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1
Intro
2
Goal of data science
3
Team Data Science Process
4
Business Understanding
5
The iterative process
6
Define a target
7
Data preparation
8
Supervised models
9
Feature engineering
10
Model tuning
11
Evaluation
12
Take a step back
13
The model
14
Deploy
15
Questions
16
Real business examples
17
Business example cases
18
Core competencies of a data scientist
19
How does TDSP improve
Description:
Explore the Team Data Science Process (TDSP), an agile and iterative methodology for delivering predictive analytics solutions and intelligent applications efficiently, in this 21-minute conference talk. Learn how to digitalize smaller, specific manual processes by infusing them with AI, making them reusable and objective. Discover the key steps of TDSP, including business understanding, data preparation, supervised models, feature engineering, model tuning, and evaluation. Understand how to define targets, take a step back to assess progress, and deploy models effectively. Gain insights into real business examples and cases, core competencies of a data scientist, and how TDSP improves the overall data science process for organizations seeking to enhance efficiency and insights in their core business processes.

Data Science for Business - Repeatable and Objective Data Science Process

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