Get the course Learn How Uber Does Decision-Based Machine Learning – Coloso free download links through Mega.nz and Google Drive.
- Total 32 videos Get exclusive tips and insights from Data Scientist, Jongbin Jung throughout 32 online classes.
- Intermediate Level Requires a basic understanding of Google Colab.
When you can perform data analysis and link it to actual business decision-making, you can be the best data scientist of the bunch.
Do you know how the Frequentist and Bayesian statistics are different exactly? How are predictive modeling and causality analysis linked to each other? Do you know what questions we ask first for data analysis centered on decision making before talking about data or machine learning?
Jongbin Jung, who used to work as a data scientist and made pricing decisions at Uber Eats tells you the answers to these questions in this class.
The top management in a company needs a data analyst who can create insights that back up optimal decision making. Learn about Bayesian statistics and inferences used in practical work and the secrets including the optimal techniques that help companies make decisions here at Coloso.
Do you really know which statistical technique to use in what situation?
I’ll help you intuitively understand statistics a data scientist must know without using any Greek characters.
There are a very few people who deeply worry about the problem of empirically applying and understanding data analysis from a viewpoint of helping human decision making. Learn from that person and become that person.
Based on statistics that a data scientist must know, you must learn about the technique of decision making on real-world cases with high reliability in order to become a highly-paid data scientist.
Focus on what’s helpful in practical work that’s centered in “decision making”. I’ll introduce to you in detail the confidence intervals, Bayesian, Frequentist, the significance of the model and the fundamentals of causality analysis, which many have heard of but cannot exactly define.
Show problems through intuitive examples. Learn and practice in theory or through Python code examples the statistics a data scientist must know without any Greek characters.
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