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Practical Data Science via Coursera Specialization


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. 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. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.


Course 1: Analyze Datasets and Train ML Models using AutoML
- In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), ... Enroll for free.

Course 2: Build, Train, and Deploy ML Pipelines using BERT
- In the second course of the Practical Data Science Specialization, you will learn to automate a natural language processing task by building ... Enroll for free.

Course 3: Optimize ML Models and Deploy Human-in-the-Loop Pipelines
- In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction ... Enroll for free.


Taught by

Antje Barth, Chris Fregly, Shelbee Eigenbrode and Sireesha Muppala

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  • Implementing, building and deploying a machine learning pipeline is complex and involves many time-consuming tasks. Amazon AWS with SageMaker and related tools allow you to automate a lot of the parts making you more efficient as a data scientist. All intermediate artefacts in the pipeline are available for manual tailoring via the AWS management console, via the CLI or the SDK. This gives you plenty of choices of which steps you want to fully automate by default, include your own scripts, or do manual interventions.

    Data scientist resources are scarce, use them optimally and automate where possible the tedious tasks, so you can make faster progress, save money, and focus on the interesting parts of the business.

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