- Lab
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Libraries: If you want this lab, consider one of these libraries.
- AI
- Cloud

Introducing Jupyter Notebooks (Amazon SageMaker)
Jupyter Notebooks are a powerful tool used most prominently in many data science and machine learning projects to allow an individual or group to collaboratively build, document, and visualize their code in an interactive environment. This open source solution can be self-hosted, and is also available as a managed solution on most major cloud platforms. In this lab, we're going to use a Jupyter Notebook with Amazon SageMaker to use an existing notebook, execute existing code written by others, and also write and execute our own code. Basic knowledge of Python will be helpful, but not required. The files used in this lab can be found on our [GitHub](https://github.com/pluralsight-cloud/AWS-Certified-Machine-Learning-Specialty-Labs/tree/main/Introducing-Jupyter-Notebooks-Amazon-SageMaker).

Lab Info
Table of Contents
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Challenge
Open the existing Jupyter Notebook
Navigate to open the existing Amazon SageMaker Notebook Instance, and launch the provided Jupyter Notebook file.
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Challenge
Execute the Demonstration code
Before we get into our scenario, we will run through a basic demonstration of how Jupyter Notebooks work, and how they might be useful to our purposes.
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Challenge
Write a new section to the code
Add a new section to our Jupyter Notebook to analyze the results from each question, and identify if there are any outlier questions where students performed worse than the baseline.
Code is available in the Lab Guide.
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Challenge
Perform basic data analytics
Import the necessary dataset of our quiz results using
pandas
, and run the basic analysis to confirm both how many questions are in our dataset, and how many attempts have been made for each question.
About the author
Real skill practice before real-world application
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