/Hands-On-Data-Analytics-for-Beginners-with-Google-Colaboratory-Video-

Hands-On Data Analytics for Beginners with Google Colaboratory [Video], published by Packt

Primary LanguageJupyter NotebookMIT LicenseMIT

Hands-On Data Analytics for Beginners with Google Colaboratory [Video]

This is the code repository for Hands-On Data Analytics for Beginners with Google Colaboratory [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Google Colaboratory is an online platform to perform data analysis. It enables you to create interactive Jupyter notebooks that mix text with Python code to run queries and display data analysis results. Stored on Google Drive you'll be able to run notebooks and collaborate with peers through Google's cloud services.

In this course, you will learn to solve problems and obtain key results with data. You will begin by building your own Jupyter notebook before you explore and learn the basics of Google Colaboratory. Then you will explore several file formats to store data and use SQLite to query large datasets. Next, you will learn to initialize 1D and 2D data structures with the Numpy and Pandas libraries to help organize and summarize metrics such as the mean, median, and standard deviation of your data.

Moving further, you will learn to identify outliers in your data, eliminate dirty data and perform common data transformations. Finally, you will use qualitative and quantitative data types with Matplotlib to display effective charts and visuals. By the end of this course, you'll have the tools to perform data analysis to tell your own compelling stories with data.

What You Will Learn

  • Create, run and style your own Jupyter Notebook online
  • Load common file types or query databases with SQLite
  • Grouping and reorganizing useful Data
  • Exploring 1D and 2D data structures with Numpy and Pandas
  • Calculate descriptive stats such as mean, median, mode
  • Clean and transform missing or dirty data
  • Plot stunning visuals with bar charts, scatter plots, and pie charts with Matplotlib
  • Share your rich-interactive notebooks findings to get immediate feedback
  • Present and summarize visually compelling stories key individuals

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:

  • Python Programming (Version 3)
  • Intermediate Algebra
  • Descriptive Statistics

    Technical Requirements

    This course has the following software requirements:

  • Python Anaconda Package (will download in the course)
  • 64-bit computer.
  • Minimum 3 GB disk space to download and install Python Anaconda package
  • Windows, macOS or Linux.
  • Python​ ​3.5 or above

    Browser System Requirements :

  • Chrome
  • Firefox
  • Windows only: Internet Explorer 11, Microsoft Edge
  • Mac only: Safari

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