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  • Tableau Data Visualization
  • PySpark Programming
  • Data Analysis
  • Data Transformation and Manipulation
  • Big Data Machine Learning
  • Geo Mapping with Tableau
  • Geospatial Machine Learning
  • Creating Dashboards

Welcome to the Big Data Analytics  with PySpark + Tableau Desktop + MongoDB course. In this course we will be creating a big data analytics solution using big data technologies like PySpark for ETL,  MLlib for Machine Learning as well as Tableau for Data Visualization and for building Dashboards.

We will be working with earthquake data, that we will transform into summary tables. We will then use these tables to train predictive models and predict future earthquakes. We will then analyze the data by building reports and dashboards in Tableau Desktop.

Tableau Desktop is a powerful data visualization tool used for big data analysis and visualization. It allows for data blending, real-time analysis and collaboration of data. No programming is needed for Tableau Desktop, which makes it a very easy and powerful tool to create dashboards apps and reports.

MongoDB is a document-oriented NoSQL database, used for high volume data storage. It stores data in JSON like format called documents, and does not use row/column tables. The document model maps to the objects in your application code, making the data easy to work with.

 

  • You will learn how to create data processing pipelines using PySpark

  • You will learn machine learning with geospatial data using the Spark MLlib library

  • You will learn data analysis using PySpark, MongoDB and Tableau

  • You will learn how to manipulate, clean and transform data using PySpark dataframes

  • You will learn how to create Geo Maps in Tableau Desktop

  • You will also learn how to create dashboards in Tableau Desktop

  • Basic Understanding of Python
  • Little or no understanding of GIS
  • Basic understanding of Programming concepts
  • Basic understanding of Data
  • Basic understanding of what Machine Learning is
  • Python Developers at any level
  • Data Engineers at any level
  • Developers at any level
  • Machine Learning engineers at any level
  • Data Scientists at any level
  • GIS Developers at any level
  • The curious mind
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  • Section 1 : Introduction 1 Lectures

    • Lecture 1 :
  • Section 2 : Setup and Installations 6 Lectures

    • Lecture 1 :
    • Python Installation
    • Lecture 2 :
    • Installing Apache Spark
    • Lecture 3 :
    • Installing Java (Optional)
    • Lecture 4 :
    • Testing Apache Spark Installation
    • Lecture 5 :
    • Installing MongoDB
    • Lecture 6 :
    • Installing NoSQL Booster for MongoDB
  • Section 3 : Data Processing with PySpark and MongoDB 4 Lectures 00:00:00

    • Lecture 1 :
    • Integrating PySpark with Jupyter Notebook
    • Lecture 2 :
    • Data Extraction
    • Lecture 3 :
    • Data Transformation
    • Lecture 4 :
    • Loading Data into MongoDB
  • Section 4 : Machine Learning with PySpark and MongoDB 3 Lectures

    • Lecture 1 :
    • Data Pre-processing
    • Lecture 2 :
    • Building the Predictive Model
    • Lecture 3 :
    • Creating the Prediction Dataset
  • Section 5 : Building the Data Pipeline Scripts 3 Lectures

    • Lecture 1 :
    • Installing Visual Studio Code
    • Lecture 2 :
    • Creating the PySpark ETL Script
    • Lecture 3 :
    • Creating the Machine Learning Script
  • Section 6 : Tableau Data Visualization 9 Lectures

    • Lecture 1 :
    • Installing Tableau
    • Lecture 2 :
    • Installing MongoDB ODBC Drivers
    • Lecture 3 :
    • Creating a System DSN for MongoDB
    • Lecture 4 :
    • Loading the Data Sources
    • Lecture 5 :
    • Creating a Geo Map
    • Lecture 6 :
    • Creating a Bar Chart
    • Lecture 7 :
    • Creating a Magnitude Chart
    • Lecture 8 :
    • Creating a Table Plot
    • Lecture 9 :
    • Creating a Dashboard
  • Section 7 : Source Code and Notebook 1 Lectures

    • Lecture 1 :
    • Source Code and Notebook
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