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Visualizing Data with Python Libraries and Amazon SageMaker

Imagine you are the data engineer, and you have been assigned to produce visualization charts and answer the following business questions using the dataset provided. Your data contains both categorical and numerical data. 1. Distribution of all employee ages. 2. Distribution of employee ages in each department. 3. Compare the salary ranges of employees by department. 4. Relationship between employee age and salary. 5. Distribution of employee salary across all departments. In this hands-on lab, you will create charts using Seaborn and matplotlib Python libraries.

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Path Info

Level
Clock icon Intermediate
Duration
Clock icon 45m
Published
Clock icon Apr 25, 2024

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Table of Contents

  1. Challenge

    Launch SageMaker Notebook

    Log in to the AWS console and navigate to AWS SageMaker. Load the Jupyter Notebook that has been provided with this hands-on lab.

  2. Challenge

    Load Libraries and Prepare the Data

    1. Use the Pandas library and load Employee.csv.
    2. Display the top few rows and ensure the data is read successfully.

  3. Challenge

    Perform Univariate Analysis

    1. Use matplotlib functions to add chart title and labels to x and y axes.
    2. Use the Seaborn Library and create a visualization showing the distribution of employee age in each department.

  4. Challenge

    Perform Bivariate Analysis

    1. Modify the Seaborn library to present the salary range of employees by department, show the descriptive statistics visually, and present the relationship between age and salary.
A Cloud Guru - Labs - Visualizing Data with Python Libraries and Amazon SageMaker
Author
A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

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