality. Learn about feature engineering, machine learning examples, and how to determine if you have sufficient data for your analysis. Discover best practices for utilizing insights, addressing common gaps in data science processes, and effectively communicating results. Gain valuable knowledge on changing mindsets, learning new domains, and asking the right questions to enhance your data science skills and decision-making abilities.
Overview
Syllabus
Intro
Getting Data
Sharp Questions
Target Data
Table Data
Data Quality
Feature Engineering
Machine Learning Example
Do I have enough data
Use the answer
The three gaps
Conclusion
Questions
Closing the Gap
Best Practices
Changing Minds
Learning Domain
Question
Taught by
Brandon Rohrer
