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  1. Home
  2. Learning Paths

Practical Data Science Learning Path

Practical Data Science is a Specialization designed by Coursera. It consists of three courses and can be completed in three months with 5 hours of learning content .

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Description

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

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Free Learning Paths by Career Track

Bootstrap Data ScientistBootstrap Data AnalystBootstrap Business AnalystBootstrap Data EngineerBootstrap Chief Data OfficerBootstrap Data Architect

Included Courses

Coursera
DeepLearning.AI
Free
1. Analyze Datasets and Train ML Models using AutoML

19 hours

Advanced

15,075
Algorithms AutoML Classification Data Analysis Data Modeling
Coursera
DeepLearning.AI
Free
2. Build, Train, and Deploy ML Pipelines using BERT

14 hours

Advanced

6,743
Algorithms Automation Classification Data Modeling Data Science
Coursera
DeepLearning.AI
3. Optimize ML Models and Deploy Human-in-the-Loop Pipelines

14 hours

Advanced

3,753
AB Testing Algorithms Artificial Intelligence Data Modeling Data Science
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