Practical-Data-Science-Specialization

The Practical Data Science on the AWS Cloud Specialization helps develop the practical skills to effectively deploy data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

Course 1: Analyze Datasets and Train ML Models using AutoML

This is the first course of the Practical Data Science on the AWS Cloud Specialization .

Week 1: Explore the Use Case and Analyze the Dataset

Week 2: Data Bias and Feature Importance

Week 3: Use Automated Machine Learning to train a Text Classifier

Week 4: Built-in algorithms

Course 2: Build, Train and Deploy ML Pipelines using BERT

This is the second course of the Practical Data Science on the AWS Cloud Specialization .

Week 1: Feature Engineering and Feature Store

Week 2: Train, Debug, and Profile a Machine Learning Model

Week 3: Deploy End-To-End Machine Learning pipelines

Course 3: Optimize ML Models and Deploy Human-in-the-Loop-Pipelines

This is the third course of the Practical Data Science on the AWS Cloud Specialization .

Week 1: Advanced model training, tuning and evaluation

Week 2: Advanced model deployment and monitoring

Week 3: Data labeling and human-in-the-loop pipelines