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1
Intro
2
Getting Data
3
Sharp Questions
4
Target Data
5
Table Data
6
Data Quality
7
Feature Engineering
8
Machine Learning Example
9
Do I have enough data
10
Use the answer
11
The three gaps
12
Conclusion
13
Questions
14
Closing the Gap
15
Best Practices
16
Changing Minds
17
Learning Domain
18
Question
Description:
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.

How Data Science Works

Brandon Rohrer
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